À financial analyst builds a regression model to predict the cost of a project based upon several project characteristics. Partial Excel output is displayed below. How much variation in the project cost is accounted for by the regression model?

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A financial analyst builds a regression model to predict the cost of a project based upon several project characteristics. Partial Excel output is displayed below. How much variation in the project cost is accounted for by the regression model?

**Regression Statistics**

- Multiple R
- R Square: 0.849
- Adjusted R Square
- Standard Error: 8.718
- Observations: 102

Enter your answer as a **percentage** rounded to one (1) decimal place.

**Your Answer:**

[Text Box]

**Answer**

---

**Explanation:**

This data represents statistical measures derived from a regression analysis. Here’s what each term means:

- **R Square (0.849):** This value represents the proportion of the variance in the dependent variable (project cost) that can be predicted from the independent variables. An R Square of 0.849 indicates that approximately 84.9% of the variation in the project cost is explained by the regression model. 

- **Multiple R:** This is not provided but typically indicates the correlation coefficient, showing the strength and direction of a linear relationship between independent and dependent variables.

- **Adjusted R Square:** This usually adjusts the R Square for the number of predictors in the model, giving a more accurate measure when multiple variables are involved. However, this value is not provided.

- **Standard Error (8.718):** This measures the average distance that the observed values fall from the regression line. A lower standard error indicates a better fit.

- **Observations (102):** This indicates the number of data points or projects considered in the analysis.
Transcribed Image Text:A financial analyst builds a regression model to predict the cost of a project based upon several project characteristics. Partial Excel output is displayed below. How much variation in the project cost is accounted for by the regression model? **Regression Statistics** - Multiple R - R Square: 0.849 - Adjusted R Square - Standard Error: 8.718 - Observations: 102 Enter your answer as a **percentage** rounded to one (1) decimal place. **Your Answer:** [Text Box] **Answer** --- **Explanation:** This data represents statistical measures derived from a regression analysis. Here’s what each term means: - **R Square (0.849):** This value represents the proportion of the variance in the dependent variable (project cost) that can be predicted from the independent variables. An R Square of 0.849 indicates that approximately 84.9% of the variation in the project cost is explained by the regression model. - **Multiple R:** This is not provided but typically indicates the correlation coefficient, showing the strength and direction of a linear relationship between independent and dependent variables. - **Adjusted R Square:** This usually adjusts the R Square for the number of predictors in the model, giving a more accurate measure when multiple variables are involved. However, this value is not provided. - **Standard Error (8.718):** This measures the average distance that the observed values fall from the regression line. A lower standard error indicates a better fit. - **Observations (102):** This indicates the number of data points or projects considered in the analysis.
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