Briefly, do you think there is enough predictive power in the freshman courses, relative to the freshman and sophomore courses combined, to change to a sophomore admit policy? (Answer only on the basis of the regression results; don’t get into other merits of the argument.) Sophomore regression results is the picture with code E270, freshm
Briefly, do you think there is enough predictive power in the freshman courses, relative to the freshman and sophomore courses combined, to change to a sophomore admit policy? (Answer only on the basis of the regression results; don’t get into other merits of the argument.) Sophomore regression results is the picture with code E270, freshm
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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Briefly, do you think there is enough predictive power in the freshman courses, relative to the freshman and sophomore courses combined, to change to a sophomore admit policy? (Answer only on the basis of the regression results; don’t get into other merits of the argument.)
Sophomore regression results is the picture with code E270, freshman is the other (K201)

Transcribed Image Text:**Date:** Saturday, October 2, 2021
**Updating:** Static
**Variable:** Core
### Multiple Regression for Core
#### Summary
- **Multiple R:** 0.4166
- **R-Square:** 0.1735
- **Adjusted R-square:** 0.1702
- **Standard Error of Estimate:** 0.520051414
- **Rows Ignored:** 0
- **Outliers:** 0
#### ANOVA Table
| Source | Degrees of Freedom | Sum of Squares | Mean of Squares | F | p-Value |
|--------------|--------------------|----------------|-----------------|-------------|----------|
| Explained | 1 | 14.08195445 | 14.08195445 | 52.06793712 | < 0.0001 |
| Unexplained | 248 | 67.07246144 | 0.270453544 | | |
#### Regression Table
| Variable | Coefficient | Standard Error | t-Value | p-Value | Confidence Interval 95% |
|-----------|-------------|-----------------|-------------|----------|----------------------------|
| Constant | 1.27174276 | 0.168542825 | 7.545517059 | < 0.0001 | Lower: 0.939784915 <br> Upper: 1.603700604 |
| E270 | 0.383435348 | 0.053138215 | 7.215811605 | < 0.0001 | Lower: 0.278775614 <br> Upper: 0.488095082 |
### Explanation
This table presents the results of a multiple regression analysis. Here's a breakdown of the key components:
- **Multiple R** indicates the correlation between the observed and predicted values of the dependent variable. A value of 0.4166 suggests a moderate positive correlation.
- **R-Square** (0.1735) shows that about 17.35% of the variance in the dependent variable can be explained by the model.
- **ANOVA Table** dissects the variance into explained and unexplained parts, revealing the significance of the overall model (p-Value < 0.0001).
- **Regression Table** lists the variables (Constant and E270

Transcribed Image Text:**Multiple Regression Analysis for Core Variable**
Date: Saturday, October 2, 2021
Updating: Static
---
### Summary
- **Multiple R**: 0.3583
- **R-Square**: 0.1284
- **Adjusted R-Square**: 0.1249
- **Standard Error of Estimate**: 0.534056823
- **Rows Ignored**: 0
- **Outliers**: 0
### ANOVA Table
- **Degrees of Freedom**:
- Explained: 1
- Unexplained: 248
- **Sum of Squares**:
- Explained: 10.42067669
- Unexplained: 70.7337392
- **Mean of Squares**:
- Explained: 10.42067669
- Unexplained: 0.285126969
- **F-Value**: 36.53599894
- **p-Value**: < 0.0001
---
### Regression Table
| Variable | Coefficient | Standard Error | t-Value | p-Value | Confidence Interval 95% (Lower) | Confidence Interval 95% (Upper) |
|------------|-------------|----------------|-------------|---------|---------------------------------|---------------------------------|
| Constant | 1.39654313 | 0.179884714 | 7.763428726 | < 0.0001 | 1.042241255 | 1.750845004 |
| K201 | 0.341735028 | 0.056365361 | 6.044501546 | < 0.0001 | 0.230382093 | 0.453087964 |
---
This regression analysis provides insights into the relationship between the core variable and predictor K201. The model shows a moderate correlation (Multiple R = 0.3583) and a determination coefficient (R-Square = 0.1284), indicating that approximately 12.84% of the variability in the core variable can be explained by K201. The significant p-values suggest that the predictor is statistically significant in the model.
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