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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 file P13_42.xlsx contains monthly data on consumer revolving credit (in millions of dollars) through credit unions. a. Use these data to forecast consumer revolving credit through credit unions for the next 12 months. Do it in two ways. First, fit an exponential trend to the series. Second, use Holts method with optimized smoothing constants. b. Which of these two methods appears to provide the best forecasts? Answer by comparing their MAPE values.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 file P13_26.xlsx contains the monthly number of airline tickets sold by the CareFree Travel Agency. a. Create a time series chart of the data. Based on what you see, which of the exponential smoothing models do you think will provide the best forecasting model? Why? b. Use simple exponential smoothing to forecast these data, using a smoothing constant of 0.1. c. Repeat part b, but search for the smoothing constant that makes RMSE as small as possible. Does it make much of an improvement over the model in part b?The file P13_22.xlsx contains total monthly U.S. retail sales data. While holding out the final six months of observations for validation purposes, use the method of moving averages with a carefully chosen span to forecast U.S. retail sales in the next year. Comment on the performance of your model. What makes this time series more challenging to forecast?i. Discuss the following methods of data collection methods. Be sure to include both merits and demerits a. Observation method b. Schedule Method ii. Distinguish between cluster sampling and stratified sampling. Be sure to include the merits and demerits of each of them
- 1.) Use the following dummy variables to develop an estimated regression equation to account for seasonal effects only in the data. Qtr1 = 1 if Quarter 1, 0otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise, Qtr3 = 1 if Quarter 3, 0otherwise. Based only on the seasonal effects in the data, compute estimates of quarterly sales for year 6.a. Report the estimate of sales for Year 6 Quarter 1. (Enter a whole value.)b. Report the estimate of sales for Year 6 Quarter 4. (Enter a whole value.)2.) Let Period t = 1 refer to the observation in quarter 1 of year 1; Period t = 2refer to the observation in quarter 2 or year 1; … and Period t = 20 refer to the observation in quarter 4 of year 5. Using the dummy variables defined in part (b) and Period (t), develop an estimated regression equation to account for seasonal effects and any linear trend in the time series. Based upon the seasonal effects in the data and linear trend, compute the estimates of quarterly sales for year 6.a. Report the estimate…Enter T for true or F for false: 1. Moving averages emphasize the variability in the data over time. 2. Three-period moving averages are called centered moving averages and the moving totals are positioned on the same tabular line as the middle time period. 3. It is never appropriate to select an even number of time periods to use in a moving average because the moving totals don’t align with a middle time period.16- Statistical forecasting models have the following weaknesses, except __________. a. Can be costly and time consuming to model “soft” information b. Can process large amounts of data c. Slow to react to changing environments d. Only as good as the model formulation and the data it is based upon
- What is producivity analysis? What is prescriptive data analytic model? How are the two related or how are they different? Give example of each.b) Caitlyn has to determine how many of the 120 employees in her work place has comprehensive car insurance. She wants to obtain a sample of 40 employees. She selections employees from each department in proportion to the number of individuals the departments Define the term population with relation to the question above. What type of sampling method did Caitlyn use? iii. List two advantages of the sampling method stated in ii). Attached are the number of individuals in each department How many employees from the Marketing department should Caitlyn select to be part of the sample? Another employee suggested that Caitlyn should group employees by the type of car they own. State a reason why grouping the employees in this way will NOT be appropriate to select this sample using the sampling method in ii.Discuss the strength and weaknesses (pros/cons) of the different data collection methods