OLS Statsmodels Summary Table Explanation in details | Linear Regression Machine Learning|Data Scien
Here I explained the Stats-model summary Table statistics in details.
Introduction 0:00
0:00 How to apply StatsModel OLS Linear Regression?
2:16 What is statsmodel.summary
3:33 DF(Degree of Freedom) residual
4:15 DF(Degree of freedom) Model
4:31 Covariance Type
6:10 R-Squared
6:03 Adjusted R-Squared
8:24 F-Statistics
11:24 Probability of F-Statistics
12:22 Log-Likelihood
15:05 AIC and BIC
17:57 Coefficient
19:02 Standard Error
21:16 Standard Error of Coefficient
22:51 t-statistics and P-Value of t-statistics
25:30 Omnibus and Probability of Omnibus -To test the residuals normal distribution
27:09 Skewness
27:53 Kurtosis
28:43 Durbin-Watson - Test the Auto-correlation in residuals
32:29 Jarque-Bera(JB) and Probability of Jarque-Bera(JB) -To test the residuals normal distribution
34:18 Condition Number To test the Multi-collinearity
StatsModel OLS Video Link : • Linear Regression Stat...
#AtulPatel #MachineLearning #LinearRegression #StatsModel
Used Notebook in Video Link :
github.com/atulpatelDS/Youtub...
Пікірлер: 21
Very Helpful video. Thank you Atul.
you have put great efforts in doing this video,what i didnt understood clearly in college ,now i understood thank u for this
Thank you so much for consolidating all the information together!!
Thank you for the great explanations.
Excellent explanation!
Truly thankful
Million times thanks!
Thats just an amazing work and great effort m8!!!
You're amazing! Thanks for the material
Sir, this is an excellent walk-through on the OLS Statsmodels Summary statistics, a high-quality work from your excellent preparation. *Thank you!*
@AtulPatelds
2 жыл бұрын
Thank you
Thanks
Very good explanation👌👌
@AtulPatelds
2 жыл бұрын
Thank you
Good explanation
@AtulPatelds
3 жыл бұрын
Thank you
bahut achha explaination tha ... base line model kya hota h ? aur kaise banate ye explain kar dijiye
@AtulPatelds
3 жыл бұрын
Dear, baseline model we create using some deafult configuration and using some know feature selection and features engineering techniques. Once we create our first model using all above methods and let's say I get the 90% accuracy so we can say this accuracy will be my base line then I will try to improve our accuracy by applying in depth feature engineering and hyperparameter running and other different methods
Sir how to find a job for fresher
@AtulPatelds
2 жыл бұрын
This is not tuff buddy. Do hard practice and more focus on concepts rather than just copy paste. Try practice code by yourself.
Your AIC,BIC explanation doesn't seem right. Also @27.00 minutes, why did you select H1 , even though the value 0.068 is more than 0.05, so why did you reject null hypothesis!!