how to write interpretation for coef for logistic regression in python with these descriptive features and target one which is (CHD) Intercept:-0.5454523916030884Coefficients:[0.00743907 0.01679157 0.03049434 0.00558767 0.17068218]OLS Regression ResultsDep. Variable:Model:Method:R-squared:Adj. R-squared:chd0.229OLS0.220Least SquaresFri, 09 Apr 202122:32:58F-statistic:27.02Prob (F-statistic):Log-Likelihood:Date:5.92e-24Time:-252.46No. Observations:Df Residuals:462AIC:516.9456ВIC:541.7Df Model:Covariance Type:nonrobustcoefstd errtP>|t|[0.0250.975]const-0.54550.130-4.1820.000-0.802-0.289age0.00740.0024.6320.0000.0040.011tobacco0.01680.0053.5110.0000.0070.026dl0.03050.0103.0400.0030.0110.050typea0.00560.0022.7690.0060.0020.010encoded_famhist0.17070.0414.1440.0000.0900.252=====:Omnibus:94.252Durbin-Watson:1.992Prob(Omnibus):Jarque-Bera (JB):Prob (JB):0.00030.041Skew:0.3873.00e-07Kurtosis:2.019Cond. No.463.Notes:[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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how to write interpretation for coef for logistic regression in python with these descriptive features and target one which is (CHD)

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Question

how to write interpretation for coef for logistic regression in python with these descriptive features and target one which is (CHD)

Intercept:
-0.5454523916030884
Coefficients:
[0.00743907 0.01679157 0.03049434 0.00558767 0.17068218]
OLS Regression Results
Dep. Variable:
Model:
Method:
R-squared:
Adj. R-squared:
chd
0.229
OLS
0.220
Least Squares
Fri, 09 Apr 2021
22:32:58
F-statistic:
27.02
Prob (F-statistic):
Log-Likelihood:
Date:
5.92e-24
Time:
-252.46
No. Observations:
Df Residuals:
462
AIC:
516.9
456
ВIC:
541.7
Df Model:
Covariance Type:
nonrobust
coef
std err
t
P>|t|
[0.025
0.975]
const
-0.5455
0.130
-4.182
0.000
-0.802
-0.289
age
0.0074
0.002
4.632
0.000
0.004
0.011
tobacco
0.0168
0.005
3.511
0.000
0.007
0.026
dl
0.0305
0.010
3.040
0.003
0.011
0.050
typea
0.0056
0.002
2.769
0.006
0.002
0.010
encoded_famhist
0.1707
0.041
4.144
0.000
0.090
0.252
=====:
Omnibus:
94.252
Durbin-Watson:
1.992
Prob(Omnibus):
Jarque-Bera (JB):
Prob (JB):
0.000
30.041
Skew:
0.387
3.00e-07
Kurtosis:
2.019
Cond. No.
463.
Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

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