# Course curriculum

• 1

### Module 01: Get Started with Practical Regression Analysis 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
• 2

### Module 02: Ordinary Least Square Regression Modelling

• 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
• 3

### Module 03: Deal with Multicollinearity in OLS Regression Models

• 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
• 4

### Module 04: Variable and Model Selection

• 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
• 5

### Module 05: Dealing With Other Violations of the OLS Regression Models

• Segment - 35 - Robust Regression-Deal with Outliers
• Segment - 36 - Dealing with Heteroscedasticity
• Segment - 37 - Conclusion
• 6

### Module 06: Generalized Linear Models(GLMs)

• 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
• 7

### Module 07: Working with Non-Parametric and Non-Linear Data

• Segment - 44 - Generalized Additive Models (GAMs) in R
• Segment - 45 - Boosted GAM Regression
• Segment - 46 - Multivariate Adaptive Regression Splines (MARS)
• Segment - 47 - CART-Regression Trees in R
• Segment - 48 - Conditional Inference Trees
• Segment - 49 - Random Forest (RF)
• Segment - 50 - Gradient Boosting Regression
• Segment - 51 - ML Model Selection