Modules

Course Length: 5 Hours

Chapter 01: Introduction

You, This Course, & Us!

Chapter 02: Connect the Dots with Linear Regression

Using Linear Regression to Connect the Dots
Two Common Applications of Regression
Extending Linear Regression to Fit Non-linear Relationships

Chapter 03: Basic Statistics Used for Regression

Understanding Mean & Variance
Understanding Random Variables
The Normal Distribution

Chapter 04: Simple Regression

Setting up a Regression Problem
Using Simple Regression to Explain Cause-Effect Relationships
Using Simple Regression for Explaining Variance
Using Simple Regression for Prediction
Interpreting the results of a Regression
Mitigating Risks in Simple Regression

Chapter 05: Applying Simple Regression

Applying Simple Regression in Excel
Applying Simple Regression in R
Applying Simple Regression in Python

Chapter 06: Multiple Regression

Introducing Multiple Regression
Some Risks inherent to Multiple Regression
Benefits of Multiple Regression
Introducing Categorical Variables
Interpreting Regression results – Adjusted R-squared
Interpreting Regression results – Standard Errors of Coefficients
Interpreting Regression results – t-statistics & p-values
Interpreting Regression results – F-Statistic

Chapter 07: Applying Multiple Regression using Excel

Implementing Multiple Regression in Excel
Implementing Multiple Regression in R
Implementing Multiple Regression in Python

Chapter 08: Logistic Regression for Categorical Dependent Variables

Understanding the need for Logistic Regression
Setting up a Logistic Regression problem
Applications of Logistic Regression
The link between Linear & Logistic Regression
The link between Logistic Regression & Machine Learning

Chapter 09: Solving Logistic Regression

Understanding the intuition behind Logistic Regression & the S-curve
Solving Logistic Regression using Maximum Likelihood Estimation
Solving Logistic Regression using Linear Regression
Binomial vs Multinomial Logistic Regression

Chapter 10: Solving Logistic Regression

Predict Stock Price movements using Logistic Regression in Excel
Predict Stock Price movements using Logistic Regression in R
Predict Stock Price movements using Rule-based & Linear Regression
Predict Stock Price movements using Logistic Regression in Python