•In general, how is a multiple linear regression model used to predict the response variable using predictor variables? •What is the equation for your model? •What are the results of the overall F-test? Summarize all important steps of this hypothesis test. This includes: a. Null Hypothesis (statistical notation and its description in words) b. Alternative Hypothesis (statistical notation and its description in words) C. Level of Significance d. Report the test statistic and the P-value.
•In general, how is a multiple linear regression model used to predict the response variable using predictor variables? •What is the equation for your model? •What are the results of the overall F-test? Summarize all important steps of this hypothesis test. This includes: a. Null Hypothesis (statistical notation and its description in words) b. Alternative Hypothesis (statistical notation and its description in words) C. Level of Significance d. Report the test statistic and the P-value.
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
Question
•In general, how is a multiple linear regression model used to predict the response variable using predictor variables?
•What is the equation for your model?
•What are the results of the overall F-test? Summarize all important steps of this hypothesis test.
This includes:
a. Null Hypothesis (statistical notation and its description in words)
b. Alternative Hypothesis (statistical notation and its description in words)
C. Level of Significance
d. Report the test statistic and the P-value.
![Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Intercept
avg_elo_n
avg_pts
avg_pts_differential
avg_elo_differential
Omnibus:
Prob (Omnibus):
Skew:
Kurtosis:
OLS Regression Results
R-squared:
Adj. R-squared:
Prob (F-statistic):
total_wins
OLS
Least Squares
Fri, 13 Oct 2023
09:45:02
nonrobust
coef
34.5753
-0.0134
0.2597
1.6206
0.0525
F-statistic:
Log-Likelihood:
618
AIC:
613 BIC:
4
std err
25.867
0.017
0.043
0.135
0.018
t
1.337
-0.769
6.070
12.024
2.915
193.608
0.000
-1.503 Prob (JB):
6.769
Cond. No.
Durbin-Watson:
Jarque-Bera (JB):
P>|t|
0.182
0.442
0.000
0.000
0.004
0.878
0.877
1102.
3.07e-278
-1815.5
3641.
3663.
[0.025
-16.223
-0.048
0.176
1.356
0.017
0.979
598.416
1.14e-130
2.11e+05
0.975]
85.373
0.021
0.344
1.885
0.088](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F9d62d096-051c-4718-a327-e350c164712e%2F0d933baa-f916-405d-a62e-43d363658c24%2Fogz2vve_processed.jpeg&w=3840&q=75)
Transcribed Image Text:Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
Intercept
avg_elo_n
avg_pts
avg_pts_differential
avg_elo_differential
Omnibus:
Prob (Omnibus):
Skew:
Kurtosis:
OLS Regression Results
R-squared:
Adj. R-squared:
Prob (F-statistic):
total_wins
OLS
Least Squares
Fri, 13 Oct 2023
09:45:02
nonrobust
coef
34.5753
-0.0134
0.2597
1.6206
0.0525
F-statistic:
Log-Likelihood:
618
AIC:
613 BIC:
4
std err
25.867
0.017
0.043
0.135
0.018
t
1.337
-0.769
6.070
12.024
2.915
193.608
0.000
-1.503 Prob (JB):
6.769
Cond. No.
Durbin-Watson:
Jarque-Bera (JB):
P>|t|
0.182
0.442
0.000
0.000
0.004
0.878
0.877
1102.
3.07e-278
-1815.5
3641.
3663.
[0.025
-16.223
-0.048
0.176
1.356
0.017
0.979
598.416
1.14e-130
2.11e+05
0.975]
85.373
0.021
0.344
1.885
0.088
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