Ý = 1.44X + 0.23 Calculate the missing predicted values of Y, residuals, and squared residuals to complete the following table. (Note: Your answers may differ slightly due to rounding. Select the responses that most dlosely match your results.) Data Values Predicted y Residual Squared Residual x Y Y - Ý (Y - Ý)2 1.6 2.2 3.4 3.40 0.00 0.00 3.4 5.2 5.13 0.07 0.00 4.4 6.4 5.6 8.8 8.29 0.51 0.26 6.6 9.4 Calculate the sum of the squares of the residual, the degrees of freedom, and the standard error of estimate for the data on Graph I. The SSresidual , and the standard error of estimate is
Ý = 1.44X + 0.23 Calculate the missing predicted values of Y, residuals, and squared residuals to complete the following table. (Note: Your answers may differ slightly due to rounding. Select the responses that most dlosely match your results.) Data Values Predicted y Residual Squared Residual x Y Y - Ý (Y - Ý)2 1.6 2.2 3.4 3.40 0.00 0.00 3.4 5.2 5.13 0.07 0.00 4.4 6.4 5.6 8.8 8.29 0.51 0.26 6.6 9.4 Calculate the sum of the squares of the residual, the degrees of freedom, and the standard error of estimate for the data on Graph I. The SSresidual , and the standard error of estimate is
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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
Transcribed Image Text:### Educational Resource: Linear Regression Analysis
#### Linear Regression Formula
The linear regression equation used is: \( \hat{Y} = 1.44X + 0.23 \)
#### Problem Statement
Calculate the missing predicted values of \( Y \) (denoted as \( \hat{Y} \)), residuals, and squared residuals to complete the following table. (**Note:** Your answers may differ slightly due to rounding. Select the responses that most closely match your results.)
#### Data Values and Calculations
| X | Y | Predicted \( \hat{Y} \) | Residual (\( Y - \hat{Y} \)) | Squared Residual (\( (Y - \hat{Y})^2 \)) |
|-----|-----|---------------------------|-------------------------------|------------------------------------------|
| 1.0 | 1.6 | - | - | - |
| 2.2 | 3.4 | 3.40 | 0.00 | 0.00 |
| 3.4 | 5.2 | 5.13 | 0.07 | 0.00 |
| 4.4 | 6.4 | - | - | - |
| 5.6 | 8.8 | 8.29 | 0.51 | 0.26 |
| 6.6 | 9.4 | - | - | - |
#### Steps to Calculate
1. **Predicted \( \hat{Y} \):** Use the linear regression formula \( \hat{Y} = 1.44X + 0.23 \).
- For \( X = 1.0 \), \( \hat{Y} = 1.44(1.0) + 0.23 = 1.67 \)
- For \( X = 4.4 \), \( \hat{Y} = 1.44(4.4) + 0.23 = 6.67 \)
- For \( X = 6.6 \), \( \hat{Y} = 1.44(6.6) + 0.23 = 10.74 \)
2. **Residuals (\( Y - \hat{Y} \))**:
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