The accompanying data file contains 57 months of tax revenue from medical and retail marijuana tax and fee collections. For cross- validation, let the training and the validation sets comprise the first 45 months and the last 12 months, respectively. Click here for the Excel Data File a-1. Use the training set to estimate the linear, the quadratic, and the cubic trend models and compute the resulting MSE, MAD, and MAPE for the validation set. Note: Do not round intermediate calculations. Round final answers to the nearest whole number. Model MSE Linear Quadratic Cubic Answer is not complete. MAD MAPE (%) 15 17 21

The accompanying data file contains 57 months of tax revenue from medical and retail marijuana tax and fee collections. For cross- validation, let the training and the validation sets comprise the first 45 months and the last 12 months, respectively. Click here for the Excel Data File a-1. Use the training set to estimate the linear, the quadratic, and the cubic trend models and compute the resulting MSE, MAD, and MAPE for the validation set. Note: Do not round intermediate calculations. Round final answers to the nearest whole number. Model MSE Linear Quadratic Cubic Answer is not complete. MAD MAPE (%) 15 17 21

Essentials of Business Analytics (MindTap Course List)

2nd Edition

ISBN:9781305627734

Author:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Publisher:Jeffrey D. Camm, James J. Cochran, Michael J. Fry, Jeffrey W. Ohlmann, David R. Anderson

Chapter7: Linear Regression

Section: Chapter Questions

Problem 6P: A marketing professor at Givens College is interested in the relationship between hours spent...

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The accompanying data provides the results of a survey of 27 employees in a tax division of a Fortune 100 company. a. Test the null hypothesis that the average number of years of service is the same for males and females. Assume that the population variances are unequal. b. Test the null hypothesis that the average years of undergraduate study is the same for males and females. Assume that the population variances are unequal. Click the icon to view the survey data. a. Is there sufficient evidence at the 0.01 level of significance that the average number of years of service is the same for males and females? Determine the null hypothesis, Ho, and the alternative hypothesis, H₁. Let females be population 1 and males be population 2. Ho P-P2 H₁: P₁ - H = 0 (Type integers or decimals. Do not round.) Compute the test statistic. - 3.65 (Round to two decimal places as needed.) Find the p-value for the test. (Round to three decimal places as needed.)
Brookbury Accounting completed 150 tax returns in the month of March. Its Effective Capacity is 170 tax returns and its Design Capacity is 175 tax returns. Calculate the Efficiency and Utilization for Brookbury Accounting. What do these two numbers tell you?
The accompanying data provides the results of a survey of 27 employees in a tax division of a Fortune 100 company. a. Test the null hypothesis that the average number of years of service is the same for males and females. Assume that the population variances are unequal. b. Test the null hypothesis that the average years of undergraduate study is the same for males and females. Assume that the population variances are unequal Click the icon to view the survey data. a. Is there sufficient evidence at the 0.01 level of significance that the average number of years of service is the same for males and females? Determine the null hypothesis, H. and the alternative hypothesis. H Let females be population 1 and males be population 2 Ho H1 H2 = 0 HH-H # 0 (Type integers or decimals. Do not round.) Compute the test statistic (Round to two decimal places as needed.) Survey data Gender Years of Service Years Undergraduate Study Gender Years of Service Years Undergraduate Study Female 18 4 Male 6…
A sociologist was hired by a large city hospital to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance (miles) between home and work for the employees. A sample of 10 employees was chosen, and the following data were collected. a. Develop a scatter chart for these data. Does a linear relationship appear reasonable? Explain. b. Use the data to develop an estimated regression equation that could be used to predict the number of days absent given the distance to work. What is the estimated regression model? c. What is the 99% confidence interval for the regression parameter 1? Based on this interval, what conclusion can you make about the hypotheses that the regression parameter 1 is equal to zero? d. What is the 99% confidence interval for the regression parameter 0? Based on this interval, what conclusion can you make about the hypotheses that the regression parameter 0 is equal to zero? e. How much of the variation in the sample values of number of days absent does the model you estimated in part (b) explain?
Jensen Tire Auto is deciding whether to purchase a maintenance contract for its new computer wheel alignment and balancing machine. Managers feel that maintenance expense should be related to usage, and they collected the following information on weekly usage (hours) and annual maintenance expense (in hundreds of dollars). a. Develop a scatter chart with weekly usage hours as the independent variable. What does the scatter chart indicate about the relationship between weekly usage and annual maintenance expense? b. Use the data to develop an estimated regression equation that could be used to predict the annual maintenance expense for a given number of hours of weekly usage. What is the estimated regression model? c. Test whether each of the regression parameters 0 and 1 is equal to zero at a 0.05 level of significance. What are the correct interpretations of the estimated regression parameters? Are these interpretations reasonable? d. How much of the variation in the sample values of annual maintenance expense does the model you estimated in part (b) explain? e. If the maintenance contract costs 3,000 per year, would you recommend purchasing it? Why or why not?
Financial information for BDS Enterprises for the year-ended December 31, 20xx, was gathered from an accounting intern, who has asked for your guidance on how to prepare an income statement format that will be distributed to management. Subtotals and totals are included in the information, but you will need to calculate the values. A. In the correct format, prepare the income statement using the following information: B. Calculate the profit margin, return on investment, and residual income. Assume an investment base of $100,000 and 6% cost of capital. C. Prepare a short response to accompany the income statement that explains why uncontrollable costs are included in the income statement.
The following time series shows the sales of a particular product over the past 12 months. Construct a time series plot. What type of pattern exists in the data? Use α = 0.3 to compute the exponential smoothing values for the time series. Use trial and error to find a value of the exponential smoothing coefficient α that results in a relatively small MSE.
Assume that you are one of the owners of Pinoy Corporation. Upon reviewing the project income statements as shown in Tables 7.3 and 7.4 you realized that salaries are understated because it only accounts for the 12 months of salary of each employee and does not include the 13th month pay, which is required by law. You also feel that cost of goods sold ought to be computed at 45% instead of 40% of salles revenues. Using a spreadsheet program, revise the projected income statements for the first year and the first three years of the business.
Consider again the example introduced in Section 7.5 of a credit card company that has a database of information provided by its customers when they apply for credit cards. An analyst has created a multiple regression model for which the dependent variable in the model is credit card charges accrued by a customer in the data set over the past year (y), and the independent variables are the customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3). Figure 7.23 provides Excel output for a multiple regression model estimated using a data set the company created. a. Estimate the corresponding simple linear regression with the customers annual household income as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers annual household income and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? b. Estimate the corresponding simple linear regression with the number of members in the customers household as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the number of members in the customers household and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? c. Estimate the corresponding simple linear regression with the customers number of years of posthigh school education as the independent variable and credit card charges accrued by a customer over the past year as the dependent variable. Interpret the estimated relationship between the customers number of years of posthigh school education and credit card charges accrued over the past year. How much variation in credit card charges accrued by a customer over the past year is explained by this simple linear regression model? d. Recall the multiple regression in Figure 7.23 with credit card charges accrued by a customer over the past year as the dependent variable and customers annual household income (x1), number of members of the household (x2), and number of years of post-high school education (x3) as the independent variables. Do the estimated slopes differ substantially from the corresponding slopes that were estimated using simple linear regression in parts (a), (b), and (c)? What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? e. Add the coefficients of determination for the simple linear regression in parts (a), (b), and (c), and compare the result to the coefficient of determination for the multiple regression model in Figure 7.23. What does this tell you about multicollinearity in the multiple regression model in Figure 7.23? f. Add age, a dummy variable for sex, and a dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as independent variables to the multiple regression model in Figure 7.23. Code the dummy variable for sex as 1 if the customer is female and 0 if male, and code the dummy variable for whether a customer has exceeded his or her credit limit in the past 12 months as 1 if the customer has exceeded his or her credit limit in the past 12 months and 0 otherwise. Do these variables substantially improve the fit of your model?
Oscar Optical Corporation provides a full line of designer eyewear to consumers. Oscar reported the following information for 2018 and 2017: (Click the icon to view the table.) Compute return on assets (ROA) for 2018 and 2017. Using the DuPont model, identify the components and state whether each improved or worsened from 2017 to 2018. Select the formula, then enter the amounts to compute ROA using the DuPont model for 2018 and 2017. (Round component ratios to four decimal places. Enter your final answers as a percentage rounded to the nearest tenth of a percent, X.X%.) 2018 2017 X X X || = = = Return on assets (ROA) % %
For several years, many utilities have employed regression analysis to forecast monthly utility usage by residential customers using weather forecasts, the number of holidays, the number of days in the month, and other factors. For example, the Connecticut Department of Public Utility Control (CDPUC) has determined that regression, properly used, can accurately predict natural gas usage. Most public gas utilities serving Connecticut have reported levels of accuracy from 4% to 10% using regression. One company, Dominion Natural Gas Company of Ohio, uses this approach not to forecast, but to explain to customers why their natural gas bills have gone up or down compared to the prior month and to the same month of the prior year. The bill shows total MCF (thousand cubic feet of natural gas) used by the customer for that month and why the total MCF usage has changed, based on three factors: 1. Change in temperature. Each degree increase in temperature causes an increase in the number of…
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