How can startbucks company use multiple regression? Please provide an example and identify dependent and independent variables.
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How can startbucks company use multiple regression? Please provide an example and identify dependent and independent variables.
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- 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?The 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?Do 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.
- A small computer chip manufacturer wants to forecast monthly ozperating costs as a function of the number of units produced during a month. The company has collected the 16 months of data in the file P13_34.xlsx. a. Determine an equation that can be used to predict monthly production costs from units produced. Are there any outliers? b. How could the regression line obtained in part a be used to determine whether the company was efficient or inefficient during any particular month?what is the standardized regression? what do the standardized regression weights or coefficients tell us about the ability of the predictors to predict the dependent variable?. What are the advantages and disadvantages of Regression Model, Econometric Model, Driving Indicator Models?
- What is the difference between linear and multiple regression?A study to determine the correlation between bankdeposits and consumer price indices in Birmingham, Alabama,revealed the following (which was based on n = 5 years of da ta):• LX = 15• Lx2 = 55• Lxy = 70• Ly = 20• L/ = 130a) What is the equation of the least-squares regression line?b) Find the coefficient of correlation. What does it imply to you?c) What is the standard error of the estimate?The following data were collected during a study of consumer buying patterns:Observation x y Observation x y 1 19 72 8 20 77 2 21 81 9 17 73 3 43 79 10 13 77 4 28 81 11 20 85 5 56 98 12 28 93 6 49 96 13 34 94 7 32 82 14a. Obtain a linear regression line for the data.b. What percentage of the variation is explained by the regression line?c. Use the equation determined in part b to predict the expected value of y for x = 49.
