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,…

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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Concept explainers

Unitary Method

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The amount earned or lost on the sale of one or more items is referred to as the profit or loss on that item.

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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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