Question 1

Which expression is a regression equation for a simple linear relationship in a population?

Accepted Answer

A)  $\hat{y}$ = b0 + b1 x 
B)  $\hat{y}$ = 44 + 0.60 x 
C)    
D)    
A)  $\hat{y}$ = b0 + b1 x 
B)  $\hat{y}$ = 44 + 0.60 x 
C)    
D)

Question 2

Use the following information for questions:
 Data has been obtained on the house size (in square feet) and the selling price (in dollars) for a sample of 100 homes in your town. Your friend is saving to buy a house and she asks you to investigate the relationship between house size and selling price and to develop a model to predict the price from size.
-Report the corresponding p-value and state the conclusion in context of this problem.

Accepted Answer

There is sufficient evidence t

Question 3

Use the following information for questions:
 A sample of 19 female bears was measured for chest girth (y) and neck girth (x), both in inches. The least squares regression equation relating the two variables is $\hat{y}$= 5.32 + 1.53 x. The standard deviation of the regression is s = 3.692.
-What is a 95% prediction interval for the chest girth of a bear with a neck girth of 20 inches? The standard error of the fit, s.e.(fit), = 0.880

Accepted Answer

A)  (27.9, 43.9) 
B)  (28.1, 43.7) 
C)  (34.1, 37.8) 
D)  None of the above 
A)  (27.9, 43.9) 
B)  (28.1, 43.7) 
C)  (34.1, 37.8) 
D)  None of the above

Question 4

To determine if there is a statistically significant relationship between two quantitative variables, one test that can be conducted is

Accepted Answer

A)  a t-test of the null hypotheses that the slope of the regression line is zero. 
B)  a t-test of the null hypotheses that the intercept of the regression line is zero. 
C)  a test that the correlation coefficient is less than one. 
D)  a test that the correlation coefficient is greater than one. 
A)  a t-test of the null hypotheses that the slope of the regression line is zero. 
B)  a t-test of the null hypotheses that the intercept of the regression line is zero. 
C)  a test that the correlation coefficient is less than one. 
D)  a test that the correlation coefficient is greater than one.

Question 5

Use the following information for questions:
Can an athlete's cardiovascular fitness (as measured by time to exhaustion on a treadmill) help to predict the athlete's performance in a 20k ski race? To address this question the time to exhaustion on a treadmill (minutes) and the time to complete a 20k ski race (minutes) were recorded for a sample of 11 athletes. The researcher hypothesizes that there would be an inverse linear relationship, that is, the longer the time to exhaustion, the faster the athlete's time in the 20k ski race, on average. The data were used to provide the following output.    
-Predict the race time for a skier who had a treadmill exhaustion time of 10 minutes.

Accepted Answer

predicted race time

Question 6

Shown below is a residual plot made as part of a regression analysis of y = ideal weight and x = actual weight for 114 women.
   Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Accepted Answer

No, there is no appa

Question 7

The r2 value is reported by a researcher to be 49%. Which of the following statements is correct?

Accepted Answer

A)  The explanatory variable explains 49% of the variability in the response variable. 
B)  The explanatory variable explains 70% of the variability in the response variable. 
C)  The response variable explains 49% of the variability in the explanatory variable. 
D)  The response variable explains 70% of the variability in the explanatory variable. 
A)  The explanatory variable explains 49% of the variability in the response variable. 
B)  The explanatory variable explains 70% of the variability in the response variable. 
C)  The response variable explains 49% of the variability in the explanatory variable. 
D)  The response variable explains 70% of the variability in the explanatory variable.

Question 8

Use the following information for questions:
 Based on a representative sample of college men, a regression line relating y = ideal weight tox = actual weight, for men, is given by
Ideal weight = 53 + 0.7 *actual weight
-In this situation, if a man has a residual of 10 pounds it means that

Accepted Answer

A)  his predicted ideal weight is 10 pounds more than his stated ideal weight. 
B)  his predicted ideal weight is 10 pounds less than his stated ideal weight. 
C)  his predicted ideal weight is 10 pounds more than his actual weight. 
D)  his predicted ideal weight is 10 pounds less than his actual weight. 
A)  his predicted ideal weight is 10 pounds more than his stated ideal weight. 
B)  his predicted ideal weight is 10 pounds less than his stated ideal weight. 
C)  his predicted ideal weight is 10 pounds more than his actual weight. 
D)  his predicted ideal weight is 10 pounds less than his actual weight.

Question 9

Use the following information for questions:
 Grades for a random sample of students who have taken statistics from a certain professor over the past 20 year were used to estimate the relationship between y = grade on the final exam and x = average exam score (for the three exams given during the term).  
-The results for a test of H0:$\beta$1 = 0 versus Ha$\beta$1 $\neq$ 0 show that

Accepted Answer

A)  the null hypothesis can be rejected because t = 3.91 and the p-value = 0.000. 
B)  the null hypothesis can be rejected because t = 14.01 and the p-value = 0.000. 
C)  the null hypothesis cannot be rejected because t = 3.91 and the p-value = 0.000. 
D)  the null hypothesis cannot be rejected because t = 14.01 and the p-value = 0.000. 
A)  the null hypothesis can be rejected because t = 3.91 and the p-value = 0.000. 
B)  the null hypothesis can be rejected because t = 14.01 and the p-value = 0.000. 
C)  the null hypothesis cannot be rejected because t = 3.91 and the p-value = 0.000. 
D)  the null hypothesis cannot be rejected because t = 14.01 and the p-value = 0.000.

Question 10

Use the following information for questions:
 The relation between y = ideal weight (lbs) and x =actual weight (lbs), based on data fromn = 119 women, resulted in the regression line $\hat{y}$ = 44 + 0.60 x
-What is the interpretation of the value 0.60 in the regression equation for this question?

Accepted Answer

A)  The proportion of women whose ideal weight is greater than their actual weight. 
B)  The estimated increase in average actual weight for an increase on one pound in ideal weight. 
C)  The estimated increase in average ideal weight for an increase of one pound in actual weight. 
D)  None of the above 
A)  The proportion of women whose ideal weight is greater than their actual weight. 
B)  The estimated increase in average actual weight for an increase on one pound in ideal weight. 
C)  The estimated increase in average ideal weight for an increase of one pound in actual weight. 
D)  None of the above

Question 11

Consider the residual plot shown below made as part of a regression of y = fuel efficiency of a car (in MPG) and x = fuel capacity of the car (in gallons).
   Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Accepted Answer

Yes. Even though there is no a

Question 12

Use the following information for questions:
 A representative sample of 190 students resulted in a regression equation between y = left hand spans (cm) and x = right hand spans (cm). The least squares regression equation is $\hat{y}$ = 1.46 + 0.938 x. The error sum of squares (SSE) was 76.67, and total sum of squares (SSTO) was 784.8.
-What is the estimated standard deviation for the regression, s?

Accepted Answer

A)  0.6352 
B)  0.6369 
C)  0.6386 
D)  None of the above 
A)  0.6352 
B)  0.6369 
C)  0.6386 
D)  None of the above

Question 13

Shown below is a scatterplot of y versus x.   Which choice is most likely to be the approximate value of r2, the proportion of variation in y explained by the linear relationship with x?

Accepted Answer

A)  0% 
B)  63% 
C)  95% 
D)  99% 
A)  0% 
B)  63% 
C)  95% 
D)  99%

Question 14

Use the following information for questions:
 A car salesman is curious if he can predict the fuel efficiency of a car (in MPG) if he knows the fuel capacity of the car (in gallons). He collects data on a variety of makes and models of cars. The scatterplot shows that a linear model is appropriate. SPSS output is provided below.
      
-How would the 98% prediction interval for the fuel efficiency for Bob's car that holds 18 gallons of fuel compare to the interval calculated in question 89?

Accepted Answer

A)  Narrower 
B)  Wider 
A)  Narrower 
B)  Wider

Question 15

Use the following information for questions:
 A linear regression analysis of the relationship between y = daily hours of TV watched andx = age is done using data from n = 50 adults. The error sum of squares is SSE = 1,000. The total sum of squares is SSTO = 5,000.
-What is the estimated standard deviation of the regression, s?

Accepted Answer

The answer of Use the following information for questions:
 A...

Question 16

Use the following information for questions:
 A car salesman is curious if he can predict the fuel efficiency of a car (in MPG) if he knows the fuel capacity of the car (in gallons). He collects data on a variety of makes and models of cars. The scatterplot shows that a linear model is appropriate. SPSS output is provided below.
      
-How many cars were included in the study?

Accepted Answer

The answer of Use the following information for questions:
 A...

Question 17

Use the following information for questions:
 The data from a representative sample of 43 male college students was used to determine a regression equation for y = weight (lbs) and x = height (inches). The least squares regression equation was $\hat{y}$ = -318 + 7.00 x. The error sum of squares (SSE) was 23617; the total sum of squares (SSTO) = 34894.
-For a male student with a height of 70 inches, what is the estimated weight?

Accepted Answer

A)  165 
B)  170 
C)  172 
D)  None of the above 
A)  165 
B)  170 
C)  172 
D)  None of the above

Question 18

Use the following information for questions:
 The data from a representative sample of 43 male college students was used to determine a regression equation for y = weight (lbs) and x = height (inches). The least squares regression equation was $\hat{y}$ = -318 + 7.00 x. The error sum of squares (SSE) was 23617; the total sum of squares (SSTO) = 34894.
-What is the estimated standard deviation for the regression, s?

Accepted Answer

A)  24.0 
B)  23.7 
C)  23.4 
D)  None of the above 
A)  24.0 
B)  23.7 
C)  23.4 
D)  None of the above

Question 19

Consider the residual plot shown below made as part of a regression of y = foot length of college men (in centimeters) and x = height of college men (in inches).
   Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Accepted Answer

No, there is no appa

Question 20

Which statement is not one of the assumptions made about a simple linear regression model for a population?

Accepted Answer

A)  The variance of Y is the same for every value of x. 
B)  The distribution of Y follows a normal distribution at every value of x. 
C)  The average (or mean) of Y changes linearly with x. 
D)  The deviations (or residuals) follow a normal distribution with mean   . 
A)  The variance of Y is the same for every value of x. 
B)  The distribution of Y follows a normal distribution at every value of x. 
C)  The average (or mean) of Y changes linearly with x. 
D)  The deviations (or residuals) follow a normal distribution with mean   .

Which expression is a regression equation for a simple linear relationship in a population?

To determine if there is a statistically significant relationship between two quantitative variables, one test that can be conducted is

Shown below is a residual plot made as part of a regression analysis of y = ideal weight and x = actual weight for 114 women. Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

The r² value is reported by a researcher to be 49%. Which of the following statements is correct?

Use the following information for questions: Based on a representative sample of college men, a regression line relating y = ideal weight tox = actual weight, for men, is given by Ideal weight = 53 + 0.7 *actual weight -In this situation, if a man has a residual of 10 pounds it means that

Use the following information for questions: The relation between y = ideal weight (lbs) and x =actual weight (lbs), based on data fromn = 119 women, resulted in the regression line $\hat{y}$ = 44 + 0.60 x -What is the interpretation of the value 0.60 in the regression equation for this question?

Consider the residual plot shown below made as part of a regression of y = fuel efficiency of a car (in MPG) and x = fuel capacity of the car (in gallons). Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Shown below is a scatterplot of y versus x. Which choice is most likely to be the approximate value of r², the proportion of variation in y explained by the linear relationship with x?

Consider the residual plot shown below made as part of a regression of y = foot length of college men (in centimeters) and x = height of college men (in inches). Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Which statement is not one of the assumptions made about a simple linear regression model for a population?

Statistics Success Stories and Cautionary Tales

Turning Data Into Information

Relationships Between Quantitative Variables

Relationships Between Categorical Variables

Sampling: Surveys and How to Ask Questions

Gathering Useful Data for Examining Relationships

Probability

Random Variables

Understanding Sampling Distributions: Statistics As Random Variables

Estimating Proportions With Confidence

Estimating Means With Confidence

Testing Hypotheses About Proportions

Testing Hypotheses About Means

More About Inference for Categorical Variables

Analysis of Variance

Turning Information Into Wisdom

Filters

Exam 14: Inference About Simple Regression

Which expression is a regression equation for a simple linear relationship in a population?

To determine if there is a statistically significant relationship between two quantitative variables, one test that can be conducted is

Shown below is a residual plot made as part of a regression analysis of y = ideal weight and x = actual weight for 114 women. Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

The r2 value is reported by a researcher to be 49%. Which of the following statements is correct?

Use the following information for questions: Based on a representative sample of college men, a regression line relating y = ideal weight tox = actual weight, for men, is given by Ideal weight = 53 + 0.7 *actual weight -In this situation, if a man has a residual of 10 pounds it means that

Consider the residual plot shown below made as part of a regression of y = fuel efficiency of a car (in MPG) and x = fuel capacity of the car (in gallons). Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Shown below is a scatterplot of y versus x. Which choice is most likely to be the approximate value of r2, the proportion of variation in y explained by the linear relationship with x?

Consider the residual plot shown below made as part of a regression of y = foot length of college men (in centimeters) and x = height of college men (in inches). Based on the residual plot, is there any evidence that one of the assumptions for a linear regression model does not hold? Explain.

Which statement is not one of the assumptions made about a simple linear regression model for a population?

Statistics Success Stories and Cautionary Tales

Turning Data Into Information

Relationships Between Quantitative Variables

Relationships Between Categorical Variables

Sampling: Surveys and How to Ask Questions

Gathering Useful Data for Examining Relationships

Probability

Random Variables

Understanding Sampling Distributions: Statistics As Random Variables

Estimating Proportions With Confidence

Estimating Means With Confidence

Testing Hypotheses About Proportions

Testing Hypotheses About Means

More About Inference for Categorical Variables

Analysis of Variance

Turning Information Into Wisdom

Filters

The r² value is reported by a researcher to be 49%. Which of the following statements is correct?

Shown below is a scatterplot of y versus x. Which choice is most likely to be the approximate value of r², the proportion of variation in y explained by the linear relationship with x?