What you'll get

  • Govt. Verified Certification
  • Job Credibility
  • Certification Valid for Life
  • Lifetime Access To E-learning

Exam details

  • Mode of Exam : Online
  • Duration : 1 Hour
  • Multiple Choice Questions are asked
  • No. of Questions are asked : 50
  • Passing Marks : 25 (50%)
  • There is no negative marking

Course Content

Total: 59 lectures
  • What is Easy Statistics: Linear Regression?
  • What is Linear Regression?
  • 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 – Part 1
  • Ordinary Least Squares (OLS) Visual – Part 2
  • Sum of Squares
  • Best Linear Unbiased Estimator
  • The Gauss–Markov Assumptions
  • Homoskedasticity
  • 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
  • What is Easy Statistics: Non-Linear Regression?
  • What is Non-Linear Regression?
  • Prerequisites
  • 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
  • Goodness-of-Fit
  • A Note about Logit Coefficients
  • Tips for Logit and Probit Regression
  • Back to the Linear Probability Model
  • Stata - Applied Logit and Probit Examples
  • Introduction
  • 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

Description

Statistics' linear and nonlinear concepts are essential for data scientists and statistical professionals. if you want to be a data scientist and want to be a great machine learning professional. Then this is the skill that you must carry. Our online linear and nonlinear courses will give you the same experience. With all the crucial tactics of statistics linear and non-linear systems. So do you want to learn about the statistical parts? That you will be using as a data scientist then scroll below to get the key highlights of the course.

If you enroll in this online linear and nonlinear course. Then you will understand OLS using R statistical. You will be able to apply machine learning regression models. There are many concepts like GLMS using logistic regression as binary classification. Besides this, the candidate will gain the personalized techniques. That let him know where he should use the machine learning techniques and when. If you want to understand all these linear and nonlinear concepts, then you can enroll in this course. 


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