A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week. To forecast the demand for Sunday for Week 5, do you need to “deseasonalize” the demands for Weeks 1-4 and then “re-seasonalize” them. Why?
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A restaurant has tracked the number of meals served at lunch over the last four weeks. The data shows little in terms of trends, but does display substantial variation by day of the week.
- To
forecast the demand for Sunday for Week 5, do you need to “deseasonalize” the demands for Weeks 1-4 and then “re-seasonalize” them. Why?
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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 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?
- 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.Management of a home appliance store wants to understand the growth pattern of the monthly sales of a new technology device over the past two years. The managers have recorded the relevant data in the file P13_05.xlsx. Have the sales of this device been growing linearly over the past 24 months? By examining the results of a linear trend line, explain why or why not.
- The file P13_29.xlsx contains monthly time series data for total U.S. retail sales of building materials (which includes retail sales of building materials, hardware and garden supply stores, and mobile home dealers). a. Is seasonality present in these data? If so, characterize the seasonality pattern. b. Use Winters method to forecast this series with smoothing constants = = 0.1 and = 0.3. Does the forecast series seem to track the seasonal pattern well? What are your forecasts for the next 12 months?The following table shows the past two years of quarterly sales information. Assume that there are both trend and seasonal factors and that the seasonal cycle is one year. QUARTER SALES 1 215 2 240 3 205 4 190 5 160 6 195 7 150 8 140 Use regression and seasonal indexes to forecast quarterly sales for the next year. Note: Do not round intermediate calculations. Round your answers to 1 decimal place.Amazon finds that sales in July are much greater relative to other months because of the Amazon Prime days in that month. Assuming that an exponential smoothing model is used for forecasting sales, what should the company best do to make the forecast for the next month (Aug) more reflective of reality? Please pick from the provided options. F t+1 = αDt + (1-α) Ft increase value of alpha decrease value of alpha stay with the same alpha decrease the expected forecast for the current month (Ft) On hearing our forecast, our clients probe - how sure are we about the forecast? Which measure of errror (among those stated below) should we best use to respond? a. Root MSE (RMSE) b. Mean absolute percentage error (MAPE) c. Mean forecast error (Avg. error) d. Cumulative forecast error (CFE)
- ThenumberofToyotaCorollasproducedintheCambridge,Ontario,plantduringeach month of January 2008 to December 2009 period was as follows Assume that the cars are sold in the same month they are produced. Identify an appropriate forecasting technique, briefly state the reason(s) you chose it, and forecast Corolla demand in January 2010.Sales of Volkswagens have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Year Sales 1 460 2 502 3 518 4 575 5 580 Part 2 a) Forecasted sales for year 6 using the trend projection (linear regression) method are enter your response here sales (round your response to one decimal place).Sales of Volkswagen's popular Beetle have grown steadily at auto dealerships in Nevada during the past 5 years (see table below). Year Sales 1 450 2 502 3 520 4 570 5 575 a) Forecasted sales for year 6 using the trend projection (linear regression) method aresales (round your response to one decimal place). b) The MAD for a linear regression forecast is nothing sales (round your response to one decimal place).