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Statistics and Machine Learning For Regression Modelling With R
Welcome to Regression Modelling With R
Introduction to the Course (6:58)
Data and Code
Install R and RStudio (6:36)
Read in Data Using R (15:28)
Data Cleaning (17:12)
More Data Cleaning (8:05)
Exploratory Data Analysis (EDA) (18:53)
Conclusions to Section 1 (1:58)
Ordinary Least Square Regression
Ordinary Least Square Regression: Theory (10:44)
OLS Implementation (8:40)
Confidence Interval-Theory (6:06)
Calculate the Confidence Interval in R (4:53)
Confidence Interval and OLS Regressions (7:19)
Linear Regression without Intercept (3:40)
Implement ANOVA on OLS Regression (3:37)
Multiple Linear Regression (6:27)
Multiple Linear regression with Interaction and Dummy Variables (15:05)
Some Basic Conditions that OLS Models Have to Fulfil (12:56)
Conclusions to Section 2 (2:55)
Deal with Multicollinearity in OLS Regression Models
Identify Multicollinearity (16:42)
Doing Regression Analyses with Correlated Predictor Variables (5:36)
Principal Component Regression in R (10:39)
Partial Least Square Regression in R (7:33)
Lasso Regression in R (4:24)
Conclusions to Section 3 (2:00)
Variable & Model Selection
Why Do Any Kind of Selection? (4:40)
Select the Most Suitable OLS Regression Model (13:19)
Select Model Subsets (8:22)
Machine Learning Perspective on Evaluate Regression Model Accuracy (7:10)
Evaluate Regression Model Performance (14:26)
LASSO Regression for Variable Selection (3:42)
Identify the Contribution of Predictors in Explaining the Variation in Y (8:38)
Conclusions to Section 4 (1:35)
Dealing With Other Violations of the OLS Conditions
Data Transformations
Robust Regression: Deal With Outliers (6:58)
Deal With Heteroelasticity (7:12)
Conclusion to Section 5 (1:12)
Generalised Linear Models (GLMs)
What are GLMs? (5:25)
Implement a Logistic Regression (16:18)
More Logistic Regression (9:10)
Modelling Count Data (6:19)
Multinomial Regression (6:11)
Conclusion to Section 6 (2:12)
Non-Parametric and Machine Learning Regression
Polynomial Regression (18:19)
Generalized Additive Models (GAMs) in R (14:09)
Boosted GAM (6:15)
Multivariate Adaptive Regression Splines (MARS) (8:06)
CART For Regression (10:54)
CIR (5:45)
Random Forest (RF) Regression (11:52)
Doing Regression Analyses with Correlated Predictor Variables
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