Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: OLS Regression Results total_wins R-squared: OLS Least Squares Fri, 13 Oct 2023 09:43:01 618 615 2 Adj. R-squared: Prob (F-statistic): F-statistic: Log-Likelihood: AIC: BIC: 0.837 0.837 1580. 4.41e-243 -1904.6 3815. 3829.

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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a) What is the equation for this model? b) What are the null hypothesis, alternative hypothesis, and the level of significance? c) What is the test statistic and the p-value?
Dep. Variable:
Model:
Method:
Date:
Time:
No. Observations:
Df Residuals:
Df Model:
Covariance Type:
coef
Intercept -152.5736
avg_elo_n
avg_pts
Omnibus:
Prob (Omnibus):
Skew:
Kurtosis:
0.1055
0.3497
OLS Regression Results
total wins R-squared:
OLS
Least Squares
Fri, 13 Oct 2023
09:43:01
nonrobust
std err
618
615
2
4.500
0.002
0.048
Adj. R-squared:
Prob (F-statistic):
F-statistic:
Log-Likelihood:
AIC:
BIC:
t
-33.903
47.952
7.297
P>|t|
0.000
0.000
0.000
89.087 Durbin-Watson:
0.000 Jarque-Bera (JB):
-0.869 Prob(JB):
4.793
Cond. No.
[0.025
-161.411
0.101
0.256
0.837
0.837
1580.
4.41e-243
-1904.6
3815.
3829.
====
0.975]
-143.736
0.110
0.444
1.203
160.540
1.38e-35
3.19e+04
Transcribed Image Text:Dep. Variable: Model: Method: Date: Time: No. Observations: Df Residuals: Df Model: Covariance Type: coef Intercept -152.5736 avg_elo_n avg_pts Omnibus: Prob (Omnibus): Skew: Kurtosis: 0.1055 0.3497 OLS Regression Results total wins R-squared: OLS Least Squares Fri, 13 Oct 2023 09:43:01 nonrobust std err 618 615 2 4.500 0.002 0.048 Adj. R-squared: Prob (F-statistic): F-statistic: Log-Likelihood: AIC: BIC: t -33.903 47.952 7.297 P>|t| 0.000 0.000 0.000 89.087 Durbin-Watson: 0.000 Jarque-Bera (JB): -0.869 Prob(JB): 4.793 Cond. No. [0.025 -161.411 0.101 0.256 0.837 0.837 1580. 4.41e-243 -1904.6 3815. 3829. ==== 0.975] -143.736 0.110 0.444 1.203 160.540 1.38e-35 3.19e+04
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