Question 1

Which of the following statements is true with respect to a simple linear regression model?

Accepted Answer

A) The regression slope coefficient is the square of the correlation coefficient. 
B) The percentage of variation in the dependent variable that is explained by the independent variable can be determined by squaring the correlation coefficient. 
C) It is possible that the correlation between a y and x variable might be statistically significant,but the regression slope coefficient could be determined to be zero since they measure different things. 
D) The standard error of the estimate is equal to the standard error of the slope. 
A) The regression slope coefficient is the square of the correlation coefficient. 
B) The percentage of variation in the dependent variable that is explained by the independent variable can be determined by squaring the correlation coefficient. 
C) It is possible that the correlation between a y and x variable might be statistically significant,but the regression slope coefficient could be determined to be zero since they measure different things. 
D) The standard error of the estimate is equal to the standard error of the slope.

Question 2

If the R-squared value for a regression model is high,the regression model will necessarily provide accurate forecasts of the y variable.

Accepted Answer

A) True 
 B)False

Question 3

Assuming that a regression has been conducted for a group of small companies where x = the number of employees at the company,y = annual revenue of the company (recorded in thousands of dollars),and the largest company included in the study had 82 employees.The resulting regression equation is   = 59.2 + 83.4x.Which of the following is true?

Accepted Answer

A) For each additional employee,revenue on average will increase by $83.4. 
B) A company with 2100 employees could be predicted to have average revenue of about $175 million. 
C) For each additional employee,revenue on average will increase by $59.2 thousand. 
D) This model should not be used to make predictions for companies with more than 82 employees. 
A) For each additional employee,revenue on average will increase by $83.4. 
B) A company with 2100 employees could be predicted to have average revenue of about $175 million. 
C) For each additional employee,revenue on average will increase by $59.2 thousand. 
D) This model should not be used to make predictions for companies with more than 82 employees.

Question 4

In conducting a hypothesis test on the correlation between a pair of variables,we assume that each variable is normally and independently distributed.

Accepted Answer

A) True 
 B)False

Question 5

When calculating prediction intervals for predicted values of y based on a given x,all 95 percent prediction intervals will be of equal width.

Accepted Answer

A) True 
 B)False

Question 6

Recently,an automobile insurance company performed a study of a random sample of 15 of its customers to determine if there is a positive relationship between the number of miles driven and the age of the driver.The sample correlation coefficient is r = .38.Given this information,and assuming that the test is to be performed at the .05 level of significance,which of the following is the correct test statistic?

Accepted Answer

A) t = 1.4812 
B) t = 1.7709 
C) z = 2.114 
D) t = 1.74 
A) t = 1.4812 
B) t = 1.7709 
C) z = 2.114 
D) t = 1.74

Question 7

What factors are of importance to an analyst when linear regression analysis is used for descriptive purposes?

Accepted Answer

The primary objective when using regress

Question 8

If the correlation between income and number of years of education is positively correlated,the scatter plot will show more points in the lower left and upper right quadrants than in the other two quadrants.

Accepted Answer

A) True 
 B)False

Question 9

The prediction interval developed from a simple linear regression model will be at its narrowest point when the value of x used to predict y is equal to the mean value of x.

Accepted Answer

A) True 
 B)False

Question 10

State University recently randomly sampled seven students and analyzed grade point average (GPA)and number of hours worked off-campus per week.The following data were observed:   In this study the independent variable is the number of hours worked off campus per week.

Accepted Answer

A) True 
 B)False

Question 11

It is believed that number of people who attend a Mardi Gras parade each year depends on the temperature that day.A regression has been conducted on a sample of years where the temperature ranged from 28 to 64 degrees and the number of people attending ranged from 8400 to 14,600.The regression equation was found to be   = 2378 + 191x.Which of the following is true?

Accepted Answer

A) The average change in parade attendance is an additional 2378 people per one-degree increase in temperature. 
B) The average change in parade attendance is an additional 191 people per one-degree increase in temperature. 
C) If the temperature is 75 degrees,we can expect that 16,703 people will attend. 
D) If the temperature is 0 degrees this year,then we should expect 2378 people to attend. 
A) The average change in parade attendance is an additional 2378 people per one-degree increase in temperature. 
B) The average change in parade attendance is an additional 191 people per one-degree increase in temperature. 
C) If the temperature is 75 degrees,we can expect that 16,703 people will attend. 
D) If the temperature is 0 degrees this year,then we should expect 2378 people to attend.

Question 12

Use the following regression results to answer the question below.   ANOVA     Which of the following is true?

Accepted Answer

A) x explains about 88.5 percent of the variation in y. 
B) y explains about 88.5 percent of the variation in x. 
C) x explains about 78.4 percent of the variation in y. 
D) y explains about 78.4 percent of the variation in x. 
A) x explains about 88.5 percent of the variation in y. 
B) y explains about 88.5 percent of the variation in x. 
C) x explains about 78.4 percent of the variation in y. 
D) y explains about 78.4 percent of the variation in x.

Question 13

Assume that a medical research study found a correlation of -0.73 between consumption of vitamin A and the cancer rate of a particular type of cancer.This could be interpreted to mean:

Accepted Answer

A) the more vitamin A consumed,the lower a person's chances are of getting this type of cancer. 
B) the less vitamin A consumed,the lower a person's chances are of getting this type of cancer. 
C) the more vitamin A consumed,the higher a person's chances are of getting this type of cancer. 
D) vitamin A causes this type of cancer. 
A) the more vitamin A consumed,the lower a person's chances are of getting this type of cancer. 
B) the less vitamin A consumed,the lower a person's chances are of getting this type of cancer. 
C) the more vitamin A consumed,the higher a person's chances are of getting this type of cancer. 
D) vitamin A causes this type of cancer.

Question 14

The following regression output is available.Notice that some of the values are missing.   Given this information,what percent of the variation in the y variable is explained by the independent variable?

Accepted Answer

A) About 75 percent 
B) Approximately 57 percent 
C) Can't be determined without having the actual data available. 
D) About 25 percent 
A) About 75 percent 
B) Approximately 57 percent 
C) Can't be determined without having the actual data available. 
D) About 25 percent

Question 15

Which of the following statements is true?

Accepted Answer

A) The interval estimate for predicting a particular value of y given a specific x will be narrower than the interval estimate for the average value of y given a particular x. 
B) The higher the r-square value,the wider will be the prediction interval based on a simple linear regression model. 
C) The prediction interval generated from a simple linear regression model will be narrowest when the value of x used to generate the predicted y value is close to the mean value of x. 
D) The prediction interval generated from a simple linear regression model will be widest when the value of x used to generate the predicted y value is close to the mean value of x. 
A) The interval estimate for predicting a particular value of y given a specific x will be narrower than the interval estimate for the average value of y given a particular x. 
B) The higher the r-square value,the wider will be the prediction interval based on a simple linear regression model. 
C) The prediction interval generated from a simple linear regression model will be narrowest when the value of x used to generate the predicted y value is close to the mean value of x. 
D) The prediction interval generated from a simple linear regression model will be widest when the value of x used to generate the predicted y value is close to the mean value of x.

Question 16

In a study of 30 customers' utility bills in which the monthly bill was the dependent variable and the number of square feet in the house is the independent variable,the resulting regression model is   = 23.40 + 0.4x.Given this model,for a customer with a 2,000 square foot house and a monthly utility bill equal to $100.00,the residual from the regression model is approximately -$3.40.

Accepted Answer

A) True 
 B)False

Which of the following statements is true with respect to a simple linear regression model?

If the R-squared value for a regression model is high,the regression model will necessarily provide accurate forecasts of the y variable.

In conducting a hypothesis test on the correlation between a pair of variables,we assume that each variable is normally and independently distributed.

When calculating prediction intervals for predicted values of y based on a given x,all 95 percent prediction intervals will be of equal width.

What factors are of importance to an analyst when linear regression analysis is used for descriptive purposes?

If the correlation between income and number of years of education is positively correlated,the scatter plot will show more points in the lower left and upper right quadrants than in the other two quadrants.

The prediction interval developed from a simple linear regression model will be at its narrowest point when the value of x used to predict y is equal to the mean value of x.

State University recently randomly sampled seven students and analyzed grade point average (GPA)and number of hours worked off-campus per week.The following data were observed: In this study the independent variable is the number of hours worked off campus per week.

Use the following regression results to answer the question below. ANOVA Which of the following is true?

Assume that a medical research study found a correlation of -0.73 between consumption of vitamin A and the cancer rate of a particular type of cancer.This could be interpreted to mean:

The following regression output is available.Notice that some of the values are missing. Given this information,what percent of the variation in the y variable is explained by the independent variable?

Which of the following statements is true?

The Where, why, and How of Data Collection

Graphs,charts and Tablesdescribing Your Data

Describing Data Using Numerical Measures

Using Probability and Probability Distributions

Discrete Probability Distributions

Introduction to Continuous Probability Distributions

Introduction to Sampling Distributions

Estimating Single Population Parameters

Introduction to Hypothesis Testing

Estimation and Hypothesis Testing for Two Population Parameters

Hypothesis Tests and Estimation for Population Variances

Analysis of Variance

Goodness-Of-Fit Tests and Contingency Analysis

Multiple Regression Analysis and Model Building

Analyzing and Forecasting Time-Series Data

Introduction to Nonparametric Statistics

Introduction to Quality and Statistical Process Control

Introduction to Decision Analysis

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Exam 14: Introduction to Linear Regression and Correlation Analysis

Which of the following statements is true with respect to a simple linear regression model?

If the R-squared value for a regression model is high,the regression model will necessarily provide accurate forecasts of the y variable.

In conducting a hypothesis test on the correlation between a pair of variables,we assume that each variable is normally and independently distributed.

When calculating prediction intervals for predicted values of y based on a given x,all 95 percent prediction intervals will be of equal width.

What factors are of importance to an analyst when linear regression analysis is used for descriptive purposes?

If the correlation between income and number of years of education is positively correlated,the scatter plot will show more points in the lower left and upper right quadrants than in the other two quadrants.

The prediction interval developed from a simple linear regression model will be at its narrowest point when the value of x used to predict y is equal to the mean value of x.

State University recently randomly sampled seven students and analyzed grade point average (GPA)and number of hours worked off-campus per week.The following data were observed: In this study the independent variable is the number of hours worked off campus per week.

Use the following regression results to answer the question below. ANOVA Which of the following is true?

Assume that a medical research study found a correlation of -0.73 between consumption of vitamin A and the cancer rate of a particular type of cancer.This could be interpreted to mean:

The following regression output is available.Notice that some of the values are missing. Given this information,what percent of the variation in the y variable is explained by the independent variable?

Which of the following statements is true?

The Where, why, and How of Data Collection

Graphs,charts and Tablesdescribing Your Data

Describing Data Using Numerical Measures

Using Probability and Probability Distributions

Discrete Probability Distributions

Introduction to Continuous Probability Distributions

Introduction to Sampling Distributions

Estimating Single Population Parameters

Introduction to Hypothesis Testing

Estimation and Hypothesis Testing for Two Population Parameters

Hypothesis Tests and Estimation for Population Variances

Analysis of Variance

Goodness-Of-Fit Tests and Contingency Analysis

Multiple Regression Analysis and Model Building

Analyzing and Forecasting Time-Series Data

Introduction to Nonparametric Statistics

Introduction to Quality and Statistical Process Control

Introduction to Decision Analysis

Filters