How to Plot graph from multiple independent variable and one dependent variable in python [Multiple linear regression]

Question

I am new to Machine Learning and facing a situation in which how to remove multiple independent variables in multiple linear regression. Steps I have gone through: 1) Read Dataset 2) separate into X and Y 3)Encode the categorical data as Dataset contains column : prof rank, profession etc... 4) Remove Dummy variable 5)OLS regression results.

I had 7 independent variables, after OLS ,I have 6 independent variables.Removed by P > 0.05 as P-value is greater than 0.05 significance level.

Can you suggest what are the steps to plot the graph with removing all unnecessary independent variables as attached in the image?. How to get just ONE independent variable from all these variables.

How to check multi-collinearity using python? What is VIF and how to use it to detect multi-collinearity

Thanks in advance. Sorry for grammmer mistakes if any.

OLS Regression Results Summary

Welcome to SO; please see why an image of your code is not helpful — desertnaut
– desertnaut, Commented Nov 13, 2018 at 13:40
@DejanMarić, yes I guess its the predictor value that needs to be above Significance level of 0.05. From this, we can determine that our assumption of null hypothesis is True or false. — ojas mehta
– ojas mehta, Commented Nov 13, 2018 at 14:22

Ethan Nguyen · Accepted Answer · 2018-11-13 13:41:08Z

2

It's rather difficult to visualise multidimensional linear relationship. This post shared some common ways to visualise it.

Multicollinearity is a big problem for regression and this causes weird coefficients in your betas. VIF is one of the tools used to detect this. Generally the closer VIF to 1 the better.

If you have multicollinearity, you might want to proceed with one of the following options:

Thowing out correlated variables
Transforming correlated variables: Principal Component Analysis to determine latent data structure or use Partial Least Squares

answered Nov 13, 2018 at 13:41

Ethan Nguyen

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