Explain the process of analyzing simultaneous changes in objective function coefficients
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Explain the process of analyzing simultaneous changes in objective function coefficients ?
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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?Suppose that a regional express delivery service company wants to estimate the cost of shipping a package (Y) as a function of cargo type, where cargo type includes the following possibilities: fragile, semifragile, and durable. Costs for 15 randomly chosen packages of approximately the same weight and same distance shipped, but of different cargo types, are provided in the file P13_16.xlsx. a. Estimate a regression equation using the given sample data, and interpret the estimated regression coefficients. b. According to the estimated regression equation, which cargo type is the most costly to ship? Which cargo type is the least costly to ship? c. How well does the estimated equation fit the given sample data? How might the fit be improved? d. Given the estimated regression equation, predict the cost of shipping a package with semifragile cargo.
- 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.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?Explain the kind of changes occur in the coefficient of a non variable while making changes in the orginal model ?
- Emenyi Company Limited is considering using Markov analysis to analyze brand switching between three different brands of its Detox medicated soap; Detox White, Detox Green and Detox Orange. The company’s marketing specialist conducted a survey and gathered data which she utilized to estimate the following transition matrix. The matrix shows the probability of moving between brands each month: From White Green OrangeWhite 0.78 0.11 0.11 Green 0.04 0.94 0.02 Orange 0.20 0.04 0.76 The current (month 1) market shares are 50%, 20% and 30% for Detox White, Detox Green and Detox Orange respectively. Required (i) What will be the expected market shares for each of the three brands after two months have elapsed (that is, in month 3)?(ii) What is the long-run prediction for the expected market share for each of the three brands?Can someone simply explain linear regression? Can linear regression be automatically calculated in SPSS?The materials handling manager of a manufacturing company is trying to forecast the cost of maintenance for the company's fleet of over-the-road tractors. The manager believes that the cost of maintaining the tractors increases with their age. The following data was collected: Age (years) 5.5 where Y = Yearly maintenance cost in dollars and X = Age in years. 5.5 5.5 5.0 5.0 5.0 6.0 6.0 6.5 Yearly Maintenance Cost (S) 1,319 1,749 1,733 1,195 1,423 1,381 1,590 2,222 1,687 Age (years) 5.0 1.5 1.5 Y = 7.0 7.0 2.0 2.0 2.0 Yearly Maintenance Cost ($) 1,894 863 882 a. Use POM for Windows' least squares-linear regression module to develop a relationship to forecast the yearly maintenance cost based on the age of a tractor. (Enter your responses rounded to three decimal places and include a minus sign if necessary.) 1,464 2,073 1,678 1,166 1,249
- The difference between a moving-average model and anexponential smoothing model is that __________.Explain three (3) of the general assumptions of a Regression (Least Squares) model.The classified department of a monthly magazine has used a combination of quantitative and qualitative methods to forecast sales of advertising space. Results over a 20-month period are as follows:Month Error1 −8 2 −2 3 4 4 7 5 9 6 5 7 0 8 −3 9 −9 10 −4 11 1 12 6 13 8 14 4 15 1 16 −2 17 −4 18 −8 19 −5 20 −1 a. Compute a tracking signal for months 11 through 20. Compute an initial value of MAD for month 11, and then update it for each month using exponential smoothing with α = .1. What can you conclude? Assume limits of ± 4.b. Using the first half of the data, construct a control chart with 2s limits. What can you conclude?c. Plot the last 10 errors on the control chart. Are the errors random? What is the implication of this?