Give quality answerNo handwritten solutionAnswer all partsYou get up vote for sure. 1. Sales data for two years are as follows. Data are aggregated with two months of sales (in1,000 units) in each "period."Year 1PeriodYear 2Sales Period126152165184167123January-FebruaryMarch-AprilMay-JuneJuly-AugustSeptember OctoberNovember-Decembera) Plot the data.b) Fit a linear regression model to all the sales data.January FebruaryMarch-AprilMay-JuneJuly-AugustSeptember OctoberNovember-DecemberSales172151204238189149c) In addition to the regression model, determine multiplicative seasonal index factors. Afull cycle is assumed to be a full year.d) Using the results from parts b) and c), prepare a forecast for the next year.

Year Period (x) Sales (y)Year1112621523165418451676123Year 2717281519204102381118912149

1. Sales data for two years are as follows. Data are aggregated with two months of sales (in 1,000 units) in each "period." Year 1 Period January February March-April May-June July-August September October November-December Year 2 Sales Period 126 152 165 184 167 123 January February March-April May-June July-August September October November-December Sales 172 151 204 238 189 149 a) Plot the data. b) Fit a linear regression model to all the sales data. c) In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year. d) Using the results from parts b) and c), prepare a forecast for the next year.

1. Sales data for two years are as follows. Data are aggregated with two months of sales (in 1,000 units) in each "period." Year 1 Period January February March-April May-June July-August September October November-December Year 2 Sales Period 126 152 165 184 167 123 January February March-April May-June July-August September October November-December Sales 172 151 204 238 189 149 a) Plot the data. b) Fit a linear regression model to all the sales data. c) In addition to the regression model, determine multiplicative seasonal index factors. A full cycle is assumed to be a full year. d) Using the results from parts b) and c), prepare a forecast for the next year.

Practical Management Science

6th Edition

ISBN:9781337406659

Author:WINSTON, Wayne L.

Publisher:WINSTON, Wayne L.

Chapter13: Regression And Forecasting Models

Section: Chapter Questions

Problem 33P: Management of a home appliance store would like to understand the growth pattern of the monthly...

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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.
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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.
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