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)
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...
Related questions
Question
i need help writting this code
![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
===
====](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fab04051d-220f-4e22-ad42-dacf93a25243%2F9ad4d60f-ac62-4671-8c79-d8ad0d62460c%2Frxekpa_processed.png&w=3840&q=75)
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
===
====
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
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
This is a popular solution!
Trending now
This is a popular solution!
Step by step
Solved in 3 steps with 2 images
Recommended textbooks for you
Computer Networking: A Top-Down Approach (7th Edi…
Computer Engineering
ISBN:
9780133594140
Author:
James Kurose, Keith Ross
Publisher:
PEARSON
Computer Organization and Design MIPS Edition, Fi…
Computer Engineering
ISBN:
9780124077263
Author:
David A. Patterson, John L. Hennessy
Publisher:
Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:
9781337569330
Author:
Jill West, Tamara Dean, Jean Andrews
Publisher:
Cengage Learning
Computer Networking: A Top-Down Approach (7th Edi…
Computer Engineering
ISBN:
9780133594140
Author:
James Kurose, Keith Ross
Publisher:
PEARSON
Computer Organization and Design MIPS Edition, Fi…
Computer Engineering
ISBN:
9780124077263
Author:
David A. Patterson, John L. Hennessy
Publisher:
Elsevier Science
Network+ Guide to Networks (MindTap Course List)
Computer Engineering
ISBN:
9781337569330
Author:
Jill West, Tamara Dean, Jean Andrews
Publisher:
Cengage Learning
Concepts of Database Management
Computer Engineering
ISBN:
9781337093422
Author:
Joy L. Starks, Philip J. Pratt, Mary Z. Last
Publisher:
Cengage Learning
Prelude to Programming
Computer Engineering
ISBN:
9780133750423
Author:
VENIT, Stewart
Publisher:
Pearson Education
Sc Business Data Communications and Networking, T…
Computer Engineering
ISBN:
9781119368830
Author:
FITZGERALD
Publisher:
WILEY