Exam 12: Simple Linear Regression

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TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:    . -True of False: The Chancellor of a university has commissioned a team to collect data on students' GPAs and the amount of time they spend bar hopping every week (measured in minutes). He wants to know if imposing much tougher regulations on all campus bars to make it more difficult for students to spend time in any campus bar will have a significant impact on general students' GPAs. His team should use a t test on the slope of the population regression. . -True of False: The Chancellor of a university has commissioned a team to collect data on students' GPAs and the amount of time they spend bar hopping every week (measured in minutes). He wants to know if imposing much tougher regulations on all campus bars to make it more difficult for students to spend time in any campus bar will have a significant impact on general students' GPAs. His team should use a t test on the slope of the population regression.

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TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, the normality of error assumption appears to have been violated. TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, the normality of error assumption appears to have been violated. TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, the normality of error assumption appears to have been violated. -Referring to Table 12-11, the normality of error assumption appears to have been violated.

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TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, the estimated mean amount of time it takes to record one additional loan application is Note: 4.3946E-15 is 4.3946 x 10-15. TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, the estimated mean amount of time it takes to record one additional loan application is TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, the estimated mean amount of time it takes to record one additional loan application is -Referring to Table 12-12, the estimated mean amount of time it takes to record one additional loan application is

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TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:    . -Referring to Table 12-2, what is the estimated slope for the candy bar price and sales data? . -Referring to Table 12-2, what is the estimated slope for the candy bar price and sales data?

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TABLE 12-3 The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of four students. For these four, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below. TABLE 12-3 The director of cooperative education at a state college wants to examine the effect of cooperative education job experience on marketability in the work place. She takes a random sample of four students. For these four, she finds out how many times each had a cooperative education job and how many job offers they received upon graduation. These data are presented in the table below.    -Referring to Table 12-3, the coefficient of determination is ________. -Referring to Table 12-3, the coefficient of determination is ________.

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If the plot of the residuals is fan shaped, which assumption is violated?

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TABLE 12-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. TABLE 12-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows.    -Referring to Table 12-4, the managers of the brokerage firm wanted to test the hypothesis that the number of new clients brought in had a positive impact on the amount of sales generated. The p-value of the test is ________. -Referring to Table 12-4, the managers of the brokerage firm wanted to test the hypothesis that the number of new clients brought in had a positive impact on the amount of sales generated. The p-value of the test is ________.

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TABLE 12-10 The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousand of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results: TABLE 12-10 The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousand of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results:    -Referring to Table 12-10, what is the p-value of the t test statistic when testing whether the number of customers who make a purchase affects weekly sales? -Referring to Table 12-10, what is the p-value of the t test statistic when testing whether the number of customers who make a purchase affects weekly sales?

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TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, what are the lower and upper limits of the 95% confidence interval estimate for the mean change in revenue as a result of a 1 thousand increase in the number of downloads? TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, what are the lower and upper limits of the 95% confidence interval estimate for the mean change in revenue as a result of a 1 thousand increase in the number of downloads? TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, what are the lower and upper limits of the 95% confidence interval estimate for the mean change in revenue as a result of a 1 thousand increase in the number of downloads? -Referring to Table 12-11, what are the lower and upper limits of the 95% confidence interval estimate for the mean change in revenue as a result of a 1 thousand increase in the number of downloads?

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TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed: TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, what is the p-value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance? TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, what is the p-value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance? TABLE 12-11 A computer software developer would like to use the number of downloads (in thousands) for the trial version of his new shareware to predict the amount of revenue (in thousands of dollars) he can make on the full version of the new shareware. Following is the output from a simple linear regression along with the residual plot and normal probability plot obtained from a data set of 30 different sharewares that he has developed:            -Referring to Table 12-11, what is the p-value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance? -Referring to Table 12-11, what is the p-value for testing whether there is a linear relationship between revenue and the number of downloads at a 5% level of significance?

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TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below: TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:    . -Referring to Table 12-2, what is the standard error of the regression slope estimate,   ? . -Referring to Table 12-2, what is the standard error of the regression slope estimate, TABLE 12-2 A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do this, the company randomly chooses six small cities and offers the candy bar at different prices. Using candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:    . -Referring to Table 12-2, what is the standard error of the regression slope estimate,   ? ?

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TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, predict the amount of time it would take to process 150 invoices. Note: 4.3946E-15 is 4.3946 x 10-15. TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, predict the amount of time it would take to process 150 invoices. TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, predict the amount of time it would take to process 150 invoices. -Referring to Table 12-12, predict the amount of time it would take to process 150 invoices.

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The sample correlation coefficient between X and Y is 0.375. It has been found out that the p-value is 0.256 when testing H₀: ρ = 0 against the one-sided alternative H₁: ρ > 0. To test H₀: ρ = 0 against the two-sided alternative H₁: ρ ≠ 0 at a significance level of 0.1, the p-value is ________.

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TABLE 12-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows. TABLE 12-4 The managers of a brokerage firm are interested in finding out if the number of new clients a broker brings into the firm affects the sales generated by the broker. They sample 12 brokers and determine the number of new clients they have enrolled in the last year and their sales amounts in thousands of dollars. These data are presented in the table that follows.    -Referring to Table 12-4, the managers of the brokerage firm wanted to test the hypothesis that the number of new clients brought in had a positive impact on the amount of sales generated. At a level of significance of 0.01, the null hypothesis should be ________ (rejected or not rejected). -Referring to Table 12-4, the managers of the brokerage firm wanted to test the hypothesis that the number of new clients brought in had a positive impact on the amount of sales generated. At a level of significance of 0.01, the null hypothesis should be ________ (rejected or not rejected).

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TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, there is sufficient evidence that the amount of time needed linearly depends on the number of loan applications at a 5% level of significance. Note: 4.3946E-15 is 4.3946 x 10-15. TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, there is sufficient evidence that the amount of time needed linearly depends on the number of loan applications at a 5% level of significance. TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, there is sufficient evidence that the amount of time needed linearly depends on the number of loan applications at a 5% level of significance. -Referring to Table 12-12, there is sufficient evidence that the amount of time needed linearly depends on the number of loan applications at a 5% level of significance.

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TABLE 12-13 In this era of tough economic conditions, voters increasingly ask the question: "Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education?" The partial computer output below is the result of using spending per student ($) as the independent variable and composite score, which is the sum of the math, science, and reading scores, as the dependent variable on 35 states that participated in a study. The table includes only partial results. TABLE 12-13 In this era of tough economic conditions, voters increasingly ask the question: Is the educational achievement level of students dependent on the amount of money the state in which they reside spends on education? The partial computer output below is the result of using spending per student ($) as the independent variable and composite score, which is the sum of the math, science, and reading scores, as the dependent variable on 35 states that participated in a study. The table includes only partial results.    -Referring to Table 12-13, the critical value at 5% level of significance of the F test on whether spending per student affects composite score is ________. -Referring to Table 12-13, the critical value at 5% level of significance of the F test on whether spending per student affects composite score is ________.

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TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output: TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application is Note: 4.3946E-15 is 4.3946 x 10-15. TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application is TABLE 12-12 The manager of the purchasing department of a large savings and loan organization would like to develop a model to predict the amount of time (measured in hours) it takes to record a loan application. Data are collected from a sample of 30 days, and the number of applications recorded and completion time in hours is recorded. Below is the regression output:     Note: 4.3946E-15 is 4.3946 x 10-15.        -Referring to Table 12-12, the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application is -Referring to Table 12-12, the 90% confidence interval for the mean change in the amount of time needed as a result of recording one additional loan application is

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The least squares method minimizes which of the following?

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TABLE 12-6 The following Microsoft Excel tables are obtained when "Score received on an exam (measured in percentage points)" (Y) is regressed on "percentage attendance" (X) for 22 students in a Statistics for Business and Economics course. TABLE 12-6 The following Microsoft Excel tables are obtained when Score received on an exam (measured in percentage points) (Y) is regressed on percentage attendance (X) for 22 students in a Statistics for Business and Economics course.    -Referring to Table 12-6, which of the following statements is true? -Referring to Table 12-6, which of the following statements is true?

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TABLE 12-10 The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousand of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results: TABLE 12-10 The management of a chain electronic store would like to develop a model for predicting the weekly sales (in thousand of dollars) for individual stores based on the number of customers who made purchases. A random sample of 12 stores yields the following results:    -Referring to Table 12-10, the residual plot indicates possible violation of which assumptions? -Referring to Table 12-10, the residual plot indicates possible violation of which assumptions?

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