I am working on an assignment and I was trying to compare my results with what is given here. https://www.bartleby.com/questions-and-answers/the-sheet-called-housepr-contains-data-on-prices-of-houses-that-have-sold-recently-and-two-attribute/9927f067-cb79-4a9b-85fd-b5fe77442023 I am getting a different Excel output and I cannot figure out why. Can I have some assistance to what I am doing wrong? Or is the solution on here incorrect?

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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I am working on an assignment and I was trying to compare my results with what is given here. https://www.bartleby.com/questions-and-answers/the-sheet-called-housepr-contains-data-on-prices-of-houses-that-have-sold-recently-and-two-attribute/9927f067-cb79-4a9b-85fd-b5fe77442023 I am getting a different Excel output and I cannot figure out why. Can I have some assistance to what I am doing wrong? Or is the solution on here incorrect?

 
**Summary Output**

**Regression Statistics**
- Multiple R: 0.762538
- R Square: 0.581464
- Adjusted R Square: 0.578636
- Standard Error: 57.03166
- Observations: 150

**ANOVA**

|            | df  | SS       | MS      | F        | Significance F  |
|------------|-----|----------|---------|----------|-----------------|
| Regression | 1   | 668781.8 | 668781.8| 205.6139 | 8.71E-30        |
| Residual   | 148 | 481386.4 | 3252.61 |          |                 |
| Total      | 149 | 1151068  |         |          |                 |

**Coefficients**

|              | Coefficient | Standard Error | t Stat  | P-value  | Lower 95%  | Upper 95%  | Lower 95.0% | Upper 95.0% |
|--------------|-------------|----------------|---------|----------|------------|------------|-------------|-------------|
| Intercept    | 296.2633    | 35.29389       | 8.394182| 3.48E-14 | 226.5183   | 366.0084   | 226.5183    | 366.0084    |
| SqrFoot      | 28.56961    | 1.992407       | 14.33924| 8.71E-30 | 24.63237   | 32.50685   | 24.63237    | 32.50685    |

**Explanation:**

The results provided are from a regression analysis, which helps in understanding the relationship between variables. 

- **Multiple R** indicates the correlation between observed and predicted values of the dependent variable; a value of 0.762538 suggests a strong positive correlation.

- **R Square** (0.581464) represents the proportion of the variance for the dependent variable that's explained by the independent variable(s) in the model.

- **Adjusted R Square** is a refined version that is adjusted for the number of predictors in the model.

- **Standard Error** provides a measure of the typical distance that the observed values fall from regression line.

- **ANOVA Table:**
Transcribed Image Text:**Summary Output** **Regression Statistics** - Multiple R: 0.762538 - R Square: 0.581464 - Adjusted R Square: 0.578636 - Standard Error: 57.03166 - Observations: 150 **ANOVA** | | df | SS | MS | F | Significance F | |------------|-----|----------|---------|----------|-----------------| | Regression | 1 | 668781.8 | 668781.8| 205.6139 | 8.71E-30 | | Residual | 148 | 481386.4 | 3252.61 | | | | Total | 149 | 1151068 | | | | **Coefficients** | | Coefficient | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | Lower 95.0% | Upper 95.0% | |--------------|-------------|----------------|---------|----------|------------|------------|-------------|-------------| | Intercept | 296.2633 | 35.29389 | 8.394182| 3.48E-14 | 226.5183 | 366.0084 | 226.5183 | 366.0084 | | SqrFoot | 28.56961 | 1.992407 | 14.33924| 8.71E-30 | 24.63237 | 32.50685 | 24.63237 | 32.50685 | **Explanation:** The results provided are from a regression analysis, which helps in understanding the relationship between variables. - **Multiple R** indicates the correlation between observed and predicted values of the dependent variable; a value of 0.762538 suggests a strong positive correlation. - **R Square** (0.581464) represents the proportion of the variance for the dependent variable that's explained by the independent variable(s) in the model. - **Adjusted R Square** is a refined version that is adjusted for the number of predictors in the model. - **Standard Error** provides a measure of the typical distance that the observed values fall from regression line. - **ANOVA Table:**
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