Operations Management: Processes And Supply Chains (12th Global Edition) - Does Not Include Mylab Operations Management
Operations Management: Processes And Supply Chains (12th Global Edition) - Does Not Include Mylab Operations Management
12th Edition
ISBN: 9780134890357
Author: Lee J. Krajewski, Manoj K. Malhotra
Publisher: Pearson Global Edition
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Chapter 8, Problem 1P

Demand for oil changes at Garcia’s Garage has been as follows:

Chapter 8, Problem 1P, Demand for oil changes at Garcia’s Garage has been as follows: Use simple linear regression

  1. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For January, let X = 1 ; for February, let X = 2 ; and so on.
  2. Use time model to forecast demand for September, October, and November. Here, X = 9 , 10 , and 11, respectively.

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3) Seasonality: The following data represent dinner sales at a busy restaurant. Use linear regression to predict sales for each day of week 5 and the total sales for week 5. Estimate the percentage of weekly sales that occur over the weekend (include Friday, Saturday, and Sunday). Finally, determine which days of the week are increasing and decreasing in sales, using the slopes of the LR lines. Week Mon Wed Fri Sat Sun Tue 177 170 Thu 190 Total 270 152 180 321 386 166 218 203 402 427 167 333 357 229 3 158 170 170 205 163 173 158 225 349 433 212 a) Graph the seasonal data and attach the graph to this page. b) Determine the slope for each day of the week. Mon Tue Wed Thu Fri Sat Sun Total Slope c) Estimate the percentage of weekend sales in week 5: d) For which day are sales increasing the fastest? e) For which day are sales decreasing the fastest?
Demand for oil changes at Garcia's Garage has been as follows: Month January February March April May June July August Number of Oil Changes 38 55 56 60 58 01 70 52 a. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For January, let X=1; for February, let X 2; and so on. The forecasting model is given by the equation Y=X (Enter your responses rounded to two decimal places)
Demand for oil changes at Garcia's Garage has been as follows: Number of Oil Changes January 44 February 49 March 66 IT April 59 May 53 June 58 59 63 Month July August a. Use simple linear regression analysis to develop a forecasting model for monthly demand. In this application, the dependent variable, Y, is monthly demand and the independent variable, X, is the month. For Janu The forecasting model is given by the equation Y=+x. (Enter your responses rounded to two decimal places) J Show Transcribed Text >. Use the model to forecast demand for September, October, and November. Here, X=9, 10, and 11, respectively. (Enter your responses rounded to two decimal places.) Forecast for the number of Oil Changes Show Transcribed Text Month September October November can you do all the parts please corrrect

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Operations Management: Processes And Supply Chains (12th Global Edition) - Does Not Include Mylab Operations Management

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