Paul van Loon – CFI Education – Regression Analysis – Fundamentals & Practical Applications

Description

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Paul van Loon – CFI Education – Regression Analysis – Fundamentals & Practical Applications

Regression Analysis – Fundamentals & Practical Applications

Learn how linear regression works and how to build effective linear regression models in Excel and Python using real data.

Overview

Regression Analysis – Fundamentals & Practical Applications

Linear regression analysis is critical for understanding and defining the strength of the relationship between variables. This analysis can be used to make predictions for a variable given the value of another known variable.

This course provides an overview of linear regression. You will learn how linear regression works, how to build effective linear regression models and how to use and interpret the information these models give us. In addition to the theory, we will perform linear regression on real data using both Excel and Python. The practical cases you will work through will be similar to those you might encounter in a business setting.

Regression Analysis – Fundamentals & Practical Applications Learning Objectives

Upon completing this course, you will be able to:

• Define linear regression and its applications
• Perform simple “pen and paper” regression calculations in Excel
• Apply Excel’s RegressIt plugin to solve advanced regression calculations
• Construct linear regression models in Python using both statsmodels and sklearn modules
• Explain the implicit assumptions behind linear regression
• Interpret regression outputs such as coefficients and p-values
• Recommend various regression techniques when appropriate

Who Should Take This Course?

Regression is the critical tool used for making inferences or predictions based on the relationships between variables. Whether you’re working as a business leader or data analyst, the theory and practical toolsets taught in this course will serve you throughout your career. No background in coding with Python is required for this course.

Common career paths for students who take the BIDA™ program are Business Intelligence, Asset Management, Data Analyst, Quantitative Analyst, and other finance careers.

What you’ll learn

Introduction to Linear Regression
Course Introduction
Learning Objectives
Download Student Files

Simple Linear Regression
Chapter Introduction – Simple Linear Regression
Simple Linear Regression
The Linear Regression Equation
Ordinary Least Squares
OLS Calculation
Fitting the Parameters
Caution with Regression
Regression in Practice
Manual Regression Calcs in Excel
Regression using Excel Data Analysis
EDA Descriptive Stats with Regressit in Excel
Regression with Regressit in Excel
Regressit Scenario 2
Import Data & EDA in Python
Regression model in Python using Statsmodels
Python Ex 2 – Import Data & EDA
Python Ex 2 – Fitting the model in Statsmodels
Python Ex 2 – Plotting the results
Python Ex 3 – Import Data in Statsmodels
Python Ex 3 – Train Test Split in Statsmodels
Python Ex 3 – Plot Training Data
Python Ex 3 – Fit Regression Model
Python Ex 3 – Plot the Results
Python Ex 3 – Apply Model to test data

Multiple Linear Regression
Chapter Introduction – Multiple Linear Regression
Multiple Linear Regression
Multicollinearity
Caution with Multiple Linear Regression
Multiple Linear Regression in Excel
Load & Assess the Data in Python
Basic Multiple Regression Model in Python
Full Multiple Regression Model
Fitting the Linear Regression Model
Multiple Linear Regression Model in Scikit-Learn

Interpreting Linear Regression
Chapter Introduction – Interpreting Linear Regression
Residuals
OLS Assumptions
OLS Assumptions – Linearity
OLS Assumptions – Normal & Heteroscedastic
OLS Assumptions – Zero Mean Errors
OLS Assumptions – Endogeneity
OLS Assumptions – Autocorrelation of Errors
OLS Assumptions – Multicollinearity
Linear Regression Evaluation
Linear Regression Evaluation – Squared Error Metrics
Linear Regression Evaluation – Absolute Error Metrics
Linear Regression Evaulation – R Squared
Linear Regression Evaluation – Adjusted R Squared
Regression Coefficients
Compare Coefficients
Calculate p-values
Interactive Exercise
Interpretation Scenarios
Interpreting Linear Regression
P-values & Coefficients
Residuals & Residual Plots
Evaluating Linear Regression

Advanced Linear Regression
Chapter Introduction – Advanced Linear Regression
Log Log Linear Regression
Polynomial Regression
Logistic Regression
Repeated Measure Regression
Segmented Regression Models
Other Advanced Models
Log Log Linear Regression – Investigating Problems
Log Log Linear Regression – Plotting Logs
Log Log Linear Regression – Model Evaluation

Course Summary
Course Conclusion

Qualified Assessment
Qualified Assessment Regression

Frequently Asked Questions:

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