MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN: 9781119256830
Author: Amos Gilat
Publisher: John Wiley & Sons Inc
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Suggest a multiple regression model with the total number of wins as the response variable and the average points scored and the average relative skill as predictor variables. This regression model will help your coach predict how many games your team might win in a regular season based on metrics like the average points scored and average relative skill. This model is more practical because you expect more than one performance metric to determine the total number of wins in a regular season. Create this multiple regression model. Make the following edits to the code block below:

    1. Null Hypothesis (statistical notation and its description in words)

    2. Alternative Hypothesis (statistical notation and its description in words)

[画像:OLS Regression Results ==== === Dep. Variable: total wins R-squared: Adj. R-squared: F-statistic: 0.837 Model: OLS 0.837 Method: Least Squares 1580. Prob (F-statistic): Log-Likelihood: Date: 2020年10月12日 4.41e-243 Time: 15:12:43 -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: 2 Covariance Type: nonrobust сoef std err P>|t| [0.025 0.975] Intercept -152.5736 4.500 -33.903 0.000 -161.411 -143.736 avg_pts 0.3497 0.048 7.297 0.000 0.256 0.444 avg_elo_n 0.1055 0.002 47.952 0.000 0.101 0.110 Omnibus: 89.087 Durbin-Watson: 1.203 Prob (Omnibus): 0.000 Jarque-Bera (JB): Prob (JB) : 160.540 Skew: -0.869 1.38e-35 Kurtosis: 4.793 Cond. No. 3.19e+04 ==== ==== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 3.19e+04. This might indicate that there are strong multicollinearity or other numerical problems. ]
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Transcribed Image Text:OLS Regression Results ==== === Dep. Variable: total wins R-squared: Adj. R-squared: F-statistic: 0.837 Model: OLS 0.837 Method: Least Squares 1580. Prob (F-statistic): Log-Likelihood: Date: 2020年10月12日 4.41e-243 Time: 15:12:43 -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: 2 Covariance Type: nonrobust сoef std err P>|t| [0.025 0.975] Intercept -152.5736 4.500 -33.903 0.000 -161.411 -143.736 avg_pts 0.3497 0.048 7.297 0.000 0.256 0.444 avg_elo_n 0.1055 0.002 47.952 0.000 0.101 0.110 Omnibus: 89.087 Durbin-Watson: 1.203 Prob (Omnibus): 0.000 Jarque-Bera (JB): Prob (JB) : 160.540 Skew: -0.869 1.38e-35 Kurtosis: 4.793 Cond. No. 3.19e+04 ==== ==== Warnings: [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 3.19e+04. This might indicate that there are strong multicollinearity or other numerical problems.
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