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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

Below the Regression Equation, identify its slope and the y-intercept.   Write the interpretation of the slope of the line of regression

MATLAB: An Introduction with Applications
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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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.
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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