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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 61 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 Month January February March April May June July August Number of Oil Changes 38 55 56 60 58 61 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)

Practical Management Science
Practical Management Science
6th Edition
ISBN:9781337406659
Author:WINSTON, Wayne L.
Publisher:WINSTON, Wayne L.
Chapter2: Introduction To Spreadsheet Modeling
Section: Chapter Questions
Problem 20P: Julie James is opening a lemonade stand. She believes the fixed cost per week of running the stand...
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### Demand for Oil Changes at Garcia's Garage #### Monthly Data for Oil Change Demand | Month | Number of Oil Changes | |-----------|------------------------| | January | 38 | | February | 55 | | March | 66 | | April | 60 | | May | 58 | | June | 61 | | July | 70 | | August | 52 | #### Analysis Task **Objective**: Use simple linear regression analysis to develop a forecasting model for monthly demand. - **Dependent Variable, \( Y \)**: Monthly demand (number of oil changes). - **Independent Variable, \( X \)**: Time period, expressed in months (e.g., January = 1, February = 2, ... August = 8). **Instructions**: 1. For January, let \( X = 1 \). 2. For February, let \( X = 2 \). 3. Continue incrementing \( X \) by 1 for each subsequent month. The forecasting model is given by the equation: \[ Y = b_0 + b_1 \cdot X \] *(Enter your responses rounded to two decimal places.)* ### Explanation of the Table The provided table displays the number of oil changes recorded for each month from January to August. This data will serve as the basis for developing a predictive model using simple linear regression, which will allow us to forecast future demand based on the trend observed in the historical data.
Transcribed Image Text:### Demand for Oil Changes at Garcia's Garage #### Monthly Data for Oil Change Demand | Month | Number of Oil Changes | |-----------|------------------------| | January | 38 | | February | 55 | | March | 66 | | April | 60 | | May | 58 | | June | 61 | | July | 70 | | August | 52 | #### Analysis Task **Objective**: Use simple linear regression analysis to develop a forecasting model for monthly demand. - **Dependent Variable, \( Y \)**: Monthly demand (number of oil changes). - **Independent Variable, \( X \)**: Time period, expressed in months (e.g., January = 1, February = 2, ... August = 8). **Instructions**: 1. For January, let \( X = 1 \). 2. For February, let \( X = 2 \). 3. Continue incrementing \( X \) by 1 for each subsequent month. The forecasting model is given by the equation: \[ Y = b_0 + b_1 \cdot X \] *(Enter your responses rounded to two decimal places.)* ### Explanation of the Table The provided table displays the number of oil changes recorded for each month from January to August. This data will serve as the basis for developing a predictive model using simple linear regression, which will allow us to forecast future demand based on the trend observed in the historical data.
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