A company that sells cars wants to determine the factors that affect the price of its cars. The company collects data on the price of the cars, as well as the following factors:
- The make of the car
- The model of the car
- The year of the car
- The mileage of the car
- The condition of the car
The company uses multiple linear regression to analyze the data. The results of the analysis show that the following factors are significant predictors of the price of the cars:
- The make of the car (coefficient = 0.5)
- The model of the car (coefficient = 0.3)
- The year of the car (coefficient = -0.1)
- The mileage of the car (coefficient = -0.2)
- The condition of the car (coefficient = 0.1)
The company can use this information to set prices for its cars and to target its marketing efforts.
Data: The following data is provided for the case study:
| Make | Model | Year | Mileage | Condition | Price |
|---|---|---|---|---|---|
| Toyota | Camry | 2015 | 30,000 | Good | $20,000 |
| Honda | Accord | 2016 | 20,000 | Excellent | $22,000 |
| Nissan | Altima | 2017 | 10,000 | Fair | $18,000 |
| Mazda | Mazda3 | 2018 | 5,000 | New | $24,000 |
| Ford | Fusion | 2019 | 15,000 | Good | $21,000 |
| Chevrolet | Malibu | 2020 | 25,000 | Fair | $19,000 |
Analysis: The multiple linear regression analysis was performed using the following steps:
- The data was loaded into a statistical software package.
- The independent variables were entered into the model.
- The dependent variable was entered into the model.
- The model was fit to the data.
- The coefficients of the model were estimated.
- The significance of the model was evaluated.
- The model was used to predict the price of the cars.
The results of the analysis show that the model is significant (p < 0.05). This means that the independent variables can be used to predict the price of the cars. The coefficients of the model show that the make of the car has the strongest effect on the price of the cars, followed by the model of the car, the year of the car, the mileage of the car, and the condition of the car.
The model can be used to predict the price of the cars. For example, if a company wants to sell a 2018 Mazda3 with 5,000 miles on it in good condition, the model would predict that the car should be priced at $24,000.
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