. Compute seasonal relatives for this data using the SA method: Quarter 1 2 3 4 Year 1 2 6 2 5 Year 2 3 10 6 9 Year 3 7 18 8 15 Year 4 4 14 8 11
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Compute the seasonal relatives for this data using the SA method. Please show the work, not just the answer. See the screen shot for details.
Operation management is commonly performed in each manufacturing organization. It all deals with the management of the operating activities that are performed to achieve the set targets. Operations management is managing the practices of the business so that efficiency and effectiveness can be bought within the organization. It is related to the effective management of materials and labor with goods and services.
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- Under what conditions might a firm use multiple forecasting methods?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 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_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?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.
- 3. Quarterly sales of a car dealer exhibit an almost constant mean over time, but sales fluctuate depending on the quarter of the year (exhibits seasonality). Quarter Q1 - Spring Q2 - Summer Q3 - Fall Q4 - Winter Mean Year 1 32 74 53 22 45.25 Year 2 30 71 63 15 44.75 Year 3 35 83 45 19 45.5 (a) Using three years' worth of quarterly data that is provided, develop a set of seasonal factors that could be used to forecast car sales (b) Forecast car sales in each of the four quarters (Q1-Q4) of the following year (Year 4), using these seasonal factors.Consider the following quarterly demand level for electricity (in 1000 megawatts) in Mankweng from 2018 to 2022: 2018 2019 2020 2021 2022 Jan – March 21 35 49 60 10 - Apr – June 42 54 55 64 05 July – Sept Oct – Dec 60 91 95 99 12 14 74 80 1. Find the least squares trend line for electricity demand using Exx=0 method. 2. Find the seasonal index for each quarter. 3. Find adjusted seasonal index. 4. Find the De-seasonalized values for Quarter 1, 2,3 and 4 for the year 2023.From the following time series data of sale project the sales for the next three years. Year 2001 2002 2003 2004 2005 2006 2007 Sales (*000 units) 80 90 92 83 94 99 92
- Develop Linear Regression Model. Forecast sales for 8, 14 and 17 years of education. Sales(1000s) Years of Education 24 10 18 9 20 11 22 6 26 13 23 16 21 182. What is the Mean Absolute Deviation (MAD)? 3. Use the following set of data to calculate the Mean Absolute Deviation (MAD) for the following set of data. Month Actual (At) Forecast (Ft) Forecast Error Absolute (Deviation) Forecast Eror January February 45 45 42 50 March 34 45 April May 48 40 38 45 MAD =The following data are for calculator sales in units at an electronics store over the past nine weeks: Week 1 2 3 4 5 Obtain the error measures. (Enter your responses rounded to two decimal places.) CFE MSE Sales Find the coefficient of determination (²). The coefficient of determination r² = 0. (Enter your response rounded to two decimal places.) 44459 46 51 58 Use trend projection with regression to forecast sales for weeks 10-13. What are the error measures (CFE, MSE, 6, MAD, and MAPE) for this forecasting procedure? How about r²? Obtain the trend projection with regression forecast for weeks 10-13. (Enter your responses rounded to two decimal places.) Forecast, Ft Period 10 11 12 13 Week 6 69809 7 Sales 54 63 53 61 MAD U MAPE %
