Minerva's Data Analysis Lab

Data For the Course

Difference Between Statistical Analysis & Machine Learning

Getting Started with R and R Studio

Reading in Data with R

Data Cleaning with R

Some More Data Cleaning with R

Basic Exploratory Data Analysis in R

Conclusion to Section 1

OLS Regression - Theory

OLS - Implementation

More on Result Interpretations

Confidence Interval - Theory

Calculate the Confidence Interval in R

Confidence Interval and OLS Regressions

Linear Regression without Intercept

Implement ANOVA on OLS Regression

Multiple Linear Regression

Multiple Linear regression with Interaction and Dummy Variables

Some Basic Conditions that OLS Models Have to Fulfill

Conclusions to Section 2

Identify Multicollinearity

Doing Regression Analyses with Correlated Predictor Variables

Principal Component Regression in R

Partial Least Square Regression in R

Ridge Regression in R

LASSO Regression

Conclusion to Section 3

Why Do Any Kind of Selection?

Select the Most Suitable OLS Regression Model

Select Model Subsets

Machine Learning Perspective on Evaluate Regression Model Accuracy

Evaluate Regression Model Performance

LASSO Regression for Variable Selection

Identify the Contribution of Predictors in Explaining the Variation in Y

Conclusions to Section 4

Data Transformations

Robust Regression-Deal with Outliers

Dealing with Heteroscedasticity

Conclusions to Section 5

What are GLMs?

Logistic regression

Logistic Regression for Binary Response Variable

Multinomial Logistic Regression

Regression for Count Data

Goodness of fit testing

Conclusions to Section 6