Practical Management Science
6th Edition
ISBN: 9781337406659
Author: WINSTON, Wayne L.
Publisher: Cengage,
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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.](https://content.bartleby.com/qna-images/question/39b5b2cb-93ff-4275-903c-3e0830d65c0b/a9e2dfc6-7738-4089-872b-32c62e01f0b3/80c3rgp_processed.jpeg)
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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