How to Plot graph from multiple independent variable and one dependent variable in python [Multiple linear...
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
python machine-learning plot linear-regression
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
add a comment |
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
python machine-learning plot linear-regression
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
do you understand p value?
– Dejan Marić
Nov 13 '18 at 13:36
Welcome to SO; please see why an image of your code is not helpful
– desertnaut
Nov 13 '18 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
Nov 13 '18 at 14:22
add a comment |
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
python machine-learning plot linear-regression
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
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
python machine-learning plot linear-regression
python machine-learning plot linear-regression
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
edited Nov 13 '18 at 14:30
ojas mehta
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
asked Nov 13 '18 at 13:24
ojas mehtaojas mehta
61
61
do you understand p value?
– Dejan Marić
Nov 13 '18 at 13:36
Welcome to SO; please see why an image of your code is not helpful
– desertnaut
Nov 13 '18 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
Nov 13 '18 at 14:22
add a comment |
do you understand p value?
– Dejan Marić
Nov 13 '18 at 13:36
Welcome to SO; please see why an image of your code is not helpful
– desertnaut
Nov 13 '18 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
Nov 13 '18 at 14:22
do you understand p value?
– Dejan Marić
Nov 13 '18 at 13:36
do you understand p value?
– Dejan Marić
Nov 13 '18 at 13:36
Welcome to SO; please see why an image of your code is not helpful
– desertnaut
Nov 13 '18 at 13:40
Welcome to SO; please see why an image of your code is not helpful
– desertnaut
Nov 13 '18 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
Nov 13 '18 at 14:22
@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
Nov 13 '18 at 14:22
add a comment |
1 Answer
1
active
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votes
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 '18 at 13:41
Ethan NguyenEthan Nguyen
465
add a comment |
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1 Answer
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active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
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 '18 at 13:41
Ethan NguyenEthan Nguyen
465
add a comment |
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 '18 at 13:41
Ethan NguyenEthan Nguyen
465
add a comment |
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 '18 at 13:41
Ethan NguyenEthan Nguyen
465
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 '18 at 13:41
Ethan NguyenEthan Nguyen
465
answered Nov 13 '18 at 13:41
Ethan NguyenEthan Nguyen
465
answered Nov 13 '18 at 13:41
Ethan NguyenEthan Nguyen
465
answered Nov 13 '18 at 13:41
Ethan NguyenEthan Nguyen
465
465
add a comment |
add a comment |
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do you understand p value?
– Dejan Marić
Nov 13 '18 at 13:36
Welcome to SO; please see why an image of your code is not helpful
– desertnaut
Nov 13 '18 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
Nov 13 '18 at 14:22