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Ordinary Least Squares - P values differing in Jupyter and Spyder for the same Multiple Linear Regression problem
The Next CEO of Stack Overflow2019 Community Moderator ElectionDifference between OLS(statsmodel) and Scikit Linear RegressionCan FTRL be applied on linear least squares? or is it just for logistic regression models?What is the difference between residual sum of squares and ordinary least squares?Why Tensorflow does NOT quit when CUDA_ERROR_OUT_OF_MEMORYDifference between Sum of Squares and Maximum Likelihood Linear RegressionWhy running the same code on the same data gives a different result every time?Value extraction from a python dataframe [ problem statement specific ]How to integrate google cloud with dropbox and jupyter notebook using tensorflowDifference between output of probabilistic and ordinary least squares regressionsMultiple regression (using machine learning - how plot data)
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I am trying out a Multiple Linear Regression problem from a Udemy course. The course used Spyder to run the models. When I used the same code on Spyder, I got the same responses. To recap and make my own notes, I was studying and creating a Jupyter Notebook. It gives me a different OLS Summary. For both Spyder and Jupyter, the code and libraries used is the same. I am using the statsmodels.formula.api (as sm) and the sm.OLS(endog=y,exog=X_opt).fit() function on the same Vector.
machine-learning python linear-regression jupyter
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$begingroup$
I am trying out a Multiple Linear Regression problem from a Udemy course. The course used Spyder to run the models. When I used the same code on Spyder, I got the same responses. To recap and make my own notes, I was studying and creating a Jupyter Notebook. It gives me a different OLS Summary. For both Spyder and Jupyter, the code and libraries used is the same. I am using the statsmodels.formula.api (as sm) and the sm.OLS(endog=y,exog=X_opt).fit() function on the same Vector.
machine-learning python linear-regression jupyter
New contributor
$endgroup$
add a comment |
$begingroup$
I am trying out a Multiple Linear Regression problem from a Udemy course. The course used Spyder to run the models. When I used the same code on Spyder, I got the same responses. To recap and make my own notes, I was studying and creating a Jupyter Notebook. It gives me a different OLS Summary. For both Spyder and Jupyter, the code and libraries used is the same. I am using the statsmodels.formula.api (as sm) and the sm.OLS(endog=y,exog=X_opt).fit() function on the same Vector.
machine-learning python linear-regression jupyter
New contributor
$endgroup$
I am trying out a Multiple Linear Regression problem from a Udemy course. The course used Spyder to run the models. When I used the same code on Spyder, I got the same responses. To recap and make my own notes, I was studying and creating a Jupyter Notebook. It gives me a different OLS Summary. For both Spyder and Jupyter, the code and libraries used is the same. I am using the statsmodels.formula.api (as sm) and the sm.OLS(endog=y,exog=X_opt).fit() function on the same Vector.
machine-learning python linear-regression jupyter
machine-learning python linear-regression jupyter
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