Write the multiple regression equation for miles per gallon as the response variable. How might the car rental company use this model?
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
Write the multiple regression equation for miles per gallon as the response variable. How might the car rental company use this model?
![### OLS Regression Results
**Dependent Variable:**
- mpg (miles per gallon)
**Model:**
- OLS (Ordinary Least Squares)
**Method:**
- Least Squares
**Date:**
- Wed, 07 Oct 2020
**Time:**
- 11:19:54
**Number of Observations:**
- 30
**Degrees of Freedom Residuals:**
- 27
**Degrees of Freedom Model:**
- 2
**Covariance Type:**
- nonrobust
**Results Summary:**
- **R-squared:** 0.821
- **Adjusted R-squared:** 0.808
- **F-statistic:** 61.98
- **Prob (F-statistic):** 8.10e-11
- **Log-Likelihood:** -70.463
- **AIC (Akaike Information Criterion):** 146.9
- **BIC (Bayesian Information Criterion):** 151.1
**Coefficients:**
| Predictor | Coef. | Std Err. | t | P>|t| | [0.025 | 0.975] |
|-----------|--------|----------|--------|------|----------|--------|
| Intercept | 37.1838| 1.650 | 22.540 | 0.000| 33.799 | 40.569 |
| wt | -3.8810| 0.658 | -5.902 | 0.000| -5.230 | -2.532 |
| hp | -0.0309| 0.010 | -3.201 | 0.003| -0.051 | -0.011 |
**Diagnostic Tests:**
- **Omnibus:** 4.159
- **Durbin-Watson:** 2.606
- **Prob(Omnibus):** 0.125
- **Jarque-Bera (JB):** 3.100
- **Prob(JB):** 0.212
- **Skew:** 0.784
- **Kurtosis:** 3.147
- **Condition Number:** 561
**Warnings:**
1. Standard Errors assume that the covariance matrix of the errors is correctly specified.
### Explanation of Coefficients:](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fe7c098e8-a055-46a5-964b-ab0cc684192c%2Fa45ec5a8-4b17-4a49-8723-2089402cc4e5%2Fkbzy4z_processed.png&w=3840&q=75)
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