Help **Multiple Regression Output Summary**---For this question, use the following multiple regression output (which may differ from the output in other questions, even though the variables are the same).**Summary Output:****Regression Statistics:**- **Multiple R:** 0.732- **R Square:** 0.536- **Adjusted R Square:** 0.511- **Standard Error:** 4451.652- **Observations:** 100**ANOVA**[Details on ANOVA not provided in the image]---*Explanation:*- **Multiple R** refers to the correlation coefficient representing the strength and direction of a linear relationship between the observed and predicted values. - **R Square** indicates the proportion of variance in the dependent variable that can be explained by the independent variables. - **Adjusted R Square** is a modified version of R Square that adjusts for the number of predictors and provides a more accurate measure when multiple variables are involved. - **Standard Error** is an estimate of the standard deviation of the regression's residuals, providing insight into the precision of the predictions. - **Observations** denote the number of data points used in the regression analysis.- **ANOVA** stands for Analysis of Variance, a statistical method used to analyze differences among group means and their associated procedures. ### Regression Analysis Summary#### ANOVA Table| Source | df | SS | MS | F | Significance F ||-------------|-----|------------|-------------|--------|------------------|| Regression | 5 | 214,822,248 | 42,964,449.76 | 21.680 | 2.246E-14 || Residual | 94 | 186,281,697 | 1,981,720.186|| Total | 99 | 401,103,946 |#### Coefficients Table| Predictor | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% ||-------------------|--------------|----------------|---------|----------|------------|------------|| Intercept | 528.896 | 2512.847 | 0.210 | 0.8338 | -4460.421 | 5518.213 || Annual Income ($1000) | 139.220 | 15.531 | 8.964 | 2.936E-14| 108.382 | 170.058 || Household Size | 821.824 | 232.404 | 3.536 | 0.0006 | 360.381 | 1283.266 || Education | -1104.740 | 299.596 | -3.687 | 0.0004 | -1699.595 | -509.885 || TV Hours | 20.638 | 27.201 | 0.759 | 0.4499 | -33.371 | 74.646 || Age | -8.312 | 45.476 | -0.183 | 0.8554 | -98.607 | 81.982 |#### Problem StatementFind the predicted annual charges for a 44-year-old customer with an annual income of $65,000, a household size of 5, 2 years of post-high school education, and 32 hours of watching television per week.

Name: Help **Multiple Regression Output Summary**---For this question, use t
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Description: Help **Multiple Regression Output Summary**---For this question, use the following multiple regression output (which may differ from the output in other questions, even though the variables are the same).**Summary Output:****Regression Statistics:**-

In this case, Annual Charge is the dependent variable and Annual Income, Household Size, Education, TV Hours an Age independent variables.The coefficients corresponding to intercept, Annual Income, Household Size, Education, TV Hours an Age are 528.896, 139.220, 821.824, –1104.740, 20.638 and –8.312, respectively. Thus, the multiple regression equation is, Annual Charge = 528.896 + 139.220 Annual Income + 821.824 Household Size – 1104.740 Education + 20.638 TV Hours – 8.312 Age. Substitute, Annual Income = 65, Household Size = 5, Education = 2, TV Hours = 32 an Age = 44 in the regression equation. That is, Annual Charge = 528.896 + (139.220)(65) + (821.824)(5) – (1104.740)(2) + (20.638)(32) – (8.312)(44) = 11,772.524. Thus, the predicted annual charge is $11,772.524.

Math

Statistics

Significance df SS MS F F Regression 2148222488 429644497.6 21.680 2.246E-14 Residual 94 1862816975 19817201.86 Total 99 4011039463 Standard Coefficients t Stat P-value Lower 95% Upper 95% Error Intercept 528.896 2512.847 0.210 0.8338 -4460,421 5518.213 Annual Income 2.936E- 139.220 15.531 8.964 108.382 170.058 ($1000) 14 Household Size 821.824 232.404 3.536 0.0006 360.381 1283.266 Education -1104.740 299.596 -3.687 0.0004 -1699,595 -509.885 TV Hours 20.638 27.201 0.759 0.4499 -33.371 74.646 Age -8.312 45.476 -0.183 0.8554 -98.607 81.982 Find the predicted annual charges for a 44-year old customer with an annual income of $65 (thousand), a household size of 5, 2 years of post- high school education, 32 hours of watching television per week.

Significance df SS MS F F Regression 2148222488 429644497.6 21.680 2.246E-14 Residual 94 1862816975 19817201.86 Total 99 4011039463 Standard Coefficients t Stat P-value Lower 95% Upper 95% Error Intercept 528.896 2512.847 0.210 0.8338 -4460,421 5518.213 Annual Income 2.936E- 139.220 15.531 8.964 108.382 170.058 ($1000) 14 Household Size 821.824 232.404 3.536 0.0006 360.381 1283.266 Education -1104.740 299.596 -3.687 0.0004 -1699,595 -509.885 TV Hours 20.638 27.201 0.759 0.4499 -33.371 74.646 Age -8.312 45.476 -0.183 0.8554 -98.607 81.982 Find the predicted annual charges for a 44-year old customer with an annual income of $65 (thousand), a household size of 5, 2 years of post- high school education, 32 hours of watching television per week.

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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### Regression Analysis Summary

#### ANOVA Table

| Source | df | SS | MS | F | Significance F |
|-------------|-----|------------|-------------|--------|------------------|
| Regression | 5 | 214,822,248 | 42,964,449.76 | 21.680 | 2.246E-14 |
| Residual | 94 | 186,281,697 | 1,981,720.186|
| Total | 99 | 401,103,946 |

#### Coefficients Table

| Predictor | Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% |
|-------------------|--------------|----------------|---------|----------|------------|------------|
| Intercept | 528.896 | 2512.847 | 0.210 | 0.8338 | -4460.421 | 5518.213 |
| Annual Income ($1000) | 139.220 | 15.531 | 8.964 | 2.936E-14| 108.382 | 170.058 |
| Household Size | 821.824 | 232.404 | 3.536 | 0.0006 | 360.381 | 1283.266 |
| Education | -1104.740 | 299.596 | -3.687 | 0.0004 | -1699.595 | -509.885 |
| TV Hours | 20.638 | 27.201 | 0.759 | 0.4499 | -33.371 | 74.646 |
| Age | -8.312 | 45.476 | -0.183 | 0.8554 | -98.607 | 81.982 |

#### Problem Statement

Find the predicted annual charges for a 44-year-old customer with an annual income of $65,000, a household size of 5, 2 years of post-high school education, and 32 hours of watching television per week.

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