I'm hoping someone can help. I worked one-on-one with a tutor for two similar assignments and they both resulted in Ds. I reached out to my professor and he will not help and will not allow me to resubmit anything for a better grade. I'm a little lost. Here are the questions.....

QUESTIONS:

1. What are the relationships between variable using the scatterplots and correlation coefficients?

2. What is a simple linear regression model to predict the response variable?

3. What is a multiple regression model to predict the response variable?

4. What is a summary of findings and practical implications?

DATA:

year_id	fran_id	avg_pts	avg_opp_pts	avg_elo_n	avg_opp_elo_n	avg_pts_differential	avg_elo_differential	total_wins
0	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
3	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 = 618

\fOLS Regression Results Dep. Variable: total_wins R-squared: 0. 228 Model: OLS Adj. R-squared: 0. 227 Method: Least Squares F-statistic: 182.1 Date: Mon, 08 Aug 2022 Prob (F-statistic): 1.52e-36 Time: 16:46:31 Log-Likelihood : -2385.4 No. Observations: 618 AIC: 4775. Of Residuals: 616 BIC: 4784. Of Model: 1 Covariance Type: nonrobust coef sto err t P>It| [0. 025 0.975] Intercept -85.5476 9.305 -9. 194 0.000 -103. 820 -67.275 avg_pts 1. 2849 0.095 13.495 0.090 1.098 1.472 Omnibus : 24.401 Durbin-Watson: 1. 768 Prob (Omnibus ) : 0.090 Jarque-Bera (JB) : 11. 089 Skew: -0.033 Prob (JB) : 0. 00391 Kurtosis : 2.347 Cond. No. 1.97e+03 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 1.97e+03. This might indicate that there are strong multicollinearity or other numerical problems.\fOLS Regression Results Dep. Variable: total_wins R- squared : 0.837 Model: OLS Adj. R-squared: 0.837 Method : Least Squares F-statistic: 1580. Date: Mon, 08 Aug 2022 Prob (F-statistic) : 4.41e-243 Time : 16:51:23 Log-Likelihood: -1904.6 No. Observations : 618 AIC: 3815. Of Residuals: 615 BIC: 3829. Of Model : 2 Covariance Type: nonrobust coef std err t P>It| [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) : 160.540 Skew: -0.869 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.OLS Regression Results Dep. Variable: total_wins R- squared: 0.876 Model : OLS Adj. R-squared: 0. 876 Method : Least Squares F-statistic: 1449. Date : Mon, 08 Aug 2022 Prob (F-statistic): 5.03e-278 Time : 16:52:59 Log-Likelihood : - 1819.8 No. Observations : 618 AIC: 3648. Of Residuals: 614 BIC: 3665. Of Model: 3 Covariance Type: nonrobust coef std err P> | t| [0. 025 0.975] Intercept -35. 8921 9.252 -3.879 0.000 -54.062 -17.723 avg_pts 0. 2406 0. 043 5.657 0.000 0. 157 0. 324 avg_elo_n 0.0348 0.005 6.421 0.000 0. 024 0. 045 avg_pts_differential 1. 7621 0. 127 13.928 0.000 1. 514 2. 011 Omnibus : 181.805 Durbin-Watson: 0.975 Prob (Omnibus ) : 0.000 Jarque-Bera (JB) : 506.551 Skew: -1.452 Prob (JB) : 1. 01e-110 Kurtosis : 6.352 Cond. No. 7.51e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 7.51e+04. This might indicate that there are strong multicollinearity or other numerical problems