Enrolment options
- Enrolled students: There are no students enrolled in this course.
Guests cannot access this course. Please log in.
Course content
Sections:
21
•
Activities:
0
•
Resources:
164
- 001 Introduction
- 001 What is Easy Statistics_ Linear Regression
- 002 What is Linear Regression
- 003 Learning Outcome
- 004 Who is this Course for
- 005 Pre-requisites
- 006 Using Stata
- 007 What is Regression Analysis
- 008 What is Linear Regression
- 009 Why is Regression Analysis Useful
- 010 What Types of Regression Analysis Exist
- 011 Explaining Regression
- 012 Lines of Best Fit
- 013 Causality vs Correlation
- 014 What is Ordinary Least Squares
- 015 Ordinary Least Squares Visual 1
- 016 Ordinary Least Squares Visual 2
- 017 Sum of Squares
- 018 Best Linear Unbiased Estimator
- 019 The Gauss-Markov Assumptions
- 020 Homoskedasticity
- 021 No Perfect Collinearity
- 022 Linear in Parameters
- 023 Zero Conditional Mean
- 024 How to Test and Correct for Endogeneity
- 025 The Gauss-Markov Assumptions Recap
- 026 Stata - Applied Examples
- Statistics_ Non-Linear Regression
- 002 What is Non-Linear Regression
- 003 What are the main learning outcomes
- 004 Who is this course for
- 005 Prerequisites
- 006 Using Stata
- 007 What is Non-Linear Regression analysis
- 008 How does Non-Linear Regression work
- 009 Why is Non-Linear Regression analysis useful
- 010 Types of Non-Linear Regression models
- 011 Maximum Likelihood
- 012 Linear Probability Model
- 013 The Logit and Probit Transformation
- 014 Latent Variables
- 015 What are Marginal Effects
- 016 Dummy Explanatory Variables
- 017 Multiple Non-Linear Regression
- 018 Goodness-of-Fit
- 019 A note about Logit Coefficients
- 020 Tips for Logit and Probit Regression
- 021 Back to the Linear Probability Model
- 022 Stata - Applied Logit and Probit Examples
- 04 - Regression Modelling/001 Introduction
- 002 Regression Modelling - Don't Rush It
- 003 Functional Form - How to Model Non-Linear Relationships in a Linear Regression
- 004 Functional Form - Stata Examples
- 005 Interaction Effects - How to Use and Interpret Interaction Effects
- 006 Interaction Effects - Stata Examples
- 007 Using Time - Exploring Dynamics Relationships with Time Information
- 008 Using Time - Stata Examples
- 009 Categorical Explanatory Variables - How to Code, Use and Interpret them
- 010 Categorical Explanatory Variables - Stata Examples
- 011 Dealing with Multicollinearity - Excluding and Transforming Collinear Variables
- 012 Dealing with Multicollinearity - Stata Examples
- 013 Dealing with Missing Values - Seeing the Unseeable
- 014 Dealing with Missing Values - Stata Examples
- 001 Introduction
- 002 The Stata Interface
- 003 Using Help in Stata
- 004 Command Syntax
- 005 .do and .ado Files
- 006 Log Files
- 007 Importing Data
- 001 Viewing Raw Data
- 002 Describing and Summarizing
- 003 Missing Values
- 004 Tabulating and Tables
- 005 Numerical Distributional Analysis
- 006 Using Weights
- 001 Recoding an Existing Variable
- 002 Creating New Variables, Replacing Old Variables
- 003 Naming and Labelling Variables
- 004 Extensions to Generate
- 005 Indicator Variables
- 006 Keep and Drop Data_Variables
- 007 Saving Data
- 008 Converting String Data
- 009 Combining Data
- 010 Using Macro's and Loop's Effectively
- 011 Accessing Stored Information
- 012 Multiple Loops
- 013 Date Variables
- 014 Subscripting over Groups
- 001 Graphing in Stata
- 002 Bar Graphs and Dot Charts
- 003 Graphing Distributions
- 004 Pie Charts
- 005 Scatterplots and Lines of Best Fit
- 006 Graphing Custom Functions
- 007 Contour Plots (and Interaction Effects)
- 008 Jitter Data in Scatterplots.
- 009 Sunflower Plots.
- 010 Combining Graphs
- 011 Changing Graph Sizes
- 012 Graphing by Groups
- 013 Changing Graph Colours.
- 014 Adding Text to Graphs.
- 015 Scatterplots with Categories
- 001 Association Between Two Categorical Variables
- 002 Testing Means
- 003 Bivariate Correlation
- 004 Analysis of Variance (ANOVA)
- 001 Ordinary Least Squares (OLS) Regression
- 002 Factor Variables in OLS Regression
- 003 Diagnostic Statistics for OLS Regression
- 005 Hypothesis Testing in OLS Regression
- 006 Presenting Estimates from OLS Regression
- 007 Standardizing Regression Estimates
- 008 Graphing Regression Estimates
- 009 Oaxaca Decomposition Analysis.
- 010 Mixed Models_ Random Intercepts and Random Coefficients.
- 011 Constrained Linear Regression.
- 001 Binary Choice Models (Logit_Probit Regression)
- 002 Diagnostics and Interpretation of Logit and Probit Regression
- 003 Ordered and Multinomial Choice Models
- 001 Fractional Logit, Beta Regression and Zero-inflated Beta Regression
- 001 Random Numbers
- 002 Data Generating Process.
- 003 Simulating a Violation of Statistical Assumptions.
- 004 Monte Carlo Simulation
- 001 Features of Count Data
- 002 Poisson Regression.
- 003 Negative Binomial Regression
- 004 Truncated and Censored Count Regression
- 005 Hurdle Count Regression
- 001 What is Survival Analysis_
- 002 Setting up Survival Data
- 003 Descriptive Statistics in Survival Data
- 004 Non-parametric Survival Analysis
- 005 Cox Proportional Hazards Model
- 006 Diagnostics for Cox Models
- 007 Parametric Survival Analysis
- 001 Setting up Panel Data
- 002 Panel Data Descriptives
- 003 Lags and Leads
- 004 Linear Panel Estimators
- 005 The Hausman Test
- 006 Non-Linear Panel Estimators
- 001 Difference-in-Differences Estimation
- 002 Parellel Trend Assumption
- 003 Difference-in-Differences without Parallel Trends
- 001 Instrumental Variable Regression
- 002 Multiple Endogenous Variables
- 003 Non-linear Instrumental Variable Regression
- 004 Heckman Selection Models
- 001 Introduction and Rate Data
- 002 Cumulative Incidence Data
- 003 Case-Control Data
- 004 Case-Control Data with Multiple Exposure
- 005 Matched Case-Control Data
- 001 Power Analysis_ Sample Size
- 002 Power Analysis_ Power and Effect Size
- 003 Power Analysis_ Simple Regression
- 001 Matrix Operations
- 002 Matrix Functions
- 003 Matrix Subscripting
- 004 Matrix Operations with Data