#20 ### Multiple Linear Regression AnalysisThis table presents the results of multiple linear regression analyses conducted to predict the variable "CITY" using various predictor combinations.#### Table Explanation1. **Predictor (x) Variables**: Lists the variables used in each regression model. - **WT**: Weight - **DISP**: Displacement - **HWY**: Highway2. **P-Value**: All models have a P-value of 0.000, indicating that the relationships between the predictors and the response variable are statistically significant.3. **R² (Coefficient of Determination)**: Indicates the proportion of variance in the dependent variable explained by the model. - Values range from 0.659 to 0.943.4. **Adjusted R²**: Adjusts the R² value for the number of predictors in the model, providing a more accurate measure when comparing models with different numbers of predictors. - Values range from 0.641 to 0.937, slightly lower than R² due to the adjustment.5. **Regression Equation**: Each row provides the equation derived from regression analysis. They represent the model to predict "CITY" based on the given predictors.#### Regression Equations- **WT/DISP/HWY**: \[ \text{CITY} = 6.82 - 0.00126 \cdot \text{WT} - 0.259 \cdot \text{DISP} + 0.657 \cdot \text{HWY} \]- **WT/DISP**: \[ \text{CITY} = 37.6 - 0.00157 \cdot \text{WT} - 1.33 \cdot \text{DISP} \]- **WT/HWY**: \[ \text{CITY} = 6.72 - 0.00157 \cdot \text{WT} + 0.674 \cdot \text{HWY} \]- **DISP/HWY**: \[ \text{CITY} = 1.84 - 0.625 \cdot \text{DISP} + 0.705 \cdot \text{HWY} \]- **WT**: \[ \text{CITY} = The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why?\[ \text{

Name: #20 ### Multiple Linear Regression AnalysisThis table presents the res
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Description: #20 ### Multiple Linear Regression AnalysisThis table presents the results of multiple linear regression analyses conducted to predict the variable "CITY" using various predictor combinations.#### Table Explanation1. **Predictor (x) Variables**: List

The different regression equations for the dependent variable CITY(fuel consumption) are given for…

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The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi'gal). The predictor (x) variables are WT (weight in pounds). DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? E Click the icon to view the table of regression equations. The best variable is because it has the best combination of V P-value,. and adjusted R?. (Type integers or decimals. Do not round.)

The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi'gal). The predictor (x) variables are WT (weight in pounds). DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why? E Click the icon to view the table of regression equations. The best variable is because it has the best combination of V P-value,. and adjusted R?. (Type integers or decimals. Do not round.)

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

#20

### Multiple Linear Regression Analysis

This table presents the results of multiple linear regression analyses conducted to predict the variable "CITY" using various predictor combinations.

#### Table Explanation

1. **Predictor (x) Variables**: Lists the variables used in each regression model.
- **WT**: Weight
- **DISP**: Displacement
- **HWY**: Highway

2. **P-Value**: All models have a P-value of 0.000, indicating that the relationships between the predictors and the response variable are statistically significant.

3. **R² (Coefficient of Determination)**: Indicates the proportion of variance in the dependent variable explained by the model.
- Values range from 0.659 to 0.943.

4. **Adjusted R²**: Adjusts the R² value for the number of predictors in the model, providing a more accurate measure when comparing models with different numbers of predictors.
- Values range from 0.641 to 0.937, slightly lower than R² due to the adjustment.

5. **Regression Equation**: Each row provides the equation derived from regression analysis. They represent the model to predict "CITY" based on the given predictors.

#### Regression Equations

- **WT/DISP/HWY**:
\[
\text{CITY} = 6.82 - 0.00126 \cdot \text{WT} - 0.259 \cdot \text{DISP} + 0.657 \cdot \text{HWY}
\]

- **WT/DISP**:
\[
\text{CITY} = 37.6 - 0.00157 \cdot \text{WT} - 1.33 \cdot \text{DISP}
\]

- **WT/HWY**:
\[
\text{CITY} = 6.72 - 0.00157 \cdot \text{WT} + 0.674 \cdot \text{HWY}
\]

- **DISP/HWY**:
\[
\text{CITY} = 1.84 - 0.625 \cdot \text{DISP} + 0.705 \cdot \text{HWY}
\]

- **WT**:
\[
\text{CITY} =

The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). If only one predictor (x) variable is used to predict the city fuel consumption, which single variable is best? Why?

\[ \text{

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