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| 1.Get Started with Practical Regression Analysis in R/01.INTRODUCTION TO THE COURSE - The Key Concepts and Software Tools.mp4 | 115.63 MB |
| 1.Get Started with Practical Regression Analysis in R/02.Difference Between Statistical Analysis & Machine Learning.mp4 | 72.07 MB |
| 1.Get Started with Practical Regression Analysis in R/03.Getting Started with R and R Studio.mp4 | 22.20 MB |
| 1.Get Started with Practical Regression Analysis in R/04.Reading in Data with R.mp4 | 49.83 MB |
| 1.Get Started with Practical Regression Analysis in R/05.Data Cleaning with R.mp4 | 44.79 MB |
| 1.Get Started with Practical Regression Analysis in R/06.Some More Data Cleaning with R.mp4 | 28.97 MB |
| 1.Get Started with Practical Regression Analysis in R/07.Basic Exploratory Data Analysis in R.mp4 | 55.59 MB |
| 1.Get Started with Practical Regression Analysis in R/08.Conclusion to Section 1.mp4 | 5.33 MB |
| 2.Ordinary Least Square Regression Modelling/09.OLS Regression- Theory.mp4 | 27.72 MB |
| 2.Ordinary Least Square Regression Modelling/10.OLS-Implementation.mp4 | 25.54 MB |
| 2.Ordinary Least Square Regression Modelling/11.More on Result Interpretations.mp4 | 18.01 MB |
| 2.Ordinary Least Square Regression Modelling/12.Confidence Interval-Theory.mp4 | 14.98 MB |
| 2.Ordinary Least Square Regression Modelling/13.Calculate the Confidence Interval in R.mp4 | 8.12 MB |
| 2.Ordinary Least Square Regression Modelling/14.Confidence Interval and OLS Regressions.mp4 | 21.31 MB |
| 2.Ordinary Least Square Regression Modelling/15.Linear Regression without Intercept.mp4 | 9.17 MB |
| 2.Ordinary Least Square Regression Modelling/16.Implement ANOVA on OLS Regression.mp4 | 7.48 MB |
| 2.Ordinary Least Square Regression Modelling/17.Multiple Linear Regression.mp4 | 17.19 MB |
| 2.Ordinary Least Square Regression Modelling/18.Multiple Linear regression with Interaction and Dummy Variables.mp4 | 30.27 MB |
| 2.Ordinary Least Square Regression Modelling/19.Some Basic Conditions that OLS Models Have to Fulfill.mp4 | 27.59 MB |
| 2.Ordinary Least Square Regression Modelling/20.Conclusions to Section 2.mp4 | 7.97 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/21.Identify Multicollinearity.mp4 | 28.71 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/22.Doing Regression Analyses with Correlated Predictor Variables.mp4 | 14.29 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/23.Principal Component Regression in R.mp4 | 29.60 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/24.Partial Least Square Regression in R.mp4 | 19.58 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/25.Ridge Regression in R.mp4 | 20.94 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/26.LASSO Regression.mp4 | 12.58 MB |
| 3.Deal with Multicollinearity in OLS Regression Models/27.Conclusion to Section 3.mp4 | 6.05 MB |
| 4.Variable & Model Selection/28.Why Do Any Kind of Selection.mp4 | 11.61 MB |
| 4.Variable & Model Selection/29.Select the Most Suitable OLS Regression Model.mp4 | 38.77 MB |
| 4.Variable & Model Selection/30.Select Model Subsets.mp4 | 21.11 MB |
| 4.Variable & Model Selection/31.Machine Learning Perspective on Evaluate Regression Model Accuracy.mp4 | 19.43 MB |
| 4.Variable & Model Selection/32.Evaluate Regression Model Performance.mp4 | 39.65 MB |
| 4.Variable & Model Selection/33.LASSO Regression for Variable Selection.mp4 | 9.08 MB |
| 4.Variable & Model Selection/34.Identify the Contribution of Predictors in Explaining the Variation in Y.mp4 | 24.88 MB |
| 4.Variable & Model Selection/35.Conclusions to Section 4.mp4 | 4.46 MB |
| 5.Dealing with Other Violations of the OLS Regression Models/36.Data Transformations.mp4 | 23.11 MB |
| 5.Dealing with Other Violations of the OLS Regression Models/37.Robust Regression-Deal with Outliers.mp4 | 19.10 MB |
| 5.Dealing with Other Violations of the OLS Regression Models/38.Dealing with Heteroscedasticity.mp4 | 14.89 MB |
| 5.Dealing with Other Violations of the OLS Regression Models/39.Conclusions to Section 5.mp4 | 3.44 MB |
| 6.Generalized Linear Models (GLMs)/40.What are GLMs.mp4 | 12.71 MB |
| 6.Generalized Linear Models (GLMs)/41.Logistic regression.mp4 | 44.41 MB |
| 6.Generalized Linear Models (GLMs)/42.Logistic Regression for Binary Response Variable.mp4 | 31.67 MB |
| 6.Generalized Linear Models (GLMs)/43.Multinomial Logistic Regression.mp4 | 18.20 MB |
| 6.Generalized Linear Models (GLMs)/44.Regression for Count Data.mp4 | 16.06 MB |
| 6.Generalized Linear Models (GLMs)/45.Goodness of fit testing.mp4 | 87.21 MB |
| 6.Generalized Linear Models (GLMs)/46.Conclusions to Section 6.mp4 | 6.73 MB |
| 7.Working with Non-Parametric and Non-Linear Data/47.Polynomial and Non-linear regression.mp4 | 18.86 MB |
| 7.Working with Non-Parametric and Non-Linear Data/48.Generalized Additive Models (GAMs) in R.mp4 | 39.93 MB |
| 7.Working with Non-Parametric and Non-Linear Data/49.Boosted GAM Regression.mp4 | 16.50 MB |
| 7.Working with Non-Parametric and Non-Linear Data/50.Multivariate Adaptive Regression Splines (MARS).mp4 | 26.45 MB |
| 7.Working with Non-Parametric and Non-Linear Data/51.CART-Regression Trees in R.mp4 | 28.33 MB |
| 7.Working with Non-Parametric and Non-Linear Data/52.Conditional Inference Trees.mp4 | 11.71 MB |
| 7.Working with Non-Parametric and Non-Linear Data/53.Random Forest(RF).mp4 | 20.49 MB |
| 7.Working with Non-Parametric and Non-Linear Data/54.Gradient Boosting Regression.mp4 | 8.61 MB |
| 7.Working with Non-Parametric and Non-Linear Data/55.ML Model Selection.mp4 | 102.18 MB |
| 7.Working with Non-Parametric and Non-Linear Data/56.Conclusions to Section 7.mp4 | 24.93 MB |
| Exercise Files/code_9781838987862.zip | 27.97 MB |
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