Identify and discuss the advantages and disadvantages of using existing data for secondary data analysis. Provide at least one example for each. In your opinion, do you think the advantages outweigh the disadvantages? Why or why not?
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Identify and discuss the advantages and disadvantages of using existing data for secondary data analysis. Provide at least one example for each. In your opinion, do you think the advantages outweigh the disadvantages? Why or why not?
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- 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?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?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.
- What are the most typical types of data trends to look for when doing a data analysis? It is necessary to present examples of each group.Explain what assumptions do qualitative forecasting systems make ?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.
