Concept explainers
The number of auto accidents in Athens, Ohio, is related to the regional number of registered automobiles in thousands (X1), alcoholic beverage sales in $10,000s (X2), and rainfall in inches (X3). Furthermore, the regression formula has been calculated as:
where
Y = number of automobile accidents
a = 7.5
b1 = 3.5
b2 = 4.5
b3 = 2.5
Calculate the expected number of automobile accidents under conditions a, b, and c:
Want to see the full answer?
Check out a sample textbook solutionChapter 4 Solutions
Pearson eText Principles of Operations Management: Sustainability and Supply Chain Management -- Instant Access (Pearson+)
- The owner of a restaurant in Bloomington, Indiana, has recorded sales data for the past 19 years. He has also recorded data on potentially relevant variables. The data are listed in the file P13_17.xlsx. a. Estimate a simple regression equation involving annual sales (the dependent variable) and the size of the population residing within 10 miles of the restaurant (the explanatory variable). Interpret R-square for this regression. b. Add another explanatory variableannual advertising expendituresto the regression equation in part a. Estimate and interpret this expanded equation. How does the R-square value for this multiple regression equation compare to that of the simple regression equation estimated in part a? Explain any difference between the two R-square values. How can you use the adjusted R-squares for a comparison of the two equations? c. Add one more explanatory variable to the multiple regression equation estimated in part b. In particular, estimate and interpret the coefficients of a multiple regression equation that includes the previous years advertising expenditure. How does the inclusion of this third explanatory variable affect the R-square, compared to the corresponding values for the equation of part b? Explain any changes in this value. What does the adjusted R-square for the new equation tell you?arrow_forwardThe Baker Company wants to develop a budget to predict how overhead costs vary with activity levels. Management is trying to decide whether direct labor hours (DLH) or units produced is the better measure of activity for the firm. Monthly data for the preceding 24 months appear in the file P13_40.xlsx. Use regression analysis to determine which measure, DLH or Units (or both), should be used for the budget. How would the regression equation be used to obtain the budget for the firms overhead costs?arrow_forwardDo the sales prices of houses in a given community vary systematically with their sizes (as measured in square feet)? Answer this question by estimating a simple regression equation where the sales price of the house is the dependent variable, and the size of the house is the explanatory variable. Use the sample data given in P13_06.xlsx. Interpret your estimated equation, the associated R-square value, and the associated standard error of estimate.arrow_forward
- The management of a technology company is trying to determine the variable that best explains the variation of employee salaries using a sample of 52 full-time employees; see the file P13_08.xlsx. Estimate simple linear regression equations to identify which of the following has the strongest linear relationship with annual salary: the employees gender, age, number of years of relevant work experience prior to employment at the company, number of years of employment at the company, or number of years of post secondary education. Provide support for your conclusion.arrow_forwarddata table below shows the number of computers sold at the Best Buy Store in a week, based on online ads. Online Ad Computers Sold 2 25 1 10 4 30 1 10 2 25 sigma)x=10 sigma)y=100 draw the graph of the regression equation, showing the slope and y intercepts?arrow_forwardThe number of internal disk drives (in million) made at a plant in Taiwan during the past 5 years follows: Year Disk Drives 1 142 2 156 3 184 4 204 5 210 a) Using simple linear regression the forecast for the number of disk drives to be made next year= 234.4 disk drives b) The mean squared error (mse) when using simple linear regression=[___] drives^2 (round your response to one decimal place)arrow_forward
- The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef Constant 55.28 SE Coef 1.04 T-Value P-Value Shoe Size 0.105 Gender 0.268 0.12 0.489 53.1 0.875 0.000 0.000 0.548 0.000 (a) The dependent variable in this regression is which of the following? height gender shoe size constant (b) What is the regression coefficient of shoe size? (c) What is the regression coefficient of gender?arrow_forwardYou work as a sales operations analyst in a company that makes 3D printers. Your manager has asked you to determine if a salesperson's sales volume (in terms of the number of 3D printers they sell in a year) depends on the number of client calls they make. After analyzing past data and creating a linear regression model, you've found the following relationship: No. of printers sold = 18.47 + 1.13 times the number of client calls. 1. Based on this, how many client calls will a salesperson need to make to sell 245 printers next year? a. 200 (rounds to) b. 215 (rounds to) c. 230 (rounds to) d. 240 (rounds to)arrow_forwardH The following gives the number of accidents that occurred on Florida State Highway 101 during the last 4 months: Month Jan Number of Accidents 25 Feb 48 Mar Apr 64 100 Using the least-squares regression method, the trend equation for forecasting is (round your responses to two decimal places): ŷ=+xarrow_forward
- PLEASE HELP ME WITH THIS EXERCISE SO THAT I CAN LEARN HOW TO SLOVEarrow_forwardRestaurant Student Population Quarterly Sales 1 2 58 2 6 105 3 8 88 4 8 118 5 12 117 6 16 137 For the data above, what would be the predicted quarterly sales for a restaurant with a student population of 15 (using linear regression)?arrow_forwardGiven the data below, what is the simple linear regression model that can be used to predict sales in future weeks? Week 1 2 3 4 5 Sales 150 157 162 166 177arrow_forward
- Practical Management ScienceOperations ManagementISBN:9781337406659Author:WINSTON, Wayne L.Publisher:Cengage,