Question 1

If we were interested in predicting the tourism revenue for a particular country that had 30 million foreign visitors

Accepted Answer

A)  We should construct a confidence interval using this equation. 
B)  We should construct a predication interval using this equation. 
C)  We should not use this equation because it is not significant. 
D)  We should use the correlation. 
E)  We should use the standard error. 
A)  We should construct a confidence interval using this equation. 
B)  We should construct a predication interval using this equation. 
C)  We should not use this equation because it is not significant. 
D)  We should use the correlation. 
E)  We should use the standard error.

Question 2

A forester would like to estimate the diameter of maple trees based on age. She gathers data from trees that have been cut and plots their diameters (in inches) against their ages (in years). She fits a linear model and both the scatterplot and residual plots are shown below.     Which of the following is true?

Accepted Answer

A)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be too low. 
B)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be quite reliable. 
C)  Re-expressing these data by taking the logarithm of age would not improve this model. 
D)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be too low or it would be too high. 
E)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be too high, furthermore, re-expressing these data by taking the logarithm of age would improve this model. 
A)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be too low. 
B)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be quite reliable. 
C)  Re-expressing these data by taking the logarithm of age would not improve this model. 
D)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be too low or it would be too high. 
E)  If we used this model to predict the diameter of a maple tree that is 50 years old, the predicted value would be too high, furthermore, re-expressing these data by taking the logarithm of age would improve this model.

Question 3

Use the following to answer the question(s) below.
A newly developed drug is tested to determine absorption levels in a patient's bloodstream. A patient is injected with the drug and the concentration (units/cc) in the patient's blood is measured every hour for seven hours. Below is the output from fitting a linear regression model.    
-Re-expressing these data results in the following model   The residuals plotted against the fitted values for this model is shown below.   What is true about the predicted concentration level after 10 hours has elapsed?

Accepted Answer

A)  The predicted value is 0.1000 units/cc. 
B)  This value is considered as a regular one. 
C)  This value is accurate because R2 = 100%. 
D)  The predicted value is 1.2589 units/cc, and this value is considered an extrapolation. 
E)  All of the given statements are true. 
A)  The predicted value is 0.1000 units/cc. 
B)  This value is considered as a regular one. 
C)  This value is accurate because R2 = 100%. 
D)  The predicted value is 1.2589 units/cc, and this value is considered an extrapolation. 
E)  All of the given statements are true.

Question 4

Which of the following is not true about the best regression models?

Accepted Answer

A)  They have relatively many predictors. 
B)  They have a relatively high R2 value. 
C)  They have predictors that are reliably measured and relatively unrelated to each other. 
D)  They have no cases with extraordinarily high leverage that might dominate and alter the model. 
E)  They have relatively small P-values for the F- and t-statistics. 
A)  They have relatively many predictors. 
B)  They have a relatively high R2 value. 
C)  They have predictors that are reliably measured and relatively unrelated to each other. 
D)  They have no cases with extraordinarily high leverage that might dominate and alter the model. 
E)  They have relatively small P-values for the F- and t-statistics.

Question 5

Use the following to answer the question(s) below.
For many countries tourism is an important source of revenue. Data are collected on the number of foreign visitors to a country (in millions) and total tourism revenue (in billions of dollars) for a sample of 10 countries. Below is the regression analysis output with tourism revenue as the dependent variable.  
-The calculated t-statistic to test whether the regression slope is significantly different from zero is

Accepted Answer

A)  6.20 
B)  13.88 
C)  0.07917 
D)  2.58307 
E)  3.73 
A)  6.20 
B)  13.88 
C)  0.07917 
D)  2.58307 
E)  3.73

Question 6

Use the following to answer the question(s) below.
Stock prices and earnings per share (EPS) data were collected for a sample of 15 companies. Below are the regression results. The dependent variable is Stock Price.  
-What is the correlation between stock price and EPS?

Accepted Answer

A)  -0.975 
B)  0.975 
C)  0.906 
D)  0.950 
E)  Cannot be determined from the information given. 
A)  -0.975 
B)  0.975 
C)  0.906 
D)  0.950 
E)  Cannot be determined from the information given.

Question 7

The residual plot for a linear regression model is shown below.   Which of the following is true?

Accepted Answer

A)  The linear model is okay because the same number of points is above the line as below it. 
B)  The linear model is okay because the association between the two variables is fairly strong. 
C)  The linear model is no good because the correlation is near 0. 
D)  The linear model is no good because some residuals are large. 
E)  The linear model is no good because of the curve in the residuals. 
A)  The linear model is okay because the same number of points is above the line as below it. 
B)  The linear model is okay because the association between the two variables is fairly strong. 
C)  The linear model is no good because the correlation is near 0. 
D)  The linear model is no good because some residuals are large. 
E)  The linear model is no good because of the curve in the residuals.

Question 8

A least squares estimated regression line has been fitted to a set of data and the resulting residual plot is shown. Which is true?

Accepted Answer

A)  The linear model is appropriate. 
B)  The linear model is poor because some residuals are large. 
C)  The linear model is poor because the correlation is near 0. 
D)  A curved model would be better. 
E)  A transformation of the data is required. 
A)  The linear model is appropriate. 
B)  The linear model is poor because some residuals are large. 
C)  The linear model is poor because the correlation is near 0. 
D)  A curved model would be better. 
E)  A transformation of the data is required.

Question 9

The model   = 3.30 + 0.235 × speed can be used to predict the stopping distance (in metres) for a car traveling at a specific speed (in kph). According to this model, about how much distance will a car going 65 kph need to stop?

Accepted Answer

A)  345.0 metres 
B)  18.6 metres 
C)  27.0 metres 
D)  4.3 metres 
E)  729.0 metres 
A)  345.0 metres 
B)  18.6 metres 
C)  27.0 metres 
D)  4.3 metres 
E)  729.0 metres

Question 10

Use the following to answer the question(s) below.
A newly developed drug is tested to determine absorption levels in a patient's bloodstream. A patient is injected with the drug and the concentration (units/cc) in the patient's blood is measured every hour for seven hours. Below is the output from fitting a linear regression model.    
-Which of the following is true?

Accepted Answer

A)  The linear model is appropriate for estimation of the concentration level after 10 hours given that it explains 90% of the variability in blood concentration levels of the drug. 
B)  This model will underestimate the concentration level after 10 hours has elapsed because the linear model is not appropriate. 
C)  This model will overestimate the concentration level after 10 hours has elapsed because the linear model is not appropriate. 
D)  The linear model is appropriate for estimation of the concentration level after 10 hours given that it explains 9.49% of the variability in blood concentration levels of the drug. 
E)  The linear model is appropriate given that it explains 90% of the variability in blood concentration levels of the drug, and it will overestimate the concentration level after 10 hours. 
A)  The linear model is appropriate for estimation of the concentration level after 10 hours given that it explains 90% of the variability in blood concentration levels of the drug. 
B)  This model will underestimate the concentration level after 10 hours has elapsed because the linear model is not appropriate. 
C)  This model will overestimate the concentration level after 10 hours has elapsed because the linear model is not appropriate. 
D)  The linear model is appropriate for estimation of the concentration level after 10 hours given that it explains 9.49% of the variability in blood concentration levels of the drug. 
E)  The linear model is appropriate given that it explains 90% of the variability in blood concentration levels of the drug, and it will overestimate the concentration level after 10 hours.

Question 11

Use the following to answer the question(s) below.
Stock prices and earnings per share (EPS) data were collected for a sample of 15 companies. Below are the regression results. The dependent variable is Stock Price.  
-Which of the following statement is true about the correlation between stock price and EPS?

Accepted Answer

A)  The correlation is negative. 
B)  The correlation is not significantly different from zero. 
C)  The correlation is positive and significantly different from zero. 
D)  The correlation is negative and significantly different from zero. 
E)  Cannot be determined from the information given. 
A)  The correlation is negative. 
B)  The correlation is not significantly different from zero. 
C)  The correlation is positive and significantly different from zero. 
D)  The correlation is negative and significantly different from zero. 
E)  Cannot be determined from the information given.

Question 12

Use the following to answer the question(s) below.
A linear regression model was fit to data representing technology adoption over time. The regression output and residual plot appears below.    
-It is known that at 1% significance level the critical values for Durbin-Watson statistic are 0.738 and 1.038. The Durbin-Watson statistic indicates

Accepted Answer

A)  that the residuals are positively autocorrelated 
B)  that the residuals are negatively autocorrelated 
C)  that the residuals are not autocorrelated 
D)  that the test is inconclusive 
E)  The Durbin-Watson cannot be used for this model. 
A)  that the residuals are positively autocorrelated 
B)  that the residuals are negatively autocorrelated 
C)  that the residuals are not autocorrelated 
D)  that the test is inconclusive 
E)  The Durbin-Watson cannot be used for this model.

Question 13

Use the following to answer the question(s) below.
Data were collected for a sample of 12 pharmacists to determine if years of experience and salary are related. Below are the regression analysis results. The dependent variable is Salary in thousands of dollars.  
-The calculated t-statistic to test whether the regression slope is significant is

Accepted Answer

A)  10.99 
B)  47.97 
C)  31.2 
D)  6.93 
E)  5.58485 
A)  10.99 
B)  47.97 
C)  31.2 
D)  6.93 
E)  5.58485

Question 14

Use the following to answer the question(s) below.
Weekly commodity prices for heating oil (in cents) were obtained and regressed against time. Below is the regression output and residual plots from fitting a linear model.        
-The Durbin-Watson statistic indicates

Accepted Answer

A)  that the residuals are positively autocorrelated 
B)  that the residuals are negatively autocorrelated 
C)  that the residuals are not autocorrelated 
D)  that the test is inconclusive 
E)  The Durbin-Watson statistic cannot be used for this model. 
A)  that the residuals are positively autocorrelated 
B)  that the residuals are negatively autocorrelated 
C)  that the residuals are not autocorrelated 
D)  that the test is inconclusive 
E)  The Durbin-Watson statistic cannot be used for this model.

Question 15

Below are residual plots for this regression model.       Based on these plots, which assumption appears to be violated?

Accepted Answer

A)  Equal Variance 
B)  Linearity 
C)  Normality 
D)  Independence 
E)  None; all appear to be satisfied. 
A)  Equal Variance 
B)  Linearity 
C)  Normality 
D)  Independence 
E)  None; all appear to be satisfied.

Question 16

Use the following to answer the question(s) below.
For many countries tourism is an important source of revenue. Data are collected on the number of foreign visitors to a country (in millions) and total tourism revenue (in billions of dollars) for a sample of 10 countries. Below is the regression analysis output with tourism revenue as the dependent variable.  
-The standard error of the slope for this estimated regression equation is

Accepted Answer

A)  2.58307 
B)  3.462 
C)  0.07917 
D)  6.672 
E)  0.29497 
A)  2.58307 
B)  3.462 
C)  0.07917 
D)  6.672 
E)  0.29497

Question 17

Use the following to answer the question(s) below.
Weekly commodity prices for heating oil (in cents) were obtained and regressed against time. Below is the regression output and residual plots from fitting a linear model.        
-Which assumption appears to be violated?

Accepted Answer

A)  Linearity 
B)  Normality 
C)  Equal Variance 
D)  Linearity and Normality 
E)  Linearity, Normality and Equal Variance assumptions are violated. 
A)  Linearity 
B)  Normality 
C)  Equal Variance 
D)  Linearity and Normality 
E)  Linearity, Normality and Equal Variance assumptions are violated.

Question 18

Which of the following measures is used to check for collinearity when building a multiple regression model?

Accepted Answer

A)  Cook's Distance 
B)  Variance Inflation Factor 
C)  Determination Coefficient 
D)  Standardized Residual 
E)  Chef's Distance 
A)  Cook's Distance 
B)  Variance Inflation Factor 
C)  Determination Coefficient 
D)  Standardized Residual 
E)  Chef's Distance

Question 19

Use the following to answer the questions below.
The following is output from regression analysis performed to develop a model for predicting a firm's Price-Earnings Ratio (PE) based on Growth Rate, Profit Margin, and whether or not the firm is Green (1 = Yes, 0 = No).  
-At α = 0.05 we can conclude that

Accepted Answer

A)  Growth Rate is not a significant variable in predicting a firm's PE ratio. 
B)  Profit Margin is a significant variable in predicting a firm's PE ratio. 
C)  The regression coefficient associated with Growth Rate is not significantly different from zero. 
D)  Whether or not a firm is Green is significant in predicting its PE ratio. 
E)  The regression coefficient associated with whether or not a firm is Green is not significantly different from zero. 
A)  Growth Rate is not a significant variable in predicting a firm's PE ratio. 
B)  Profit Margin is a significant variable in predicting a firm's PE ratio. 
C)  The regression coefficient associated with Growth Rate is not significantly different from zero. 
D)  Whether or not a firm is Green is significant in predicting its PE ratio. 
E)  The regression coefficient associated with whether or not a firm is Green is not significantly different from zero.

Question 20

Which of the following is/are true about the best regression models?

Accepted Answer

A)  They have relatively few predictors. 
B)  They have a relatively high R2 value. 
C)  They have predictors that are reliably measured and relatively unrelated to each other. 
D)  They have no cases with extraordinarily high leverage that might dominate and alter the model. 
E)  All of the given statements are true. 
A)  They have relatively few predictors. 
B)  They have a relatively high R2 value. 
C)  They have predictors that are reliably measured and relatively unrelated to each other. 
D)  They have no cases with extraordinarily high leverage that might dominate and alter the model. 
E)  All of the given statements are true.

If we were interested in predicting the tourism revenue for a particular country that had 30 million foreign visitors

Which of the following is not true about the best regression models?

Use the following to answer the question(s) below. Stock prices and earnings per share (EPS) data were collected for a sample of 15 companies. Below are the regression results. The dependent variable is Stock Price. -What is the correlation between stock price and EPS?

The residual plot for a linear regression model is shown below. Which of the following is true?

A least squares estimated regression line has been fitted to a set of data and the resulting residual plot is shown. Which is true?

The model = 3.30 + 0.235 × speed can be used to predict the stopping distance (in metres) for a car traveling at a specific speed (in kph). According to this model, about how much distance will a car going 65 kph need to stop?

Use the following to answer the question(s) below. Weekly commodity prices for heating oil (in cents) were obtained and regressed against time. Below is the regression output and residual plots from fitting a linear model. -The Durbin-Watson statistic indicates

Below are residual plots for this regression model. Based on these plots, which assumption appears to be violated?

Use the following to answer the question(s) below. Weekly commodity prices for heating oil (in cents) were obtained and regressed against time. Below is the regression output and residual plots from fitting a linear model. -Which assumption appears to be violated?

Which of the following measures is used to check for collinearity when building a multiple regression model?

Which of the following is/are true about the best regression models?

Data

Surveys and Sampling

Displaying and Describing Categorical Data

Displaying and Describing Quantitative Data

Scatterplots, Association, and Correlation

Introduction to Linear Regression

Randomness and Probability

Random Variables and Probability Distributions

Sampling Distributions

Confidence Intervals for Proportions

Testing Hypotheses About Proportions

Confidence Intervals and Hypothesis Tests for Means

Comparing Two Means

Design of Experiments and Analysis of Variance Anova

Inference for Counts: Chi-Square Tests

Nonparametric Methods

Inference for Regression

Understanding Regression Residuals

Multiple Regression

Building Multiple Regression Models

Time Series Analysis

Decision Making and Risk

Quality Control

Exploring and Collecting Data

Understanding Probability Distributions and Statistical Inference

Using Statistics for Decision Making

Filters

Exam 27: Exploring Relationships Among Variables

If we were interested in predicting the tourism revenue for a particular country that had 30 million foreign visitors

Which of the following is not true about the best regression models?

Use the following to answer the question(s) below. Stock prices and earnings per share (EPS) data were collected for a sample of 15 companies. Below are the regression results. The dependent variable is Stock Price. -What is the correlation between stock price and EPS?

The residual plot for a linear regression model is shown below. Which of the following is true?

A least squares estimated regression line has been fitted to a set of data and the resulting residual plot is shown. Which is true?

The model = 3.30 + 0.235 × speed can be used to predict the stopping distance (in metres) for a car traveling at a specific speed (in kph). According to this model, about how much distance will a car going 65 kph need to stop?

Use the following to answer the question(s) below. Weekly commodity prices for heating oil (in cents) were obtained and regressed against time. Below is the regression output and residual plots from fitting a linear model. -The Durbin-Watson statistic indicates

Below are residual plots for this regression model. Based on these plots, which assumption appears to be violated?

Use the following to answer the question(s) below. Weekly commodity prices for heating oil (in cents) were obtained and regressed against time. Below is the regression output and residual plots from fitting a linear model. -Which assumption appears to be violated?

Which of the following measures is used to check for collinearity when building a multiple regression model?

Which of the following is/are true about the best regression models?

Data

Surveys and Sampling

Displaying and Describing Categorical Data

Displaying and Describing Quantitative Data

Scatterplots, Association, and Correlation

Introduction to Linear Regression

Randomness and Probability

Random Variables and Probability Distributions

Sampling Distributions

Confidence Intervals for Proportions

Testing Hypotheses About Proportions

Confidence Intervals and Hypothesis Tests for Means

Comparing Two Means

Design of Experiments and Analysis of Variance Anova

Inference for Counts: Chi-Square Tests

Nonparametric Methods

Inference for Regression

Understanding Regression Residuals

Multiple Regression

Building Multiple Regression Models

Time Series Analysis

Decision Making and Risk

Quality Control

Exploring and Collecting Data

Understanding Probability Distributions and Statistical Inference

Using Statistics for Decision Making

Filters