Develop linear and non-linear regression skills and gain the confidence to work with quantitative analysis
About This Video
- 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
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.
Who this book is for
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.
Table of contents
Chapter 1 : Linear Regression
- What is Easy Statistics: Linear Regression?
- What is Linear Regression?
- Learning Outcomes
- Whom is this Course for?
- 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 - Part 1
- Ordinary Least Squares (OLS) Visual - Part 2
- Sum of Squares
- Best Linear Unbiased Estimator
- The Gauss-Markov Assumptions
- No Perfect Collinearity
- Linear in Parameters
- Zero Conditional Mean
- How to Test and Correct for Endogeneity?
- The Gauss-Markov Assumptions - Recap
- Stata - Applied Examples
- Final Thoughts and Tips
Chapter 2 : Non-Linear Regression
- What is Easy Statistics: Non-Linear Regression?
- What is Non-Linear Regression?
- What are the Main Learning Outcomes?
- Whom is this Course for?
- Using Stata
- What is Non-Linear Regression Analysis?
- How does Non-Linear Regression Work?
- Why is Non-Linear Regression analysis Useful?
- Types of Non-Linear Regression models
- Maximum Likelihood
- Linear Probability Model
- The Logit and Probit Transformation
- Latent Variables
- What are Marginal Effects?
- Dummy Explanatory Variables
- Multiple Non-Linear Regression
- A Note about Logit Coefficients
- Tips for Logit and Probit Regression
- Back to the Linear Probability Model
- Stata - Applied Logit and Probit Examples
Chapter 3 : Regression Modelling
- Regression Modelling - Don't Rush it
- Non-Linear Shapes in Regression
- Non-Linear 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
- Title: Easy Statistics: Linear and Non-Linear Regression
- Release date: November 2020
- Publisher(s): Packt Publishing
- ISBN: 9781800566590
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