MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Below the Regression Equation, identify its slope and the y-intercept.
Write the interpretation of the slope of the line of regression
![### Fitted Line Plot Analysis
#### Description
This graph is a fitted line plot, illustrating the relationship between Predicted GPA and College GPA.
#### Details:
- **X-Axis (Horizontal):** Predicted GPA
- **Y-Axis (Vertical):** College GPA
#### Data Points:
Each blue dot on the graph represents an individual data point, plotting a Predicted GPA against a corresponding College GPA.
#### Fitted Line:
A red line is shown on the plot, representing the best fit line calculated through linear regression. This line provides a visual representation of the average trend in the data.
#### Regression Equation:
The equation for the line is:
\[ \text{College GPA} = -0.006 + 1.021 \times \text{Predicted GPA} \]
This equation suggests that for every one unit increase in Predicted GPA, College GPA increases by approximately 1.021.
#### Statistical Information:
- **S (Standard Error):** 0.400783
- **R-Squared (R-Sq):** 16.0%
- **Adjusted R-Squared (R-Sq(adj)):** 13.0%
These statistics indicate the fit of the regression line to the data. An R-Squared value of 16% signifies that 16% of the variation in College GPA is explained by the Predicted GPA.
This analysis can be helpful for educational insights into how well predicted performance aligns with actual academic outcomes.](https://content.bartleby.com/qna-images/question/ac3be618-b645-4bd8-855b-808ff3fc18d4/c0d19be0-bb8f-45db-9fc6-7c5ca02385d6/dlxiwvo_processed.png)
Transcribed Image Text:### Fitted Line Plot Analysis
#### Description
This graph is a fitted line plot, illustrating the relationship between Predicted GPA and College GPA.
#### Details:
- **X-Axis (Horizontal):** Predicted GPA
- **Y-Axis (Vertical):** College GPA
#### Data Points:
Each blue dot on the graph represents an individual data point, plotting a Predicted GPA against a corresponding College GPA.
#### Fitted Line:
A red line is shown on the plot, representing the best fit line calculated through linear regression. This line provides a visual representation of the average trend in the data.
#### Regression Equation:
The equation for the line is:
\[ \text{College GPA} = -0.006 + 1.021 \times \text{Predicted GPA} \]
This equation suggests that for every one unit increase in Predicted GPA, College GPA increases by approximately 1.021.
#### Statistical Information:
- **S (Standard Error):** 0.400783
- **R-Squared (R-Sq):** 16.0%
- **Adjusted R-Squared (R-Sq(adj)):** 13.0%
These statistics indicate the fit of the regression line to the data. An R-Squared value of 16% signifies that 16% of the variation in College GPA is explained by the Predicted GPA.
This analysis can be helpful for educational insights into how well predicted performance aligns with actual academic outcomes.
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More information on the regression equation that is presented
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Can the regression equation reasonably be used to make predictions
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More information on the regression equation that is presented
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Can the regression equation reasonably be used to make predictions
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