Oh my. This is the first time I have seen a problem with 4 predictor variables. I don't know where to start. Can you help with any of the below? I have included all the data. It's the last OLS Regression results for Predicting the Total Number of Wins using Average Points Scored, Average Relative Skill, Average Points Differential, and Average Relative Skill Differential that utilizes 4 variables.

The data will be aggregated to calculate the number of wins for teams in a basketball regular season between the years 1995 and 2015. fran id avg_pts avg_opp_pts avg clo_n avg opp_clo n avg_pts_differential avg_clo_differential total wins 1995 Bucks 99.341463 103.707317 1368.604789 1497.311587 -4.365854 -128.706798 34 1 1995 Bulls 101 524390 96.695122 1569.892129 1488. 199352 4.829268 81.692777 47 2 1995 Cavaliers 90.451220 89.829268 1542.433391 1498.848261 0.621951 43.585130 43 1995 Celtics 102 780488 104.658537 1431.307532 1495.936224 -1.878049 -64.628693 35 4 1995 Clippers 96.670732 105.829268 1309.053701 1517.260260 -9.158537 -208 206558 17 printed only the first five observations... Number of rows in the dataset = 618Total Number of Wins by Average Relative Skill Total Number of Wins 1200 1300 1400 1500 1600 1700 1800 Average Relative Skill Correlation between Average Relative Skill and the Total Number of Wins Pearson Correlation Coefficient = 0.9072 P-value = 0.0 Predicting the Total Number of Wins using Average Relative Skill OLS Regression Results Dep. Variable: total_wins 9.823 Model : OLS Adj. R-squ Least Squares F-statistic: 2865. Date : 1 Oct 2022 Prob (F-statistic): . 06e-234 Log-Likelihood: No. Observations: 618 AIC: 3865 Of Residuals: 616 BIC : 3873. Of Model: Covariance Type: nonrobus coef std err P>ItI [e.825 8.975] Intercept -128.2475 3.149 -40.731 e.wee -134.431 -122.06 avg_elo_n 0.1121 0.002 53.523 e.eee e.108 0.116 Omnibus : 152.822 Durbin-Watson: Prob(Omnibus) : 0.0ee Jarque-Bera (JB) : 393.223 Skew: -1.247 Prob(JB) : 4.10e-86 Kurtosis : 6.009 Cond. No. 2.140+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified [2] The condition number is large, 2.14e+04. This might indicate that there are strong multicollinearity or other numerical problems. Scatterplot and Correlation for the Total Number of Wins and Average Points Scored Total Number of Wins by Average Points Scored 60 50 Total Num Average Points Scored 105 110 Correlation between Average Points Scored and the Total Number of Wins Pearson Correlation Coefficient = 0.4777 P-value = 0.0 Predicting the Total Number of Wins using Average Points Scored and Average Relative Skill OLS Regres Dep. Variable: total_wins "couared. Model: OLS Adj. R-squa Method Least Squares F-statistic: Tue, 11 Oct 2022 Prob (F-statistic): . 41e-243 06:48:44 Log-Likelihood: No. Observations: 618 AIC: 3815. Of Residuals 615 BIC: 3829. Of Model: Covariance Type: nonrobust P>It [8.025 8.975] Intercept -152.5736 4. 500 -33.903 -161.411 -143.736 avg_pts 0.3497 0.048 7.297 e.0ee 0.256 0.444 avg_elo_n 0.1055 47.952 e.0ee 0.101 0.110 Omnibus. 89.087 "Durbin-Watson. Prob (Omnibus) : Jarque-Bera (JB) : 160. 540 Skew: -8.869 Prob(JB) : 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. You created a multiple regression model with the total number of wins as the response variable, with average points scored, average relative skill, average points differential, and average relative skill differential as predictor variables. Predicting the Total Number of Wins using Average Points Scored, Average Relative Skill, Average Points Differential and Average Relative Skill Differential OLS Regression Results Dep. Variable: total wins R-squared: 0.878 Model: OLS Method Adj. R-squared 0.877 Least Squares Date: 1 Oct 2022 F-statistic: Prob (F-statistic): 3. 07e-27 06:56:47 Log-Likelihood: -1815.5 No. Observations: 618 AIC: 3641. Of Residuals: 613 BIC: 3663. Of Model : Covariance Type: robust std err 16.625 Intercept 25, 867 1.337 e.182 16.223 85.373 avg_pts 0.043 6.070 0.176 0.344 avg_elo_n -0.0134 8.017 0.021 avg_pts_differential 9.135 -0.769 1:356 1:885 avg_elo_differential 0.0525 0.018 2.915 0.017 9.088 Omnibus : 193.608 Durbin-Watson : Prob(Omnibus) : Jarque-Bera (JB) : 598.416 Skew: Prob(JB) : . 14e-130 Kurtosis : 6.769 Cond. No. 2.11e+05 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 2.1le+05. This might indicate that there are strong multicollinearity or other numerical problems. - What is the equation for your model? - What is the null and alternative hypothesis (statistical notation and in words)? - Level of Significance - Test Statistic? - P-value? - Based on the results of the overall F-test, is at least one of the predictors statistically significant in predicting the number of wins in the season? - What are the results of individual t-tests for the parameters of each predictor variable? Is each of the predictor variables statistically significant based on its P-value? Use a 1% level of significance. - Report and interpret the coefficient of determination. - What is the predicted total number of wins in a regular season for a team that is averaging 75 points per game with a relative skill level of 1350, average point differential of -5 and average relative skill differential of -30