Question 1

This model fits 96% of the data points exactly.

Accepted Answer

A) This is not correct.This model fits 48% of the data points exactly. 
B) This is not correct.This model fits 100% of the data points exactly. 
C) This is not correct.R2 gives the fraction of variability,not the fraction of data values. 
D) This is correct. 
E) This is not correct.R2 is a measure of the straightness of the regression. 
A) This is not correct.This model fits 48% of the data points exactly. 
B) This is not correct.This model fits 100% of the data points exactly. 
C) This is not correct.R2 gives the fraction of variability,not the fraction of data values. 
D) This is correct. 
E) This is not correct.R2 is a measure of the straightness of the regression. C

Question 2

A company has undertaken a study to predict the calorie content of a serving of breakfast cereal based on protein,fat,fibre,carbohydrates,and sugar content (all in grams).Measurements were taken from 77 different breakfast cereals.A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.845
-From this model,what is the predicted calorie content of a serving of breakfast cereal which contains 10 g of protein,3 g of fat,6 g of fibre,14 g of carbohydrates,and 2 g of sugar?

Accepted Answer

A) 203 calories 
B) 144 calories 
C) 183 calories 
D) 98 calories 
E) 111 calories 
A) 203 calories 
B) 144 calories 
C) 183 calories 
D) 98 calories 
E) 111 calories B

Question 3

Every extra kilogram of weight means an increase of 5.2 metres in length.

Accepted Answer

A) This is not correct.Every extra kilogram of weight means an increase on average of 5.2 metres in length. 
B) This is not correct.Every extra kilogram of weight means an increase of 5.2 metres in length and an increase of 2.2 centimetres in chest size. 
C) This is not correct.Weight,the response variable,does not effect the predictors. 
D) This is correct. 
E) This is not correct.Weight,a predictor,does not effect the response variables. 
A) This is not correct.Every extra kilogram of weight means an increase on average of 5.2 metres in length. 
B) This is not correct.Every extra kilogram of weight means an increase of 5.2 metres in length and an increase of 2.2 centimetres in chest size. 
C) This is not correct.Weight,the response variable,does not effect the predictors. 
D) This is correct. 
E) This is not correct.Weight,a predictor,does not effect the response variables. C

Question 4

Use the following computer data,which refers to bear measurements,to answer the question.
Dependent variable is Weight
S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6%   
Analysis of Variance  
-Which measurement is the worst predictor of weight,after allowing for the linear effects of the other variables in the model?

Accepted Answer

A) Neck 
B) Age 
C) Head Width 
D) Length 
E) Sex 
A) Neck 
B) Age 
C) Head Width 
D) Length 
E) Sex

Question 5

A company has undertaken a study to predict the calorie content of a serving of breakfast cereal based on protein,fat,fibre,carbohydrates,and sugar content (all in grams).Measurements were taken from 77 different breakfast cereals.A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.845
-Interpret the R-squared value of 84.5%.

Accepted Answer

A) 84.5% of all calorie contents can be accurately predicted by this model. 
B) The average correlation between protein,fat,fibre,carbohydrates,and sugar is 0.845. 
C) The average correlation between protein,fat,fibre,carbohydrates,and sugar is 0.919. 
D) 84.5% of all samples would result in a useful model. 
E) 84.5% of the observed variation in calorie content can be explained by this model. 
A) 84.5% of all calorie contents can be accurately predicted by this model. 
B) The average correlation between protein,fat,fibre,carbohydrates,and sugar is 0.845. 
C) The average correlation between protein,fat,fibre,carbohydrates,and sugar is 0.919. 
D) 84.5% of all samples would result in a useful model. 
E) 84.5% of the observed variation in calorie content can be explained by this model.

Question 6

Use the following computer data,which refers to bear measurements,to answer the question.
Dependent variable is Weight
S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6%   
Analysis of Variance  
-Write the equation of the regression model.

Accepted Answer

A) Weight = -285.21 - 1.3838 Age - 11.24 Head Width + 28.594 Neck 
B) Weight = 78.45 - 0.9022 Age - 20.88 Head Width + 5.870 Neck 
C) Weight = 132425- 44142 Age - 41.81 Head Width + 0.002 Neck 
D) Weight = -285.21 + 78.45 Age - 3.64 Head Width + 0.022 Neck 
E) Weight = -3.64 - 1.53 Age - 0.54 Head Width + 4.87 Neck 
A) Weight = -285.21 - 1.3838 Age - 11.24 Head Width + 28.594 Neck 
B) Weight = 78.45 - 0.9022 Age - 20.88 Head Width + 5.870 Neck 
C) Weight = 132425- 44142 Age - 41.81 Head Width + 0.002 Neck 
D) Weight = -285.21 + 78.45 Age - 3.64 Head Width + 0.022 Neck 
E) Weight = -3.64 - 1.53 Age - 0.54 Head Width + 4.87 Neck

Question 7

Use the following computer data,which refers to bear measurements,to answer the question.
Dependent variable is Weight
S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6%   
Analysis of Variance  
-How much of the variation in bear measurements is explained by the model?

Accepted Answer

A) 2.22% 
B) 96.9% 
C) 94.6% 
D) 20% 
E) 61.9% 
A) 2.22% 
B) 96.9% 
C) 94.6% 
D) 20% 
E) 61.9%

Question 8

An anti-smoking group used data in the table to relate the carbon monoxide output of various brands of cigarettes to their tar and nicotine content.

Accepted Answer

A) CO = 1.25 + 1.55 Tar - 5.79 Nicotine 
B) CO = 1.38 - 5.53 Tar + 1.33 Nicotine 
C) CO = 1.38 + 5.50 Tar - 1.38 Nicotine 
D) CO = 1.27 - 5.53 Tar + 5.79 Nicotine 
E) CO = 1.30 + 5.50 Tar - 1.33 Nicotine 
A) CO = 1.25 + 1.55 Tar - 5.79 Nicotine 
B) CO = 1.38 - 5.53 Tar + 1.33 Nicotine 
C) CO = 1.38 + 5.50 Tar - 1.38 Nicotine 
D) CO = 1.27 - 5.53 Tar + 5.79 Nicotine 
E) CO = 1.30 + 5.50 Tar - 1.33 Nicotine

Question 9

A health specialist gathered the data in the table to see if pulse rates can be explained by exercise,smoking,and age.For exercise,he assigns 1 for yes,2 for no.For smoking,he assigns 1 for yes,2 for no.

Accepted Answer

A) Pulse = 58.04 + 10.57 Exercise - 3.77 Smoke + 0.47 Age 
B) Pulse = 24.1 + 8.15 Exercise + 6.33 Smoke + 0.83 Age 
C) Pulse = 37.3 + 9.24 Exercise + 1.15 Smoke + 1.2 Age 
D) Pulse = 58.04 + 10.57 Exercise + 3.77 Smoke + 0.47 Age 
E) Pulse = 37.3 + 9.4 Exercise + 1.6 Smoke + 1.2 Age 
A) Pulse = 58.04 + 10.57 Exercise - 3.77 Smoke + 0.47 Age 
B) Pulse = 24.1 + 8.15 Exercise + 6.33 Smoke + 0.83 Age 
C) Pulse = 37.3 + 9.24 Exercise + 1.15 Smoke + 1.2 Age 
D) Pulse = 58.04 + 10.57 Exercise + 3.77 Smoke + 0.47 Age 
E) Pulse = 37.3 + 9.4 Exercise + 1.6 Smoke + 1.2 Age

Question 10

A company has undertaken a study of 16 secretaries' yearly salaries (in thousands of dollars).They want to predict salaries from several other variables.
The variables considered to be potential predictors of salary are:
X1 = months of service
X2 = years of education
X3 = score on standardized test
X4 = words per minute of typing speed
X5 = ability to take dictation in words per minute
A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.958
-From this model,what is the predicted salary of a secretary with 2.5 years (30 months)experience,10th grade education (10 years of education),an 80 on the standardized test,45 wpm typing speed,and the ability to take 30 wpm dictation?

Accepted Answer

A) $47,371 
B) $24,054 
C) $75,431 
D) $144,225 
E) $42,600 
A) $47,371 
B) $24,054 
C) $75,431 
D) $144,225 
E) $42,600

Question 11

A company has undertaken a study of 16 secretaries' yearly salaries (in thousands of dollars).They want to predict salaries from several other variables.
The variables considered to be potential predictors of salary are:
X1 = months of service
X2 = years of education
X3 = score on standardized test
X4 = words per minute of typing speed
X5 = ability to take dictation in words per minute
A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.958
-Which measurement is the worst predictor of salary,after allowing for the linear effects of the other variables in the model?

Accepted Answer

A) months of service 
B) words per minute of typing speed 
C) ability to take dictation in words per minute 
D) score on standardized test 
E) years of education 
A) months of service 
B) words per minute of typing speed 
C) ability to take dictation in words per minute 
D) score on standardized test 
E) years of education

Question 12

A company has undertaken a study to predict the calorie content of a serving of breakfast cereal based on protein,fat,fibre,carbohydrates,and sugar content (all in grams).Measurements were taken from 77 different breakfast cereals.A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.845
-Which measurement is the worst predictor of calorie content,after allowing for the linear effects of the other variables in the model?

Accepted Answer

A) sugar 
B) carbohydrates 
C) protein 
D) fat 
E) fibre 
A) sugar 
B) carbohydrates 
C) protein 
D) fat 
E) fibre

Question 13

Every extra centimetre of the chest adds 2.2 kg to the average weight,for a given length and sex.

Accepted Answer

A) This is not correct.Weight,the response variable,does not effect the predictors. 
B) This is correct. 
C) This is not correct.Specific values for the other predictors are not given. 
D) This is not correct.Every extra centimetres of the chest adds 2.2 kg to the average weight,for any length and sex. 
E) This is not correct.Chest size does not effect weight. 
A) This is not correct.Weight,the response variable,does not effect the predictors. 
B) This is correct. 
C) This is not correct.Specific values for the other predictors are not given. 
D) This is not correct.Every extra centimetres of the chest adds 2.2 kg to the average weight,for any length and sex. 
E) This is not correct.Chest size does not effect weight.

Question 14

A company has undertaken a study to predict the calorie content of a serving of breakfast cereal based on protein,fat,fibre,carbohydrates,and sugar content (all in grams).Measurements were taken from 77 different breakfast cereals.A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.845
-Which measurement is the best predictor of calorie content,after allowing for the linear effects of the other variables in the model?

Accepted Answer

A) protein 
B) sugar 
C) carbohydrates 
D) fat 
E) fibre 
A) protein 
B) sugar 
C) carbohydrates 
D) fat 
E) fibre

Question 15

What does the coefficient of neck mean?

Accepted Answer

A) For every measurement unit of the neck,all other measurements will increase by 28.594 units. 
B) For every measurement unit of the neck,the average head width will decrease by -11.24 units. 
C) For every measurement unit of the neck,the average weight will increase by 28.594 units. 
D) For every measurement unit of the neck,the average weight will increase by one unit. 
E) For every measurement unit of the neck,the average age will decrease by -1.3838 units. 
A) For every measurement unit of the neck,all other measurements will increase by 28.594 units. 
B) For every measurement unit of the neck,the average head width will decrease by -11.24 units. 
C) For every measurement unit of the neck,the average weight will increase by 28.594 units. 
D) For every measurement unit of the neck,the average weight will increase by one unit. 
E) For every measurement unit of the neck,the average age will decrease by -1.3838 units.

Question 16

A visitor to Yellowstone National Park in Wyoming,U.S.A,sat down one day and observed Old Faithful,which faithfully erupts throughout the day,day in and day out.He surmised that the height of a given eruption was caused by the pressure buildup during the interval between eruptions and by the momentum buildup during the duration of the eruption.He wrote down the data to test his hypothesis,but he didn't know what to do with his data.

Accepted Answer

A) Height = 125.1 + 0.36 Interval - 0.89 Duration 
B) Height = 24.8 + 0.53 Interval - 0.11 Duration 
C) Height = 126.3 + 0.37 Interval - 0.079 Duration 
D) Height = 126.3 + 0.73 Interval - 0.11 Duration 
E) Height = 25.1 + 0.73 Interval - 0.62 Duration 
A) Height = 125.1 + 0.36 Interval - 0.89 Duration 
B) Height = 24.8 + 0.53 Interval - 0.11 Duration 
C) Height = 126.3 + 0.37 Interval - 0.079 Duration 
D) Height = 126.3 + 0.73 Interval - 0.11 Duration 
E) Height = 25.1 + 0.73 Interval - 0.62 Duration

Question 17

Use the following computer data,which refers to bear measurements,to answer the question.
Dependent variable is Weight
S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6%   
Analysis of Variance  
-Which measurement is the best predictor of weight,after allowing for the linear effects of the other variables in the model?

Accepted Answer

A) Age 
B) Sex 
C) Length 
D) Neck 
E) Head Width 
A) Age 
B) Sex 
C) Length 
D) Neck 
E) Head Width

Question 18

A company has undertaken a study of 16 secretaries' yearly salaries (in thousands of dollars).They want to predict salaries from several other variables.
The variables considered to be potential predictors of salary are:
X1 = months of service
X2 = years of education
X3 = score on standardized test
X4 = words per minute of typing speed
X5 = ability to take dictation in words per minute
A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.958
-Interpret the R-squared value of 95.8%.

Accepted Answer

A) The average correlation between X1,X2,X3,X4,and X5 is 0.975. 
B) 95.8% of all samples would result in a useful model. 
C) 95.8% of all salaries can be accurately predicted by this model. 
D) The average correlation between X1,X2,X3,X4,and X5 is 0.958. 
E) 95.8% of the observed variation in salaries can be explained by this model. 
A) The average correlation between X1,X2,X3,X4,and X5 is 0.975. 
B) 95.8% of all samples would result in a useful model. 
C) 95.8% of all salaries can be accurately predicted by this model. 
D) The average correlation between X1,X2,X3,X4,and X5 is 0.958. 
E) 95.8% of the observed variation in salaries can be explained by this model.

Question 19

A company has undertaken a study of 16 secretaries' yearly salaries (in thousands of dollars).They want to predict salaries from several other variables.
The variables considered to be potential predictors of salary are:
X1 = months of service
X2 = years of education
X3 = score on standardized test
X4 = words per minute of typing speed
X5 = ability to take dictation in words per minute
A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.958
-Which measurement is the best predictor of salary,after allowing for the linear effects of the other variables in the model?

Accepted Answer

A) months of service 
B) words per minute of typing speed 
C) score on standardized test 
D) years of education 
E) ability to take dictation in words per minute 
A) months of service 
B) words per minute of typing speed 
C) score on standardized test 
D) years of education 
E) ability to take dictation in words per minute

Question 20

A company has undertaken a study to predict the calorie content of a serving of breakfast cereal based on protein,fat,fibre,carbohydrates,and sugar content (all in grams).Measurements were taken from 77 different breakfast cereals.A multiple regression model with all five variables was run,resulting in the following output:   
   
= 0.845
-What is the regression equation?

Accepted Answer

A) calories = 5.984 + 1.072 protein +1.033 fat + 0.454 fibre +0.260 carbohydrates + 0.250 sugar 
B) calories = 20.2454 + 1.072 protein +8.09 fat + 0.454 fibre +11.30 carbohydrates + 0.250 sugar 
C) calories = 3.38 + 5.32 protein +8.09 fat - 2.11 fibre +11.30 carbohydrates + 13.30 sugar 
D) calories = 5.984 + 5.32 protein +1.033 fat - 2.11 fibre +0.260 carbohydrates + 13.30 sugar 
E) calories = 20.2454 + 5.6954 protein + 8.3596 fat - 1.0202 fibre + 2.9357 carbohydrates + 3.3185 sugar 
A) calories = 5.984 + 1.072 protein +1.033 fat + 0.454 fibre +0.260 carbohydrates + 0.250 sugar 
B) calories = 20.2454 + 1.072 protein +8.09 fat + 0.454 fibre +11.30 carbohydrates + 0.250 sugar 
C) calories = 3.38 + 5.32 protein +8.09 fat - 2.11 fibre +11.30 carbohydrates + 13.30 sugar 
D) calories = 5.984 + 5.32 protein +1.033 fat - 2.11 fibre +0.260 carbohydrates + 13.30 sugar 
E) calories = 20.2454 + 5.6954 protein + 8.3596 fat - 1.0202 fibre + 2.9357 carbohydrates + 3.3185 sugar

This model fits 96% of the data points exactly.

Every extra kilogram of weight means an increase of 5.2 metres in length.

Use the following computer data,which refers to bear measurements,to answer the question. Dependent variable is Weight S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6% Analysis of Variance -Write the equation of the regression model.

Use the following computer data,which refers to bear measurements,to answer the question. Dependent variable is Weight S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6% Analysis of Variance -How much of the variation in bear measurements is explained by the model?

An anti-smoking group used data in the table to relate the carbon monoxide output of various brands of cigarettes to their tar and nicotine content.

A health specialist gathered the data in the table to see if pulse rates can be explained by exercise,smoking,and age.For exercise,he assigns 1 for yes,2 for no.For smoking,he assigns 1 for yes,2 for no.

Every extra centimetre of the chest adds 2.2 kg to the average weight,for a given length and sex.

What does the coefficient of neck mean?

Data

Displaying and Describing Categorical Data

Displaying and Summarizing Quantitative Data

Understanding and Comparing Distributions

The Standard Deviation As a Ruler and the Normal Model

Scatterplots, association, and Correlation

Linear Regression

Regression Wisdom

Re-Expressing Data: Get It Straight

Understanding Randomness

Sample Surveys

Experiments and Observational Surveys

From Randomness to Probability

Probability Rules

Random Variables

Probability Models

Sampling Distribution Models

Confidence Intervals for Proportions

Testing Hypotheses About Proportions

More About Tests

Comparing Two Proportions

Inferences About Means

Comparing Means

Paired Samples and Blocks

Comparing Counts

Inferences for Regression

Analysis of Variance

Multifactor Analysis of Variance

Multiple Regression Wisdom

Rank-Based Nonparametric Tests

The Bootstrap

Filters

Exam 29: Multiple Regression

This model fits 96% of the data points exactly.

Every extra kilogram of weight means an increase of 5.2 metres in length.

Use the following computer data,which refers to bear measurements,to answer the question. Dependent variable is Weight S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6% Analysis of Variance -Write the equation of the regression model.

Use the following computer data,which refers to bear measurements,to answer the question. Dependent variable is Weight S = 32.49 R-Sq = 96.9% R-Sq (adj)= 94.6% Analysis of Variance -How much of the variation in bear measurements is explained by the model?

An anti-smoking group used data in the table to relate the carbon monoxide output of various brands of cigarettes to their tar and nicotine content.

A health specialist gathered the data in the table to see if pulse rates can be explained by exercise,smoking,and age.For exercise,he assigns 1 for yes,2 for no.For smoking,he assigns 1 for yes,2 for no.

Every extra centimetre of the chest adds 2.2 kg to the average weight,for a given length and sex.

What does the coefficient of neck mean?

Data

Displaying and Describing Categorical Data

Displaying and Summarizing Quantitative Data

Understanding and Comparing Distributions

The Standard Deviation As a Ruler and the Normal Model

Scatterplots, association, and Correlation

Linear Regression

Regression Wisdom

Re-Expressing Data: Get It Straight

Understanding Randomness

Sample Surveys

Experiments and Observational Surveys

From Randomness to Probability

Probability Rules

Random Variables

Probability Models

Sampling Distribution Models

Confidence Intervals for Proportions

Testing Hypotheses About Proportions

More About Tests

Comparing Two Proportions

Inferences About Means

Comparing Means

Paired Samples and Blocks

Comparing Counts

Inferences for Regression

Analysis of Variance

Multifactor Analysis of Variance

Multiple Regression Wisdom

Rank-Based Nonparametric Tests

The Bootstrap

Filters