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Deck 10: Multiple Regression
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Question
Use the following to answer the questions below:
The ANOVA table from a multiple regression analysis is provided.
-How many predictors are in the model?
The ANOVA table from a multiple regression analysis is provided.
-How many predictors are in the model?
Question
Use the following to answer the questions below:
The ANOVA table from a multiple regression analysis is provided.
-How large is the sample size?
The ANOVA table from a multiple regression analysis is provided.
-How large is the sample size?
Question
Compute 
for this model. Round to three decimal places.
A) 0.333
B) 0.667
C) 0.501
D) 0.083
for this model. Round to three decimal places.A) 0.333
B) 0.667
C) 0.501
D) 0.083
Question
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-The "Caramel Macchiato" was one of the drinks selected for the sample. When made with 2% milk, a grande Caramel Macchiato has 7 grams of fat, 34 grams of carbohydrates, and 10 grams of protein. Predict the number of calories in a Caramel Macchiato. Round to two decimal places.
A) 234.79 calories
B) 235.00 calories
C) 347.79 calories
D) 241.60 calories
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-The "Caramel Macchiato" was one of the drinks selected for the sample. When made with 2% milk, a grande Caramel Macchiato has 7 grams of fat, 34 grams of carbohydrates, and 10 grams of protein. Predict the number of calories in a Caramel Macchiato. Round to two decimal places.
A) 234.79 calories
B) 235.00 calories
C) 347.79 calories
D) 241.60 calories
Question
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret the coefficient of Fat in context.
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret the coefficient of Fat in context.
Question
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-How many drinks were used in this sample?
A) 12
B) 11
C) 10
D) 9
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-How many drinks were used in this sample?
A) 12
B) 11
C) 10
D) 9
Question
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret
for this model.
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret
for this model. Question
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Is the model effective according to the ANOVA test? Use a 5% significance level. Include all details of the test.
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Is the model effective according to the ANOVA test? Use a 5% significance level. Include all details of the test.
Question
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Which predictors are significant at the 5% level?
A) Fat and Carbs
B) Fat
C) Carbs
D) Fat, Carbs, and Protein
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Which predictors are significant at the 5% level?
A) Fat and Carbs
B) Fat
C) Carbs
D) Fat, Carbs, and Protein
Question
A dotplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
Question
Which of the following scatterplots of the residuals versus the predicted values does not indicate problems with either the linearity or the consistent variability conditions?
A)
B)
C)
A)
B)
C)
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Predict the GPA of a student at this university with a Math SAT score of 600, a Verbal SAT score of 580, and who watches 5 hours of television in a typical week. Round to three decimal places.
A) 3.174
B) 3.233
C) 3.248
D) 3.142
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Predict the GPA of a student at this university with a Math SAT score of 600, a Verbal SAT score of 580, and who watches 5 hours of television in a typical week. Round to three decimal places.
A) 3.174
B) 3.233
C) 3.248
D) 3.142
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Interpret the coefficient of TV in context.
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Interpret the coefficient of TV in context.
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-The
for this model is missing in the provided output. Use the available information to compute (round to three decimal places) 
for this model.
A) 0.195
B) 0.243
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-The
for this model is missing in the provided output. Use the available information to compute (round to three decimal places) for this model.A) 0.195
B) 0.243
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Use the output to determine how many students were included in the sample.
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Use the output to determine how many students were included in the sample.
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should appear in the "Regression"
row of the table?
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should appear in the "Regression"
row of the table?
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should be listed in the "Residual Error"
row?
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should be listed in the "Residual Error"
row?
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-At the 5% significance level, is the model effective according to the ANOVA test. Include all details of the test.
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-At the 5% significance level, is the model effective according to the ANOVA test. Include all details of the test.
Question
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Which predictors are significant at the 5% level?
A) Math SAT, Verbal SAT, and TV
B) Verbal SAT, and TV
C) Math SAT, Verbal SAT
D) Math SAT, and TV
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Which predictors are significant at the 5% level?
A) Math SAT, Verbal SAT, and TV
B) Verbal SAT, and TV
C) Math SAT, Verbal SAT
D) Math SAT, and TV
Question
A dotplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-What are the explanatory variables used in this model?
A) Total Fat (g), Cholesterol (mg), and Sodium (mg)
B) Total Fat (g), Cholesterol (mg), Sodium (mg), and Calories
C) Total Fat (g) and Calories
D) Cholesterol (mg), Sodium (mg), and Calories
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-What are the explanatory variables used in this model?
A) Total Fat (g), Cholesterol (mg), and Sodium (mg)
B) Total Fat (g), Cholesterol (mg), Sodium (mg), and Calories
C) Total Fat (g) and Calories
D) Cholesterol (mg), Sodium (mg), and Calories
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Use the provided output to determine how many menu items were included in the sample.
A) 12
B) 13
C) 14
D) 15
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Use the provided output to determine how many menu items were included in the sample.
A) 12
B) 13
C) 14
D) 15
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the predicted response for the McDouble? Round your answer to two decimal places.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the predicted response for the McDouble? Round your answer to two decimal places.
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the residual for the McDouble? Round your answer to two decimal places.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the residual for the McDouble? Round your answer to two decimal places.
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictor appears to be the most important in this model? Explain briefly.
A) Total fat (g)
B) Cholesterol (mg)
C) Sodium (mg)
D) Calories
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictor appears to be the most important in this model? Explain briefly.
A) Total fat (g)
B) Cholesterol (mg)
C) Sodium (mg)
D) Calories
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret the coefficient of Sodium in context.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret the coefficient of Sodium in context.
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret
for this model.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret
for this model. Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-At the 5% significance level, is the model effective according to the ANOVA test? Include all details of the test.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-At the 5% significance level, is the model effective according to the ANOVA test? Include all details of the test.
Question
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictors are significant at the 5% level? What are their p-values?
A) Total fat and sodium
B) Total fat, cholesterol, and sodium
C) Total fat
D) Cholesterol, and sodium
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictors are significant at the 5% level? What are their p-values?
A) Total fat and sodium
B) Total fat, cholesterol, and sodium
C) Total fat
D) Cholesterol, and sodium
Question
A boxplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
Question
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-What is the explanatory variable used in the single predictor model?
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-What is the explanatory variable used in the single predictor model?
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of this car using the single predictor model? Round to three decimal places.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of this car using the single predictor model? Round to three decimal places.
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of the car using the two predictor model? Round to three decimal places.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of the car using the two predictor model? Round to three decimal places.
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant single predictor of the price of used Hyundai Elantras? Use α = 0.05. Include all details of your test.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant single predictor of the price of used Hyundai Elantras? Use α = 0.05. Include all details of your test.
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Explain why Age is a potential confounding variable in the relationship between Age and Price of used Hyundai Elantras.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Explain why Age is a potential confounding variable in the relationship between Age and Price of used Hyundai Elantras.
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is the two predictor model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is the two predictor model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant predictor of the price of used Hyundai Elantras, even after accounting for age?
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant predictor of the price of used Hyundai Elantras, even after accounting for age?
Question
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Use the provided output to determine how many cars were in the sample.
A) 22
B) 23
C) 24
D) 25
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Use the provided output to determine how many cars were in the sample.
A) 22
B) 23
C) 24
D) 25
Question
A boxplot of the residuals and a scatterplot of the residuals versus the predicted values from the two predictor model are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
Question
Regression output for the model that only uses Age as a predictor in the model is provided. Assuming that the residuals for this single predictor model do not indicate any problems, is this model an improvement over the model that uses both Age and Mileage as predictors? Statistically justify your answer by discussing at least two quantitative criteria.
The regression equation is Price = 15.3 - 1.71 Age
S = 1.37179 R-Sq = 88.9% R-Sq(adj) = 88.4%
Analysis of Variance
The regression equation is Price = 15.3 - 1.71 Age
S = 1.37179 R-Sq = 88.9% R-Sq(adj) = 88.4%
Analysis of Variance
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 92-year-old, 1,742 square foot house in Canton. What is the predicted selling price of this house? Round to three decimal places.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 92-year-old, 1,742 square foot house in Canton. What is the predicted selling price of this house? Round to three decimal places.
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 62-year-old, 1,865 square foot house in Potsdam. What is the predicted selling price of this house? Round to three decimal places.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 62-year-old, 1,865 square foot house in Potsdam. What is the predicted selling price of this house? Round to three decimal places.
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Age in context.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Age in context.
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Town in context.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Town in context.
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-How many houses are used in this dataset?
A) 48
B) 47
C) 46
D) 45
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-How many houses are used in this dataset?
A) 48
B) 47
C) 46
D) 45
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret
for this model.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret
for this model. Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Using α = 0.05, is the model effective according to the ANOVA test? Include all details of the test.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Using α = 0.05, is the model effective according to the ANOVA test? Include all details of the test.
Question
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-Which predictors are significant at the 5% level?
A) Size and Age
B) Size
C) Age
D) Size, Age, and Town
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-Which predictors are significant at the 5% level?
A) Size and Age
B) Size
C) Age
D) Size, Age, and Town
Question
A dotplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
Question
Regression output for a model that omits Town as a predictor is provided. Assuming that the residuals for this reduced model do not indicate any problems with using multiple regression, is this model an improvement over the model that uses Size, Age, and Town as predictors? Statistically justify your answer by discussing at least two quantitative criteria.
The regression equation is
Price (in thousands) = 70.6 + 0.0624 Size (sq. ft.) - 0.635 Age
S = 39.6368 R-Sq = 59.3% R-Sq(adj) = 57.5%
Analysis of Variance
The regression equation is
Price (in thousands) = 70.6 + 0.0624 Size (sq. ft.) - 0.635 Age
S = 39.6368 R-Sq = 59.3% R-Sq(adj) = 57.5%
Analysis of Variance
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Monday with a high temperature of 62°F. Use one decimal place in your answer.
A) 116.4 kilowatt hours
B) 91.2 kilowatt hours
C) 32.8 kilowatt hours
D) 141.6 kilowatt hours
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Monday with a high temperature of 62°F. Use one decimal place in your answer.
A) 116.4 kilowatt hours
B) 91.2 kilowatt hours
C) 32.8 kilowatt hours
D) 141.6 kilowatt hours
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Saturday with a high temperature of 68°F. Use one decimal place in your answer.
A) 94.4 kilowatt hours
B) 119.6 kilowatt hours
C) 58.9 kilowatt hours
D) 92.4 kilowatt hours
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Saturday with a high temperature of 68°F. Use one decimal place in your answer.
A) 94.4 kilowatt hours
B) 119.6 kilowatt hours
C) 58.9 kilowatt hours
D) 92.4 kilowatt hours
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of High Temp in context.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of High Temp in context.
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of Weekend in context.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of Weekend in context.
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-How many days are included in the sample?
A) 365
B) 311
C) 312
D) 313
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-How many days are included in the sample?
A) 365
B) 311
C) 312
D) 313
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret
for this model.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret
for this model. Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Which predictors are significant at the 5% level? What are their p-values?
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Which predictors are significant at the 5% level? What are their p-values?
Question
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Another possible predictor they recorded was the average temperature over the course of each day. Regression output for the model that uses High Temp, Weekend, and Avg. Temp is provided. Explain why these results differ so drastically from those for the two-predictor model.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Another possible predictor they recorded was the average temperature over the course of each day. Regression output for the model that uses High Temp, Weekend, and Avg. Temp is provided. Explain why these results differ so drastically from those for the two-predictor model.
Question
A histogram of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
Question
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in a home game? Round to one decimal place.
A) 93.2 points
B) 110.8 points
C) 94.0 points
D) 111.8 points
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in a home game? Round to one decimal place.
A) 93.2 points
B) 110.8 points
C) 94.0 points
D) 111.8 points
Question
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in an away game? Round to one decimal place.
A) 102.0 points
B) 101.0 points
C) 93.2 points
D) 110.8 points
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in an away game? Round to one decimal place.
A) 102.0 points
B) 101.0 points
C) 93.2 points
D) 110.8 points
Question
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Interpret the
for this model.
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Interpret the
for this model. Question
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Using α = 0.05, is there a difference in the number of points scored for home and away games? Include all details of the test.
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Using α = 0.05, is there a difference in the number of points scored for home and away games? Include all details of the test.
Question
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Hyundai Elantra? Round to three decimal places.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Hyundai Elantra? Round to three decimal places.
Question
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Toyota Camry? Round to three decimal places.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Toyota Camry? Round to three decimal places.
Question
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret the coefficient of Model in context.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret the coefficient of Model in context.
Question
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret
for this model.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret
for this model. Question
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Question
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Which predictors are significant at the 5% level? What are their p-values?
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Which predictors are significant at the 5% level? What are their p-values?
Question
A histogram of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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Deck 10: Multiple Regression
1
Use the following to answer the questions below:
The ANOVA table from a multiple regression analysis is provided.
-How many predictors are in the model?
The ANOVA table from a multiple regression analysis is provided.
-How many predictors are in the model?
4
2
Use the following to answer the questions below:
The ANOVA table from a multiple regression analysis is provided.
-How large is the sample size?
The ANOVA table from a multiple regression analysis is provided.
-How large is the sample size?
35
3
Compute for this model. Round to three decimal places.
A) 0.333
B) 0.667
C) 0.501
D) 0.083
for this model. Round to three decimal places.A) 0.333
B) 0.667
C) 0.501
D) 0.083
0.333
4
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-The "Caramel Macchiato" was one of the drinks selected for the sample. When made with 2% milk, a grande Caramel Macchiato has 7 grams of fat, 34 grams of carbohydrates, and 10 grams of protein. Predict the number of calories in a Caramel Macchiato. Round to two decimal places.
A) 234.79 calories
B) 235.00 calories
C) 347.79 calories
D) 241.60 calories
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-The "Caramel Macchiato" was one of the drinks selected for the sample. When made with 2% milk, a grande Caramel Macchiato has 7 grams of fat, 34 grams of carbohydrates, and 10 grams of protein. Predict the number of calories in a Caramel Macchiato. Round to two decimal places.
A) 234.79 calories
B) 235.00 calories
C) 347.79 calories
D) 241.60 calories
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5
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret the coefficient of Fat in context.
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret the coefficient of Fat in context.
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6
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-How many drinks were used in this sample?
A) 12
B) 11
C) 10
D) 9
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-How many drinks were used in this sample?
A) 12
B) 11
C) 10
D) 9
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7
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret for this model.
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Interpret
for this model. Unlock Deck
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8
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Is the model effective according to the ANOVA test? Use a 5% significance level. Include all details of the test.
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Is the model effective according to the ANOVA test? Use a 5% significance level. Include all details of the test.
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9
Use the following to answer the questions below:
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Which predictors are significant at the 5% level?
A) Fat and Carbs
B) Fat
C) Carbs
D) Fat, Carbs, and Protein
While many people count calories, some often don't think about calories in the beverages they consume. Starbucks, one of the leading coffeehouse chains, provides nutrition information about all of their beverages on their website. Nutrition information, including number of calories, fat (g), carbohydrates (g), and protein (g), was collected on a random sample of Starbucks' 16 ounce ("Grande") hot espresso drinks. Note that all of the drinks in the sample are made with 2% milk unless the name specifically included the term "Skinny," which is how Starbucks indicated a beverage made with nonfat milk.
The regression equation is
Calories = 6.7 + 9.61 Fat (g) + 3.43 Carbs (g) + 4.42 Protein (g)
-Which predictors are significant at the 5% level?
A) Fat and Carbs
B) Fat
C) Carbs
D) Fat, Carbs, and Protein
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10
A dotplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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11
Which of the following scatterplots of the residuals versus the predicted values does not indicate problems with either the linearity or the consistent variability conditions?
A)
B)
C)
A)
B)
C)
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12
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Predict the GPA of a student at this university with a Math SAT score of 600, a Verbal SAT score of 580, and who watches 5 hours of television in a typical week. Round to three decimal places.
A) 3.174
B) 3.233
C) 3.248
D) 3.142
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Predict the GPA of a student at this university with a Math SAT score of 600, a Verbal SAT score of 580, and who watches 5 hours of television in a typical week. Round to three decimal places.
A) 3.174
B) 3.233
C) 3.248
D) 3.142
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13
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Interpret the coefficient of TV in context.
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Interpret the coefficient of TV in context.
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14
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-The for this model is missing in the provided output. Use the available information to compute (round to three decimal places) for this model.
A) 0.195
B) 0.243
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-The
for this model is missing in the provided output. Use the available information to compute (round to three decimal places) for this model.A) 0.195
B) 0.243
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15
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Use the output to determine how many students were included in the sample.
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Use the output to determine how many students were included in the sample.
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16
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should appear in the "Regression"
row of the table?
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should appear in the "Regression"
row of the table?
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17
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should be listed in the "Residual Error"
row?
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Some of the information in the ANOVA table is missing. How many degrees of freedom should be listed in the "Residual Error"
row?
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18
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-At the 5% significance level, is the model effective according to the ANOVA test. Include all details of the test.
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-At the 5% significance level, is the model effective according to the ANOVA test. Include all details of the test.
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19
Use the following to answer the questions below:
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Which predictors are significant at the 5% level?
A) Math SAT, Verbal SAT, and TV
B) Verbal SAT, and TV
C) Math SAT, Verbal SAT
D) Math SAT, and TV
Output for a model to predict the GPAs of students at a small university based on their Math SAT scores, Verbal SAT scores, and the number of hours spent watching television in a typical week is provided.
The regression equation is
GPA = 1.80 + 0.00104 Math SAT + 0.00142 Verbal SAT - 0.0147 TV
-Which predictors are significant at the 5% level?
A) Math SAT, Verbal SAT, and TV
B) Verbal SAT, and TV
C) Math SAT, Verbal SAT
D) Math SAT, and TV
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20
A dotplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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21
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-What are the explanatory variables used in this model?
A) Total Fat (g), Cholesterol (mg), and Sodium (mg)
B) Total Fat (g), Cholesterol (mg), Sodium (mg), and Calories
C) Total Fat (g) and Calories
D) Cholesterol (mg), Sodium (mg), and Calories
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-What are the explanatory variables used in this model?
A) Total Fat (g), Cholesterol (mg), and Sodium (mg)
B) Total Fat (g), Cholesterol (mg), Sodium (mg), and Calories
C) Total Fat (g) and Calories
D) Cholesterol (mg), Sodium (mg), and Calories
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22
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Use the provided output to determine how many menu items were included in the sample.
A) 12
B) 13
C) 14
D) 15
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Use the provided output to determine how many menu items were included in the sample.
A) 12
B) 13
C) 14
D) 15
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Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the predicted response for the McDouble? Round your answer to two decimal places.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the predicted response for the McDouble? Round your answer to two decimal places.
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Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the residual for the McDouble? Round your answer to two decimal places.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-One of the menu items in the sample is the "McDouble,"
which has 390 calories, 12 grams of fat, 65 mg of cholesterol, and 850 mg of sodium. What is the residual for the McDouble? Round your answer to two decimal places.
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25
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictor appears to be the most important in this model? Explain briefly.
A) Total fat (g)
B) Cholesterol (mg)
C) Sodium (mg)
D) Calories
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictor appears to be the most important in this model? Explain briefly.
A) Total fat (g)
B) Cholesterol (mg)
C) Sodium (mg)
D) Calories
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Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret the coefficient of Sodium in context.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret the coefficient of Sodium in context.
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27
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret for this model.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Interpret
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28
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-At the 5% significance level, is the model effective according to the ANOVA test? Include all details of the test.
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-At the 5% significance level, is the model effective according to the ANOVA test? Include all details of the test.
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29
Use the following to answer the questions below:
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictors are significant at the 5% level? What are their p-values?
A) Total fat and sodium
B) Total fat, cholesterol, and sodium
C) Total fat
D) Cholesterol, and sodium
Fast food restaurants are required to publish nutrition information about the foods they serve. Nutrition information for a random sample of McDonald's lunch/dinner menu items (excluding sides and drinks) was obtained from their website. Output from a multiple regression analysis is provided.
The regression equation is Calories = 65.2 + 9.46 Total Fat (g) + 0.876 Cholesterol (mg) + 0.131 Sodium (mg)
-Which predictors are significant at the 5% level? What are their p-values?
A) Total fat and sodium
B) Total fat, cholesterol, and sodium
C) Total fat
D) Cholesterol, and sodium
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30
A boxplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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31
Which variable, if any, would you suggest trying to eliminate first to possibly improve this model? Describe one way in which you might determine if the model had been improved by removing that variable. Explain briefly.
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32
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-What is the explanatory variable used in the single predictor model?
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-What is the explanatory variable used in the single predictor model?
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33
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of this car using the single predictor model? Round to three decimal places.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of this car using the single predictor model? Round to three decimal places.
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34
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of the car using the two predictor model? Round to three decimal places.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-One of the cars in the sample was a 5-year-old Hyundai Elantra with 87,100 miles being sold for $6,000. What is the predicted price of the car using the two predictor model? Round to three decimal places.
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35
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant single predictor of the price of used Hyundai Elantras? Use α = 0.05. Include all details of your test.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant single predictor of the price of used Hyundai Elantras? Use α = 0.05. Include all details of your test.
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36
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Explain why Age is a potential confounding variable in the relationship between Age and Price of used Hyundai Elantras.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Explain why Age is a potential confounding variable in the relationship between Age and Price of used Hyundai Elantras.
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37
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is the two predictor model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is the two predictor model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
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38
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant predictor of the price of used Hyundai Elantras, even after accounting for age?
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Is mileage a significant predictor of the price of used Hyundai Elantras, even after accounting for age?
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39
Use the following to answer the questions below:
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Use the provided output to determine how many cars were in the sample.
A) 22
B) 23
C) 24
D) 25
Data were collected on the age (in years), mileage (in thousands of miles), and price (in thousands of dollars) of a random sample of used Hyundai Elantras. Output from two models are provided.
Single Predictor Model:
The regression equation is Price = 13.8 - 0.0912 Mileage
-Use the provided output to determine how many cars were in the sample.
A) 22
B) 23
C) 24
D) 25
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40
A boxplot of the residuals and a scatterplot of the residuals versus the predicted values from the two predictor model are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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41
Regression output for the model that only uses Age as a predictor in the model is provided. Assuming that the residuals for this single predictor model do not indicate any problems, is this model an improvement over the model that uses both Age and Mileage as predictors? Statistically justify your answer by discussing at least two quantitative criteria.
The regression equation is Price = 15.3 - 1.71 Age
S = 1.37179 R-Sq = 88.9% R-Sq(adj) = 88.4%
Analysis of Variance
The regression equation is Price = 15.3 - 1.71 Age
S = 1.37179 R-Sq = 88.9% R-Sq(adj) = 88.4%
Analysis of Variance
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42
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 92-year-old, 1,742 square foot house in Canton. What is the predicted selling price of this house? Round to three decimal places.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 92-year-old, 1,742 square foot house in Canton. What is the predicted selling price of this house? Round to three decimal places.
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43
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 62-year-old, 1,865 square foot house in Potsdam. What is the predicted selling price of this house? Round to three decimal places.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-One of the houses they are considering is a 62-year-old, 1,865 square foot house in Potsdam. What is the predicted selling price of this house? Round to three decimal places.
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44
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Age in context.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Age in context.
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45
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Town in context.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret the coefficient of Town in context.
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46
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-How many houses are used in this dataset?
A) 48
B) 47
C) 46
D) 45
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-How many houses are used in this dataset?
A) 48
B) 47
C) 46
D) 45
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47
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret for this model.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Interpret
for this model. Unlock Deck
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48
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Using α = 0.05, is the model effective according to the ANOVA test? Include all details of the test.
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
-Using α = 0.05, is the model effective according to the ANOVA test? Include all details of the test.
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49
Use the following to answer the questions below:
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-Which predictors are significant at the 5% level?
A) Size and Age
B) Size
C) Age
D) Size, Age, and Town
A quantitatively savvy, young couple is interested in purchasing a home in northern New York. They collected data on houses that had recently sold in the two towns they are considering. The variables they collected are the selling price of the home (in thousands of dollars), the size of the home (in square feet), the age of the home (in years), and the town in which the house is located (coded 1 = Canton and 0 = Potsdam). Output from their multiple regression analysis is provided.
The regression equation is
Price (in thousands) = 69.2 + 0.0627 Size (sq. ft.) - 0.632 Age + 1.6 Town
?
-Which predictors are significant at the 5% level?
A) Size and Age
B) Size
C) Age
D) Size, Age, and Town
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50
A dotplot of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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51
Regression output for a model that omits Town as a predictor is provided. Assuming that the residuals for this reduced model do not indicate any problems with using multiple regression, is this model an improvement over the model that uses Size, Age, and Town as predictors? Statistically justify your answer by discussing at least two quantitative criteria.
The regression equation is
Price (in thousands) = 70.6 + 0.0624 Size (sq. ft.) - 0.635 Age
S = 39.6368 R-Sq = 59.3% R-Sq(adj) = 57.5%
Analysis of Variance
The regression equation is
Price (in thousands) = 70.6 + 0.0624 Size (sq. ft.) - 0.635 Age
S = 39.6368 R-Sq = 59.3% R-Sq(adj) = 57.5%
Analysis of Variance
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52
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Monday with a high temperature of 62°F. Use one decimal place in your answer.
A) 116.4 kilowatt hours
B) 91.2 kilowatt hours
C) 32.8 kilowatt hours
D) 141.6 kilowatt hours
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Monday with a high temperature of 62°F. Use one decimal place in your answer.
A) 116.4 kilowatt hours
B) 91.2 kilowatt hours
C) 32.8 kilowatt hours
D) 141.6 kilowatt hours
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53
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Saturday with a high temperature of 68°F. Use one decimal place in your answer.
A) 94.4 kilowatt hours
B) 119.6 kilowatt hours
C) 58.9 kilowatt hours
D) 92.4 kilowatt hours
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-Predict the amount of electricity used on a Saturday with a high temperature of 68°F. Use one decimal place in your answer.
A) 94.4 kilowatt hours
B) 119.6 kilowatt hours
C) 58.9 kilowatt hours
D) 92.4 kilowatt hours
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54
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of High Temp in context.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of High Temp in context.
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55
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of Weekend in context.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret the coefficient of Weekend in context.
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56
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-How many days are included in the sample?
A) 365
B) 311
C) 312
D) 313
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
?
-How many days are included in the sample?
A) 365
B) 311
C) 312
D) 313
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57
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret for this model.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Interpret
for this model. Unlock Deck
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58
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
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59
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Which predictors are significant at the 5% level? What are their p-values?
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Which predictors are significant at the 5% level? What are their p-values?
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60
Use the following to answer the questions below:
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Another possible predictor they recorded was the average temperature over the course of each day. Regression output for the model that uses High Temp, Weekend, and Avg. Temp is provided. Explain why these results differ so drastically from those for the two-predictor model.
A small university is concerned with monitoring the electricity usage in its Student Center, and its officials want to better understand what influences the amount of electricity used on a given day. They collected data on the amount of electricity used in the Student Center each day and the daily high temperature for nearly a year. They also made note of whether each day was a weekend or not (1 = Saturday/Sunday and 0 = Monday - Friday). Regression output is provided.
Helpful notes: 1) Electricity usage is measured in kilowatt hours, 2) During the cold months, the Student Center is heated by gas, not electricity, and 3) Air conditioning the building during the warm months does use electricity.
The regression equation is Electricity = 83.6 + 0.529 High Temp - 25.2 Weekend
-Another possible predictor they recorded was the average temperature over the course of each day. Regression output for the model that uses High Temp, Weekend, and Avg. Temp is provided. Explain why these results differ so drastically from those for the two-predictor model.
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61
A histogram of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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62
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in a home game? Round to one decimal place.
A) 93.2 points
B) 110.8 points
C) 94.0 points
D) 111.8 points
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in a home game? Round to one decimal place.
A) 93.2 points
B) 110.8 points
C) 94.0 points
D) 111.8 points
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63
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in an away game? Round to one decimal place.
A) 102.0 points
B) 101.0 points
C) 93.2 points
D) 110.8 points
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-How many points are the Celtics predicted to score in an away game? Round to one decimal place.
A) 102.0 points
B) 101.0 points
C) 93.2 points
D) 110.8 points
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64
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Interpret the for this model.
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Interpret the
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65
Use the following to answer the questions below:
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Using α = 0.05, is there a difference in the number of points scored for home and away games? Include all details of the test.
Is there such thing as a "home court/field advantage"? The number of points scored and whether or not it was a home game are available for a sample of games played by the Boston Celtics during the regular season. The Home variable is coded as 1 = home game and 0 = away game.
The regression equation is Points Scored = 102 - 8.76 Home
-Using α = 0.05, is there a difference in the number of points scored for home and away games? Include all details of the test.
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66
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Hyundai Elantra? Round to three decimal places.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Hyundai Elantra? Round to three decimal places.
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67
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Toyota Camry? Round to three decimal places.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-What is the predicted price of a 6-year-old Toyota Camry? Round to three decimal places.
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68
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret the coefficient of Model in context.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret the coefficient of Model in context.
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69
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret for this model.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Interpret
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70
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Is the model effective according to the ANOVA test? Use α = 0.05. Include all details of the test.
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71
Use the following to answer the questions below:
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Which predictors are significant at the 5% level? What are their p-values?
Does the price of used cars depend upon the model? Data were collected on the selling price and age of used Hyundai Elantras (coded as Model = 1) and Toyota Camrys (coded as Model = 0). Output from the multiple regression analysis is provided.
The regression equation is Price = 14.5 - 0.619 Age - 3.63 Model
-Which predictors are significant at the 5% level? What are their p-values?
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72
A histogram of the residuals and a scatterplot of the residuals versus the predicted values are provided. Discuss whether the conditions for a multiple linear regression are reasonable by referring to the appropriate plots.
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