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Step 6: Multiple Regression: Predicting the Total Number of Wins using Average Points Scored, Average Relative Skill, and Average Points Differential The coach also wants you to consider the average points differential as a predictor variable in the multiple regression model. Create a multiple regression model with the total number of wins as the response variable, and average points scored, average relative skill, and average points differential 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 score, average relative skill, and the average points differential between the team and their opponents. Use Step 5 to help you write this code block. Here is some information that will help you write this code block. 1. The dataframe used in this project is called nba_wins_df. 2. The variable avg_pts represents average points scored by each team in a regular season. 3. The variable avg_elo_n represents average relative skill of each team in a regular season. 4. The variable avg_pts_differential represents average points differential between each team and their opponents in a regular season. 5. Print the model summary. Write your code in the code block section below. After you are done, click this block of code and hit the Run button above. # Write your code in this code block section

Step 6: Multiple Regression: Predicting the Total Number of Wins using Average Points Scored, Average Relative Skill, and Average Points Differential The coach also wants you to consider the average points differential as a predictor variable in the multiple regression model. Create a multiple regression model with the total number of wins as the response variable, and average points scored, average relative skill, and average points differential 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 score, average relative skill, and the average points differential between the team and their opponents. Use Step 5 to help you write this code block. Here is some information that will help you write this code block. 1. The dataframe used in this project is called nba_wins_df. 2. The variable avg_pts represents average points scored by each team in a regular season. 3. The variable avg_elo_n represents average relative skill of each team in a regular season. 4. The variable avg_pts_differential represents average points differential between each team and their opponents in a regular season. 5. Print the model summary. Write your code in the code block section below. After you are done, click this block of code and hit the Run button above. # Write your code in this code block section

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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In [10]: import statsmodels.formula.api as smf # Multiple Regression # TODO: make your edits here smf.ols('total_wins model2 avg_pts + avg_elo_n', nba_wins_df).fit() print (model2.summary()) OLS Regression Results ===== ===== ==== ========================= ===== ====: ===== ==== ===== ==== ==== total wins R-squared: Adj. R-squared: Dep. Variable: 0.837 Model: OLS 0.837 Method: Least Squares F-statistic: 1580. Prob (F-statistic): Log-Likelihood: Date: Sun, 12 Dec 2021 4.41e-243 Time: 05:44:01 -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: 2 Covariance Type: nonrobust ===== сoef std err t 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): 0.000 160.540 Jarque-Bera (JB): Prob(JB): d. No. Skew: -0.869 1.38e-35 Kurtosis: 4.793 3.19e+04 === ====
Transcribed Image Text:In [10]: import statsmodels.formula.api as smf # Multiple Regression # TODO: make your edits here smf.ols('total_wins model2 avg_pts + avg_elo_n', nba_wins_df).fit() print (model2.summary()) OLS Regression Results ===== ===== ==== ========================= ===== ====: ===== ==== ===== ==== ==== total wins R-squared: Adj. R-squared: Dep. Variable: 0.837 Model: OLS 0.837 Method: Least Squares F-statistic: 1580. Prob (F-statistic): Log-Likelihood: Date: Sun, 12 Dec 2021 4.41e-243 Time: 05:44:01 -1904.6 No. Observations: 618 AIC: 3815. Df Residuals: 615 BIC: 3829. Df Model: 2 Covariance Type: nonrobust ===== сoef std err t 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): 0.000 160.540 Jarque-Bera (JB): Prob(JB): d. No. Skew: -0.869 1.38e-35 Kurtosis: 4.793 3.19e+04 === ====
Step 6: Multiple Regression: Predicting the Total Number of Wins using Average Points Scored, Average Relative Skill, and Average Points Differential The coach also wants you to consider the average points differential as a predictor variable in the multiple regression model. Create a multiple regression model with the total number of wins as the response variable, and average points scored, average relative skill, and average points differential 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 score, average relative skill, and the average points differential between the team and their opponents. Use Step 5 to help you write this code block. Here is some information that will help you write this code block. 1. The dataframe used in this project is called nba_wins_df. 2. The variable avg_pts represents average points scored by each team in a regular season. 3. The variable avg_elo_n represents average relative skill of each team in a regular season. 4. The variable avg_pts_differential represents average points differential between each team and their opponents in a regular season. 5. Print the model summary. Write your code in the code block section below. After you are done, click this block of code and hit the Run button above. # Write your code in this code block section
Transcribed Image Text:Step 6: Multiple Regression: Predicting the Total Number of Wins using Average Points Scored, Average Relative Skill, and Average Points Differential The coach also wants you to consider the average points differential as a predictor variable in the multiple regression model. Create a multiple regression model with the total number of wins as the response variable, and average points scored, average relative skill, and average points differential 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 score, average relative skill, and the average points differential between the team and their opponents. Use Step 5 to help you write this code block. Here is some information that will help you write this code block. 1. The dataframe used in this project is called nba_wins_df. 2. The variable avg_pts represents average points scored by each team in a regular season. 3. The variable avg_elo_n represents average relative skill of each team in a regular season. 4. The variable avg_pts_differential represents average points differential between each team and their opponents in a regular season. 5. Print the model summary. Write your code in the code block section below. After you are done, click this block of code and hit the Run button above. # Write your code in this code block section
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