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2. Suppose you want to estimate the following model: Reorders, = B + A · Products + ß · Days where Reorders, is the number of reorders. Products is the number of ordered products. Days, is the number of days since the last order. Using Excel, you have the following output: SUMMARY OUTPUT Regression Statistics Multiple R 0.8373 R Square 0.7010 Adjusted R Square 0.6998 Standard Error 3.1710 Observations 500 ANOVA df SS MS Significance F Regression 11717.33 5858.67 582.66 0.00 Residual 497 4997.38 10.06 Total 499 16714.71 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept products days since prior orde 0.9778 0.3416 2.8623 0.0044 0.3066 1.6489 0.6333 0.0188 33.6482 0.0000 0.5963 0.6703 -0.0653 0.0333 -4.9207 0.0000 -0.0914 -0.0392 (a) Report regression results, what are 6? 6? What are Standard Errors of 6? 6? B? What are their 95% Confidence Intervals?

2. Suppose you want to estimate the following model: Reorders, = B + A · Products + ß · Days where Reorders, is the number of reorders. Products is the number of ordered products. Days, is the number of days since the last order. Using Excel, you have the following output: SUMMARY OUTPUT Regression Statistics Multiple R 0.8373 R Square 0.7010 Adjusted R Square 0.6998 Standard Error 3.1710 Observations 500 ANOVA df SS MS Significance F Regression 11717.33 5858.67 582.66 0.00 Residual 497 4997.38 10.06 Total 499 16714.71 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Intercept products days since prior orde 0.9778 0.3416 2.8623 0.0044 0.3066 1.6489 0.6333 0.0188 33.6482 0.0000 0.5963 0.6703 -0.0653 0.0333 -4.9207 0.0000 -0.0914 -0.0392 (a) Report regression results, what are 6? 6? What are Standard Errors of 6? 6? B? What are their 95% Confidence Intervals?

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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**Problem Set 2-1: Analyzing Regression Models** In this task, you are asked to estimate the following regression model: \[ \text{Reorders} = \beta_0 + \beta_1 \cdot \text{Products} + \beta_2 \cdot \text{Days} \] Where: - **Reorders**: The number of reorders. - **Products**: The number of ordered products. - **Days**: The number of days since the last order. Using Excel, the output generated is as follows: **Summary Output:** - **Regression Statistics** - **Multiple R**: 0.8373 - **R Square**: 0.7010 - **Adjusted R Square**: 0.6998 - **Standard Error**: 3.1710 - **Observations**: 500 **ANOVA Table:** - **df**: Degrees of freedom - Regression: 2 - Residual: 497 - Total: 499 - **SS**: Sum of Squares - Regression: 11717.33 - Residual: 4997.38 - Total: 16714.71 - **MS**: Mean Square - Regression: 5858.67 - Residual: 10.06 - **F**: F-statistic: 582.66 - **Significance F**: 0.00 **Coefficients Table:** | Coefficient | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |------------------------|----------------|---------|---------|-----------|-----------| | Intercept | 0.9778 | 0.3416 | 2.8623 | 0.0044 | 0.3066 | 1.6489 | | Products | 0.6333 | 0.0188 | 33.6482 | 0.0000 | 0.5953 | 0.6703 | | Days since prior order | -0.0653 | 0.0133 | -4.9207 | 0.0000 | -0.0914 |
Transcribed Image Text:**Problem Set 2-1: Analyzing Regression Models** In this task, you are asked to estimate the following regression model: \[ \text{Reorders} = \beta_0 + \beta_1 \cdot \text{Products} + \beta_2 \cdot \text{Days} \] Where: - **Reorders**: The number of reorders. - **Products**: The number of ordered products. - **Days**: The number of days since the last order. Using Excel, the output generated is as follows: **Summary Output:** - **Regression Statistics** - **Multiple R**: 0.8373 - **R Square**: 0.7010 - **Adjusted R Square**: 0.6998 - **Standard Error**: 3.1710 - **Observations**: 500 **ANOVA Table:** - **df**: Degrees of freedom - Regression: 2 - Residual: 497 - Total: 499 - **SS**: Sum of Squares - Regression: 11717.33 - Residual: 4997.38 - Total: 16714.71 - **MS**: Mean Square - Regression: 5858.67 - Residual: 10.06 - **F**: F-statistic: 582.66 - **Significance F**: 0.00 **Coefficients Table:** | Coefficient | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |------------------------|----------------|---------|---------|-----------|-----------| | Intercept | 0.9778 | 0.3416 | 2.8623 | 0.0044 | 0.3066 | 1.6489 | | Products | 0.6333 | 0.0188 | 33.6482 | 0.0000 | 0.5953 | 0.6703 | | Days since prior order | -0.0653 | 0.0133 | -4.9207 | 0.0000 | -0.0914 |
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