c) Does the addition of X significantly contribute to prediction of y after accounting for the contribution of X₂? Use=0.05- O Reject the null hypothesis O Do not reject the null hypothesis
c) Does the addition of X significantly contribute to prediction of y after accounting for the contribution of X₂? Use=0.05- O Reject the null hypothesis O Do not reject the null hypothesis
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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
Transcribed Image Text:**SAS Output Analysis for Regressions**
This page provides an overview of how to analyze regression output using SAS for a random sample of size 20.
### Regression of \( y \) on \( x_1 \) and \( x_2 \)
- **Number of Observations Read:** 20
- **Number of Observations Used:** 20
#### Analysis of Variance Table
| Source | DF | Sum of Squares | Mean Square | F Value | Pr > F |
|---------|----|----------------|-------------|---------|---------|
| Model | 2 | 1317.79841 | 658.89921 | 20.84 | <.0001 |
| Error | 17 | 537.40359 | 31.61198 | | |
| Corrected Total | 19 | 1855.20200 | | | |
#### Summary Statistics
- **R-Square:** 0.818318
- **Coefficient Variance:** 22.31297
- **Root MSE:** 4.589778
- **y Mean:** 20.57000
#### Parameter Estimates
| Parameter | Estimate | Standard Error |
|-----------|------------|----------------|
| Intercept | -36.7649258| 7.01092577 |
| x1 | 0.00076294 | 0.00063630 |
| x2 | 1.19217421 | 0.56165391 |
#### Type I SS
| Source | SS |
|--------|-----------|
| x1 | 1.352481 |
| x2 | 1309.445931|
#### Type III SS
| Source | SS |
|--------|-----------|
| x1 | 30.2855361|
| x2 | 94.9129162|
### Regression of \( y \) on \( x_1, x_2, \) and \( x_3 \)
#### Analysis of Variance Table
| Source | DF | Sum of Squares | Mean Square | F Value | Pr > F |
|---------|----|----------------|-------------|---------|---------|
| Model | 3 | 1519.144491 | 506.048114 | 24
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