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What you'll get

  • Job Credibility
  • Certification Valid for Life
  • Live Classes
  • Certificate of Completion

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

Do you want to take an online SATA course to get complete information about regression and nonlinear regression work? Then stay here and take this course. The reason is it contains each essential curriculum that you need to know about regression analysis. This course aims to teach you about regression interpretation by using various methods that suit you the most. This STATA OMNIBUS course helps students understand animated graphics. So that to demonstrate any statistical problem and help their organization or where they work. In short, this online program is designed to help you in learning about SATA and its various use so that you can work and use modern data analysis. You will learn all the terms and methods that you need to know as per current industry need.

What will you learn in this course?

  • You will start learning from the basic concepts of statistics.
  • Then you will learn about the general terminologies and assumptions. 
  • We will tell you about the linear and nonlinear regression analysis in detail.
  • During the course, you will also understand regression modeling. 
  • Asides from this, we will give you essential and comprehensive information about SATA.

During this SATA online course, you will also understand the concepts of exploring, manipulating, and visualizing data with all the required tips and tricks to do this efficiently.


Course Content

Total: 161 lectures
  • Introduction
  • What are Easy Statistics: Linear Regression?
  • What is Linear Regression?
  • Prerequisites
  • Using Stata
  • What is Regression Analysis?
  • What is Linear Regression?
  • Why is Regression Analysis Useful?
  • What Types of Regression Analysis Exist?
  • Explaining Regression
  • Lines of Best Fit
  • Causality Versus Correlation
  • What are Ordinary Least Squares (OLS)?
  • Ordinary Least Squares Visual – Part 1
  • Ordinary Least Squares Visual – Part 2
  • Sum of Squares
  • Best Linear Unbiased Estimator
  • The Gauss-Markov Assumptions
  • Homoskedasticity
  • No Perfect Collinearity
  • Linearity in Parameters
  • Zero Conditional Mean
  • How to Test and Correct Endogeneity?
  • The Gauss-Markov Assumptions Recap
  • Stata - Applied Examples
  • What are 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 (LPM)
  • 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 – Do not Rush it
  • Functional Form - How to Model Non-Linear Relationships in Linear Regression?
  • Functional Form - Stata Examples
  • Interaction Effects - How to Use and Interpret Interaction Effects?
  • Interaction Effects - Stata Examples
  • Using Time - Exploring Dynamics Relationships with Time Information
  • Using Time - Stata Examples
  • Categorical Explanatory Variables - How to Code, Use, and Interpret them?
  • Categorical Explanatory Variables - Stata Examples
  • Dealing with Multicollinearity - Excluding and Transforming Collinear Variables
  • Dealing with Multicollinearity - Stata Examples
  • Dealing with Missing Values - Seeing the Unseeable
  • Dealing with Missing Values - Stata Examples
  • Introduction
  • The Stata Interface
  • Using Help in Stata
  • Command Syntax
  • .do and .ado Files
  • Log Files
  • Importing Data
  • Viewing Raw Data
  • Describing and Summarizing
  • Missing Values
  • Tabulating and Tables
  • Numerical Distributional Analysis
  • Using Weights
  • Recoding an Existing Variable
  • Creating New Variables, Replacing Old Variables
  • Naming and Labelling Variables
  • Extensions to Generate
  • Indicator Variables
  • Keep and Drop Data/Variables
  • Saving Data
  • Converting String Data
  • Combining Data
  • Using Macros and Loops Effectively
  • Accessing Stored Information
  • Multiple Loops
  • Date Variables
  • Subscripting Over Groups
  • Graphing in Stata
  • Bar Graphs and Dot Charts
  • Graphing Distributions
  • Pie Charts
  • Scatter Plots and Lines of Best Fit
  • Graphing Custom Functions
  • Contour Plots (and Interaction Effects)
  • Jitter Data in Scatterplots
  • Sunflower Plots
  • Combining Graphs
  • Changing Graph Sizes
  • Graphing by Groups
  • Changing Graph Colors
  • Adding Text to Graphs
  • Scatterplots with Categories
  • Association Between Two Categorical Variables
  • Testing Means
  • Bivariate Correlation
  • Analysis of Variance (ANOVA)
  • Ordinary Least Squares (OLS) Regression
  • Factor Variables in Ordinary Least Squares (OLS) Regression
  • Diagnostic Statistics for Ordinary Least Squares (OLS) Regression
  • Log Dependent Variables and Interaction Effects in Ordinary Least Squares (OLS) Regression
  • Hypothesis Testing in Ordinary Least Squares (OLS) Regression
  • Presenting Estimates from Ordinary Least Squares (OLS) Regression
  • Standardizing Regression Estimates
  • Graphing Regression Estimates
  • Oaxaca Decomposition Analysis
  • Mixed Models: Random Intercepts and Random Coefficients
  • Constrained Linear Regression
  • Binary Choice Models (Logit/Probit Regression)
  • Diagnostics and Interpretation of Logit and Probit Regression
  • Ordered and Multinomial Choice Models
  • Fractional Logit, Beta Regression, and Zero-Inflated Beta Regression
  • Random Numbers
  • Data Generating Process
  • Simulating a Violation of Statistical Assumptions
  • Monte Carlo Simulation
  • Features of Count Data
  • Poisson Regression
  • Negative Binomial Regression
  • Truncated and Censored Count Regression
  • Hurdle Count Regression
  • What is Survival Analysis?
  • Setting Up Survival Data
  • Descriptive Statistics in Survival Data
  • Non-Parametric Survival Analysis
  • Cox Proportional Hazard’s Model
  • Diagnostics for Cox Models
  • Parametric Survival Analysis
  • Setting up Panel Data
  • Panel Data Descriptive
  • Lags and Leads
  • Linear Panel Estimators
  • The Hausman Test
  • Non-Linear Panel Estimators
  • Difference-In-Differences Estimation
  • Parallel Trend Assumption
  • Difference-In-Differences without Parallel Trends
  • Instrumental Variable Regression
  • Multiple Endogenous Variables
  • Non-Linear Instrumental Variable Regression
  • Heckman Selection Models
  • Introduction and Rate Data
  • Cumulative Incidence Data
  • Case-Control Data
  • Case-Control Data with Multiple Exposure
  • Matched Case-Control Data
  • Power Analysis: Sample Size
  • Power Analysis: Power and Effect Size
  • Power Analysis: Simple Regression
  • Matrix Operations
  • Matrix Functions
  • Matrix Sub-Scripting
  • Matrix Operations with Data

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