Easy Statistics: Linear and NonLinear Regression [Video]
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Free ChapterLinear Regression
 What is Easy Statistics: Linear Regression?
 What is Linear Regression?
 Learning Outcomes
 Whom is this Course for?
 Prerequisites
 Using Stata
 What is Regression Analysis?
 Get to Know About Linear Regression
 Why is Regression Analysis Useful?
 What Types of Regression Analysis Exist?
 Explaining Regression
 Lines of Best Fit
 Causality vs. Correlation
 What is Ordinary Least Squares (OLS)?
 Ordinary Least Squares (OLS) Visual â€“ Part 1
 Ordinary Least Squares (OLS) Visual â€“ Part 2
 Sum of Squares
 Best Linear Unbiased Estimator
 The Gaussâ€“Markov Assumptions
 Homoskedasticity
 No Perfect Collinearity
 Linear in Parameters
 Zero Conditional Mean
 How to Test and Correct for Endogeneity?
 The Gaussâ€“Markov Assumptions  Recap
 Stata  Applied Examples
 Final Thoughts and Tips

NonLinear Regression
 What is Easy Statistics: NonLinear Regression?
 What is NonLinear Regression?
 What are the Main Learning Outcomes?
 Whom is this Course for?
 Prerequisites
 Using Stata
 What is NonLinear Regression Analysis?
 How does NonLinear Regression Work?
 Why is NonLinear Regression analysis Useful?
 Types of NonLinear Regression models
 Maximum Likelihood
 Linear Probability Model
 The Logit and Probit Transformation
 Latent Variables
 What are Marginal Effects?
 Dummy Explanatory Variables
 Multiple NonLinear Regression
 GoodnessofFit
 A Note about Logit Coefficients
 Tips for Logit and Probit Regression
 Back to the Linear Probability Model
 Stata  Applied Logit and Probit Examples

Regression Modelling
 Introduction
 Regression Modelling  Don't Rush it
 NonLinear Shapes in Regression
 NonLinear Shapes in Regression  Practical Examples
 How to use and Interpret Interaction Effects?
 How to use and Interpret Interaction Effects?  Practical Examples
 Using Time in Regression
 Using Time in Regression  Practical Examples
 Categorical Explanatory Variables in Regression
 Categorical Explanatory Variables in Regression  Practical Examples
 Dealing with Multicollinearity in Regression
 Dealing with Multicollinearity in Regression  Practical Examples
 Dealing with Missing Data in Regression
 Dealing with Missing Data in Regression  Practical Examples
Working with statistics and quantitative reports requires a good understanding of statistics fundamentals and techniques. However, learning and applying new statistical techniques can often be a daunting experience. This is where this course comes into play.
To make your experience with statistics a pleasant one, this course gives you comprehensive knowledge of basic principles of the statistical methodology, focusing on linear regression and nonlinear regression.
The course starts with an introduction to easy statistics and gives you an overview of the course objectives. Next, you will explore the types of regression analysis that exist and find out how ordinary least squares (OLS) works. To gain a deeper understanding of linear regression and OLS, you will learn to interpret and analyze complicated regression output from OLS. You will also focus on Gauss–Markov assumptions and zero conditional mean. Moving ahead, you will cover nonlinear regression, exploring how it works, what the different nonlinear regression models are, and the major uses. Towards the end, you will learn to work around with regression modeling with the help of practical examples.
By the end of this video, you will be wellversed with linear and nonlinear regression and the basic principles of the statistical methodology.
All the resource files are added on GitHub at https://github.com/PacktPublishing/EasyStatisticsLinearandNonLinearRegression
 Publication date:
 November 2020
 Publisher
 Packt
 Duration
 5 hours 16 minutes
 ISBN
 9781800566590