how to write interpretation for coef for logistic regression in python with these descriptive features and target one which is (CHD)
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.
how to write interpretation for coef for logistic regression in python with these descriptive features and target one which is (CHD)
![Intercept:
-0.5080305742152667
Coefficients:
[ 0.00684398 -0.00023639
0.00133872 0.01658411
0.03317909
0.00230257
0.00608168 -0.01117113 0.17342903]
OLS Regression Results
========
chd
R-squared:
Adj. R-squared:
Dep. Variable:
0.236
Model:
OLS
0.221
Method:
Least Squares
Thu, 08 Apr 2021
F-statistic:
15.51
Prob (F-statistic):
Log-Likelihood:
Date:
3.92e-22
Time:
03:52:05
-250.21
No. Observations:
Df Residuals:
462
AIC:
520.4
452
BIC:
561.8
Df Model:
9
Covariance Type:
nonrobust
coef
std err
P>|t|
[0.025
0.975]
const
-0.5080
0.205
-2.483
0.013
-0.910
-0.106
age
0.0068
0.002
3.445
0.001
0.003
0.011
alcohol
-0.0002
0.001
-0.285
0.776
-0.002
0.001
sbp
0.0013
0.001
1.265
0.206
-0.001
0.003
tobacco
0.0166
0.005
3.412
0.001
0.007
0.026
ldl
0.0332
0.011
3.108
0.002
0.012
0.054
adiposity
typea
obesity
encoded_famhist
0.0023
0.005
0.483
0.629
-0.007
0.012
0.0061
0.002
2.986
0.003
0.002
0.010
-0.0112
0.007
-1.589
0.113
-0.025
0.003
0.1734
0.041
4.202
0.000
0.092
0.255
ニニ===ニニニ
Omnibus:
81.628
Durbin-Watson:
1.984
Prob(Omnibus):
Skew:
Jarque-Bera (JB):
Prob(JB):
Cond. No.
0.000
29.329
0.397
4.28e-07
Kurtosis:
2.055
1.68e+03
=======
====ニ=
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The condition number is large, 1.68e+03. 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%2F231ebc47-ca0e-4c51-981b-d583c81638ff%2F9105e4a7-0511-4d2a-80c4-805c6d64dceb%2Fzzpn9cd_processed.jpeg&w=3840&q=75)
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