Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final = 3o+B₁atndrte + B₂hwrte + BapriGPA + B₁ ACT .reg final atndrte hwrte priGPA ACT Source ss 3094.70776 Model Residual 11929.9465 Total final atndrte hwrte priGPA ACT _cons Source 15024.6543 reg final priGPA ACT Source Model Residual Total Total atndrte hwrte priGPA ACT _cons Coef. Std. Err. .0138725 0183476 .010863 1.906347 .3750236 .3990516 9.225908 final priGPA ACT _cons .reg atndrte hwrte priGPA ACT SS 2950.00785 12074.6465 15024.6543 Calculate the se(3₁). Model 89295.5136 Residual 93922.8539 df 4 773.676939 17.832506 669 673 22.324895 ss 0535332 1.46863 Coef. Std. Err. 183218.367 df 2.390227 3233916 .3534674 0505019 11.74854 1.147688 MS df 1475.00393 2 671 17.9950022 673 22.324895 t MS Coef. Std. Err. MS Number of obs F(4, 669) Prob > F R-squared P>|t| [95% Conf. Interval) 3 29765.1712 670 140.183364 Adj R-squared Root MSE 673 272.241259 t P>|t| Number of obs F(2, 671) Prob > F R-squared 7.39 0.000 7.00 0.000 10.24 0.000 Adj R-squared. Root MSE Number of obs F(3, 670) Prob > F R-squared Adj R-squared Root MSE t P>|t| .428654 16.77 0.000 .0255616 10.48599 .9703064 10.81 0.000 -1.026373 .1447621 -7.09 0.000 40.33227 3.811502 10.58 0.000 674 [95% Conf. Interval) 1.755245 3.025208 .2543065 9.495044 674 81.97 0.0000 0.1963 0.1939 4.2421 .4526283 14.00203 674 212.33 0.0000 0.4874 0.4851 11.84 [95% Conf. Intervall .4788445 .3784635 8.580784 12.3912 -1.310615 -.7421306 32.84835 47.8162
Use the Stata output below. The data comes from students in an undergraduate economics course. The regression of interest is: final = 3o+B₁atndrte + B₂hwrte + BapriGPA + B₁ ACT .reg final atndrte hwrte priGPA ACT Source ss 3094.70776 Model Residual 11929.9465 Total final atndrte hwrte priGPA ACT _cons Source 15024.6543 reg final priGPA ACT Source Model Residual Total Total atndrte hwrte priGPA ACT _cons Coef. Std. Err. .0138725 0183476 .010863 1.906347 .3750236 .3990516 9.225908 final priGPA ACT _cons .reg atndrte hwrte priGPA ACT SS 2950.00785 12074.6465 15024.6543 Calculate the se(3₁). Model 89295.5136 Residual 93922.8539 df 4 773.676939 17.832506 669 673 22.324895 ss 0535332 1.46863 Coef. Std. Err. 183218.367 df 2.390227 3233916 .3534674 0505019 11.74854 1.147688 MS df 1475.00393 2 671 17.9950022 673 22.324895 t MS Coef. Std. Err. MS Number of obs F(4, 669) Prob > F R-squared P>|t| [95% Conf. Interval) 3 29765.1712 670 140.183364 Adj R-squared Root MSE 673 272.241259 t P>|t| Number of obs F(2, 671) Prob > F R-squared 7.39 0.000 7.00 0.000 10.24 0.000 Adj R-squared. Root MSE Number of obs F(3, 670) Prob > F R-squared Adj R-squared Root MSE t P>|t| .428654 16.77 0.000 .0255616 10.48599 .9703064 10.81 0.000 -1.026373 .1447621 -7.09 0.000 40.33227 3.811502 10.58 0.000 674 [95% Conf. Interval) 1.755245 3.025208 .2543065 9.495044 674 81.97 0.0000 0.1963 0.1939 4.2421 .4526283 14.00203 674 212.33 0.0000 0.4874 0.4851 11.84 [95% Conf. Intervall .4788445 .3784635 8.580784 12.3912 -1.310615 -.7421306 32.84835 47.8162
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
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![**Stata Output and Regression Analysis**
The data presented here is derived from an undergraduate economics course. The regression model of interest is expressed as:
\[ \text{final} = \beta_0 + \beta_1 \text{attndre} + \beta_2 \text{hwrtte} + \beta_3 \text{priGPA} + \beta_4 \text{ACT} \]
### 1. Regression: final on attndre hwrtte priGPA ACT
**ANOVA Table:**
- **Source of Variation:**
- **Model:**
- SS = 3904.70776
- df = 4
- MS = 773.676939
- **Residual:**
- SS = 11229.9465
- df = 669
- MS = 17.832506
- **Total:**
- SS = 15024.6543
- df = 673
- MS = 22.324895
- **Number of observations:** 674
- **F(4, 669):** [Not specified]
- **Prob > F:** [Not specified]
- **R-squared:** [Not specified]
- **Adj R-squared:** [Not specified]
- **Root MSE:** [Not specified]
**Coefficients:**
- **attndre:** Coef = 0.0138725, Std. Err. = 0.010863, t = 1.28, P>|t| = 0.199, 95% CI = [-0.0074064, 0.0351514]
- **hwrtte:** Coef = 0.0180476, Std. Err. = 0.010863, t = 1.66, P>|t| = 0.098, 95% CI = [-0.0032849, 0.0393801]
- **priGPA:** Coef = 1.906347, Std. Err. = 0.3750236, t = 5.08, P>|t| = 0.000, 95% CI = [1.170316, 2.642377]
- **ACT:** Coef = -0.3995616, Std. Err. = 0.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Ff2c90e6f-21b1-498d-a838-23f699b7822e%2F43083c09-5e2d-4d04-b09b-4e0d9b3a5d5b%2Fnedexbt_processed.png&w=3840&q=75)
Transcribed Image Text:**Stata Output and Regression Analysis**
The data presented here is derived from an undergraduate economics course. The regression model of interest is expressed as:
\[ \text{final} = \beta_0 + \beta_1 \text{attndre} + \beta_2 \text{hwrtte} + \beta_3 \text{priGPA} + \beta_4 \text{ACT} \]
### 1. Regression: final on attndre hwrtte priGPA ACT
**ANOVA Table:**
- **Source of Variation:**
- **Model:**
- SS = 3904.70776
- df = 4
- MS = 773.676939
- **Residual:**
- SS = 11229.9465
- df = 669
- MS = 17.832506
- **Total:**
- SS = 15024.6543
- df = 673
- MS = 22.324895
- **Number of observations:** 674
- **F(4, 669):** [Not specified]
- **Prob > F:** [Not specified]
- **R-squared:** [Not specified]
- **Adj R-squared:** [Not specified]
- **Root MSE:** [Not specified]
**Coefficients:**
- **attndre:** Coef = 0.0138725, Std. Err. = 0.010863, t = 1.28, P>|t| = 0.199, 95% CI = [-0.0074064, 0.0351514]
- **hwrtte:** Coef = 0.0180476, Std. Err. = 0.010863, t = 1.66, P>|t| = 0.098, 95% CI = [-0.0032849, 0.0393801]
- **priGPA:** Coef = 1.906347, Std. Err. = 0.3750236, t = 5.08, P>|t| = 0.000, 95% CI = [1.170316, 2.642377]
- **ACT:** Coef = -0.3995616, Std. Err. = 0.
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