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: Null Hypothesis (statistical notation and its description in words) Alternative Hypothesis (statistical notation and its description in words)
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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:
-
Null Hypothesis (statistical notation and its description in words)
-
Alternative Hypothesis (statistical notation and its description in words)
![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:
Mon, 12 Oct 2020
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.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fde359f2f-7d52-4a3f-a1b6-cbf0103f2e45%2Fa0a134e6-62d6-4e1f-93b6-f7d6f5add29b%2F8a8noki_processed.png&w=3840&q=75)
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