Demand for oil changes at Garcia's Garage has been as follows: IT Month Number of Oil Changes January February March 39 41 66 April 56 May 67 June 60 July August 66 60 a. Use simple linear regression analysis X= 2; and so on. 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 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: IT Month Number of Oil Changes January February March 39 41 66 April 56 May 67 June 60 July August 66 60 a. Use simple linear regression analysis X= 2; and so on. 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 The forecasting model is given by the equation Y = +x. (Enter your responses rounded to two decimal places.)
Practical Management Science
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ISBN:9781337406659
Author:WINSTON, Wayne L.
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![### Demand for Oil Changes at Garcia's Garage
The table below illustrates the monthly demand for oil changes at Garcia's Garage:
| **Month** | **Number of Oil Changes** |
|-------------|---------------------------|
| January | 39 |
| February | 41 |
| March | 66 |
| April | 56 |
| May | 67 |
| June | 60 |
| July | 66 |
| August | 60 |
### Analysis Task
**Objective**: Use simple linear regression analysis to create a forecasting model for monthly demand.
**Definitions**:
- **Dependent Variable (Y)**: Monthly demand for oil changes.
- **Independent Variable (X)**: Month. Assign numerical values starting with January as X = 1, February as X = 2, and so on.
**Forecasting Model Equation**:
\[ Y = \text{(Intercept)} + \text{(Slope)} \times X \]
*Enter your responses rounded to two decimal places.*
This model will help predict the number of oil changes expected in a future month based on historical data trends.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F36a81628-4e73-47c6-859d-2f57e4b1f42c%2Fcd16d727-34c2-47f7-afad-35904ed81055%2F1uresf_processed.png&w=3840&q=75)
Transcribed Image Text:### Demand for Oil Changes at Garcia's Garage
The table below illustrates the monthly demand for oil changes at Garcia's Garage:
| **Month** | **Number of Oil Changes** |
|-------------|---------------------------|
| January | 39 |
| February | 41 |
| March | 66 |
| April | 56 |
| May | 67 |
| June | 60 |
| July | 66 |
| August | 60 |
### Analysis Task
**Objective**: Use simple linear regression analysis to create a forecasting model for monthly demand.
**Definitions**:
- **Dependent Variable (Y)**: Monthly demand for oil changes.
- **Independent Variable (X)**: Month. Assign numerical values starting with January as X = 1, February as X = 2, and so on.
**Forecasting Model Equation**:
\[ Y = \text{(Intercept)} + \text{(Slope)} \times X \]
*Enter your responses rounded to two decimal places.*
This model will help predict the number of oil changes expected in a future month based on historical data trends.
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