Month Jan Feb Mar Apr May Jun Sales 51 55 54 57 50 68 66 59 Jul Aug Sep Oct Nov Dec 67 69 75 77 PepsiCo uses potatoes for most of its products. The CEO has requested that you outline the forecasted volumes in tonnes using trend projection for the first FIVE months of 2025 (January, February, March, April, May and June) using the data for 2024 provided. The following data for 2024 is provided for potatoes in tonnes. Make use of relevant calculations.

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?

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 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_02.xlsx contains five years of monthly data on sales (number of units sold) for a particular company. The company suspects that except for random noise, its sales are growing by a constant percentage each month and will continue to do so for at least the near future. a. Explain briefly whether the plot of the series visually supports the companys suspicion. b. By what percentage are sales increasing each month? c. What is the MAPE for the forecast model in part b? In words, what does it measure? Considering its magnitude, does the model seem to be doing a good job? d. In words, how does the model make forecasts for future months? Specifically, given the forecast value for the last month in the data set, what simple arithmetic could you use to obtain forecasts for the next few months?

ABC Inc. sells patio sets. Monthly sales for a seven-month period were as follows:    Month Sales (000) Unites Feb 19 Mar 18 Apr 15 May 20 Jun 18 Jul 22 Aug 20 Forecast September sales volume using a five-month moving average approach.

Given the following historical data, what is the simple three-period moving average forecast for period 6?  Period Value 1 73 2 68 3 65 4 72 5 67

Please consider the following information and make forecasts for January, February, March, and April of Year 2. Use second-order polynomial regression to make forecasts. Adjusted seasonal index Actual sales Deseasonalized sales 836 825 Year 1 Year 2 January February March April May June July August September October November December January February March April 0.72 0.75 1:22 1.48 1 1.23 0.7 0.78 0.92 1.13 1.28 1.33 0.72 0.75 1.22 1.48 1220 1319 1020 1184 685 641 875 1008 1068 1141

Favors Distribution Company purchases small imported trinkets in bulk, packages     them, and sells them to retail stores. They are conducting an inventory control study of all their     items. The following data for the year 2006 are for one such item, which is not seasonal.                     Month Jan Feb Mar Apr May Jun Jul Aug Sales 11 19 17 14 15 16 20 15 Use trend projection to estimate the relationship between time and sales (state the trend line equation in this problem).   Calculate forecasts for February 2007, April 2007, and February 2008 (SHOW CALCULATIONS)