Course curriculum

  • 1
  • 2

    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
  • 3

    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
  • 4

    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
  • 5

    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
  • 6

    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
  • 7

    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
  • 8

    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