Statistical and Machine Learning For Regression Modelling in R
Learn Complete Hands-On Regression Analysis for Practical Statistical Modelling and Machine Learning in R
Segment - 01 - Introduction
Segment - 02 - Getting Started with R and R Studio
Segment - 03 - Reading in Data with R
Segment - 04 - Data Cleaning with R
Segment - 05 - Some More Data Cleaning with R
Segment - 06 - Basic Exploratory Data Analysis in R
Segment - 07 - Conclusion
Segment - 08 - OLS Regression - Theory
Segment - 09 - OLS - Implementation
Segment - 10 - More on Result Interpretations
Segment - 11 - Confidence Interval - Theory
Segment - 12 - Calculate the Confidence Interval in R
Segment - 13 - Confidence Interval and OLS Regressions
Segment - 14 - Linear Regression without Intercept
Segment - 15 - Implement ANOVA on OLS Regression
Segment - 16 - Multiple Linear Regression
Segment - 17 - Multiple Linear regression with Interaction and Dummy Variables
Segment - 18 - Some Basic Conditions that OLS Models Have to Fulfill
Segment - 19 - Conclusion
Segment - 20 - Identify Multicollinearity
Segment - 21 - Doing Regression Analyses with Correlated Predictor Variables
Segment - 22 - Principal Component Regression in R
Segment - 23 - Partial Least Square Regression in R
Segment - 24 - Ridge Regression in R
Segment - 25 - LASSO Regression
Segment - 26 - Conclusion
Segment - 27 - Why Do Any Kind of Selection?
Segment - 28 - Select the Most Suitable OLS Regression Model
Segment - 29 - Select Model Subsets
Segment - 30 - Machine Learning Perspective on Evaluate Regression Model Accuracy
Segment - 31 - Evaluate Regression Model Performance
Segment - 32 - LASSO Regression for Variable Selection
Segment - 33 - Identify the Contribution of Predictors in Explaining the Variation in Y
Segment - 34 - Conclusion
Segment - 35 - Robust Regression-Deal with Outliers
Segment - 36 - Dealing with Heteroscedasticity
Segment - 37 - Conclusion
Segment - 38 - What are GLMs?
Segment - 39 - Logistic regression
Segment - 40 - Logistic Regression for Binary Response Variable
Segment - 41 -Multinomial Logistic Regression
Segment - 42 - Regression for Count Data
Segment - 43 - Conclusion