Description

This course covers the fundamental topics of statistical methodology, enabling you to understand the application and interpretation of linear and non-linear regression modeling.

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 non-linear 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 non-linear regression, exploring how it works, what the different non-linear 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 well-versed with linear and non-linear regression and the basic principles of the statistical methodology. All the resource files are added on GitHub at https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression

What You Will Learn

Understand the basic concept of statistical regression analysis
Become familiar with linear and non-linear regression terminologies
Distinguish between different types of regression methods
Analyze and integrate complicated regression output from ordinary least squares (OLS)
Find out the difference between logit and probit transformation
Model non-linear relationships in a linear regression

Audience

If you're a student, an experienced professional, a manager, or a government worker who wants to learn linear and non-linear regression, regression modeling, and ordinary least squares, then this course is for you. This is a beginner-level course and does not require any prior knowledge of mathematics or statistics.

Approach

With the help of eye-catching animated graphics and interesting examples, this course will teach you linear and non-linear regression and regression modeling to ensure that you become well-versed with the fundamentals of statistics and quantitative analysis.

Key Features

Understand the statistical fundamentals of ordinary least squares (OLS) * Gain the confidence to comfortably interpret complicated regression output from OLS * Explore regression modeling and its application

Github Repo

https://github.com/PacktPublishing/-Easy-Statistics-Linear-and-Non-Linear-Regression

About the Author

Franz Buscha

Franz Buscha is a professor of economics at the University of Westminster, which he joined after completing his Ph.D. in economics at Lancaster University. He has been involved in numerous funded research projects from research councils and government departments. He has also contributed to a wide range of projects, including policy evaluation and bespoke econometric advice to UK government departments. Franz has published in leading journals and contributed to numerous policy reports. His research has even been covered by various media outlets. He is an experienced online educator and has published several online courses, including LinkedIn Learning. Franz also has a monthly radio program called Policy Matters on Share Radio.

Course Outline

1. Linear Regression

1. What is Easy Statistics: Linear Regression?

This video explains the concept of easy statistics with linear regression.

2. What is Linear Regression?

This video explains the concept of linear regression.

3. Learning Outcomes

This video explains the various learning outcomes.

4. Whom is this Course for?

This video discusses the target audience for this course.

5. Prerequisites

This video explains the various prerequisites of the course.

6. Using Stata

This video shows how to use Stata for linear regression.

7. What is Regression Analysis?

This video explains the concept of regression analysis.

8. Get to Know About Linear Regression

This video explains the concept of linear regression in detail.

9. Why is Regression Analysis Useful?

This video explains the importance and uses of regression analysis.

10. What Types of Regression Analysis Exist?

This video explains the various types of regression analysis.

11. Explaining Regression

This video explains the concept of regression.

12. Lines of Best Fit

This video explains the lines of best fit concept.

13. Causality vs. Correlation

This video illustrates the difference between causality and correlation.

14. What is Ordinary Least Squares (OLS)?

This video explains the concept of OLS.

15. Ordinary Least Squares (OLS) Visual - Part 1

This video is the first part of the two-part video that explains OLS visually.

16. Ordinary Least Squares (OLS) Visual - Part 2

This video is the second part of the two-part video that explains OLS visually.

17. Sum of Squares

This video explains how to calculate the sum of squares using linear regression.

18. Best Linear Unbiased Estimator

This video explains the best linear unbiased estimator.

19. The Gauss-Markov Assumptions

This video discusses the Gauss-Markov assumptions.

20. Homoskedasticity

This video explains the concept of homoskedasticity.

21. No Perfect Collinearity

This video explains the concept of no perfect collinearity.

22. Linear in Parameters

This video focuses on linearity in parameters.

23. Zero Conditional Mean

This video explains what is zero conditional mean.

24. How to Test and Correct for Endogeneity?

This video explains how to test and correct for endogeneity.

25. The Gauss-Markov Assumptions - Recap

This video presents a recap of the Gauss-Markov assumptions.

26. Stata - Applied Examples

This video explains with an example how to use Stata.

27. Final Thoughts and Tips

This video provides some final thoughts and tips with respect to linear regression.

2. Non-Linear Regression

1. What is Easy Statistics: Non-Linear Regression?

This video explains the concept of easy statistics with non-linear regression.

2. What is Non-Linear Regression?

This video explains the concept of non-linear regression.

3. What are the Main Learning Outcomes?

This video presents the learning outcome of this course.

4. Whom is this Course for?

This video discusses the target audience for this course.

5. Prerequisites

This video discusses the various prerequisites for this course.

6. Using Stata

This video explains how to use Stata.

7. What is Non-Linear Regression Analysis?

This video explains the concept of non-linear regression analysis.

8. How does Non-Linear Regression Work?

This video explains the working of non-linear regression.

9. Why is Non-Linear Regression analysis Useful?

This video explains the importance and uses of non-linear regression analysis.

10. Types of Non-Linear Regression models

This video explains the various types of non-linear regression models.

11. Maximum Likelihood

This video explains the maximum likelihood estimation technique.

12. Linear Probability Model

This video explains the linear probability model.

13. The Logit and Probit Transformation

This video explains the logit and probit transformation model.

14. Latent Variables

This video focuses on latent variables.

15. What are Marginal Effects?

This video explains the concept of marginal effects.

16. Dummy Explanatory Variables

This video focuses on the dummy explanatory variables.

17. Multiple Non-Linear Regression

This video explains multiple non-linear regression.

18. Goodness-of-Fit

This video discusses the concept of goodness-of-fit.

19. A Note about Logit Coefficients

This video provides a note about logit coefficients.

20. Tips for Logit and Probit Regression

This video provides some tips for logit and probit regression.

21. Back to the Linear Probability Model

This video explains the linear probability model.

22. Stata - Applied Logit and Probit Examples

This video provides examples of applied logit and probit using Stata.

3. Regression Modelling

1. Introduction

This video provides an introduction to regression modelling.

2. Regression Modelling - Don't Rush it

This video explains the concept of regression modelling in detail.

3. Non-Linear Shapes in Regression

This video explains the concept of non-linear shapes in regression.

4. Non-Linear Shapes in Regression - Practical Examples

This video explains the concept of non-linear shapes in regression with the help of examples.

5. How to use and Interpret Interaction Effects?

This video explains how to use and interpret interaction effects.

6. How to use and Interpret Interaction Effects? - Practical Examples

This video explains how to use and interpret interaction effects with the help of examples.

7. Using Time in Regression

This video explains how to use time in regression.

8. Using Time in Regression - Practical Examples

This video explains how to use time in regression with the help of examples.

9. Categorical Explanatory Variables in Regression

This video explains the categorical explanatory variables in regression.

10. Categorical Explanatory Variables in Regression - Practical Examples

This video explains the categorical explanatory variables in regression with the help of examples.

11. Dealing with Multicollinearity in Regression

This video explains how to deal with multicollinearity in regression.

12. Dealing with Multicollinearity in Regression - Practical Examples

This video explains how to deal with multicollinearity in regression with the help of examples.

13. Dealing with Missing Data in Regression

This video explains how to deal with missing data in regression.

14. Dealing with Missing Data in Regression - Practical Examples

This video explains how to deal with missing data in regression with the help of examples.

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Easy Statistics: Linear and Non-Linear Regression

By Packt

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