Course curriculum

    1. Segment - 01 - Introduction

    2. Segment - 02 - Getting Started with R and R Studio

    3. Segment - 03 - Reading in Data with R

    4. Segment - 04 - Data Cleaning with R

    5. Segment - 05 - Some More Data Cleaning with R

    6. Segment - 06 - Basic Exploratory Data Analysis in R

    7. Segment - 07 - Conclusion

    1. Segment - 08 - OLS Regression - Theory

    2. Segment - 09 - OLS - Implementation

    3. Segment - 10 - More on Result Interpretations

    4. Segment - 11 - Confidence Interval - Theory

    5. Segment - 12 - Calculate the Confidence Interval in R

    6. Segment - 13 - Confidence Interval and OLS Regressions

    7. Segment - 14 - Linear Regression without Intercept

    8. Segment - 15 - Implement ANOVA on OLS Regression

    9. Segment - 16 - Multiple Linear Regression

    10. Segment - 17 - Multiple Linear regression with Interaction and Dummy Variables

    11. Segment - 18 - Some Basic Conditions that OLS Models Have to Fulfill

    12. Segment - 19 - Conclusion

    1. Segment - 20 - Identify Multicollinearity

    2. Segment - 21 - Doing Regression Analyses with Correlated Predictor Variables

    3. Segment - 22 - Principal Component Regression in R

    4. Segment - 23 - Partial Least Square Regression in R

    5. Segment - 24 - Ridge Regression in R

    6. Segment - 25 - LASSO Regression

    7. Segment - 26 - Conclusion

    1. Segment - 27 - Why Do Any Kind of Selection?

    2. Segment - 28 - Select the Most Suitable OLS Regression Model

    3. Segment - 29 - Select Model Subsets

    4. Segment - 30 - Machine Learning Perspective on Evaluate Regression Model Accuracy

    5. Segment - 31 - Evaluate Regression Model Performance

    6. Segment - 32 - LASSO Regression for Variable Selection

    7. Segment - 33 - Identify the Contribution of Predictors in Explaining the Variation in Y

    8. Segment - 34 - Conclusion

    1. Segment - 35 - Robust Regression-Deal with Outliers

    2. Segment - 36 - Dealing with Heteroscedasticity

    3. Segment - 37 - Conclusion

    1. Segment - 38 - What are GLMs?

    2. Segment - 39 - Logistic regression

    3. Segment - 40 - Logistic Regression for Binary Response Variable

    4. Segment - 41 -Multinomial Logistic Regression

    5. Segment - 42 - Regression for Count Data

    6. Segment - 43 - Conclusion

About this course

  • Free
  • 51 lessons
  • 7 hours of video content