This course is your comprehensive guide to mastering regression analysis and modeling using STATA. Starting with an introduction to the basics of linear regression, it takes you through essential concepts such as ordinary least squares, best linear unbiased estimators, and the crucial Gauss-Markov assumptions. You will also explore the difference between causality and correlation, learning how to apply these concepts practically in STATA with real-world examples. By the end of the linear regression module, you’ll be equipped with a deep understanding of regression analysis fundamentals.
Moving beyond linear regression, the course delves into non-linear regression analysis, providing a robust framework for more advanced statistical modeling. You will gain expertise in models such as logit and probit transformations, maximum likelihood estimation, and techniques for managing multiple non-linear regression variables. Practical examples with STATA are woven throughout, ensuring that your learning is as practical as it is theoretical.
The course rounds off with regression modeling strategies, including managing multicollinearity, handling missing values, and working with categorical explanatory variables. You’ll also explore dynamic relationships using time-based data and understand how to interpret regression outputs effectively. This training is packed with applied STATA demonstrations, allowing you to master both the technical and interpretative aspects of regression modeling.
This course is designed for statisticians, data analysts, econometricians, and researchers. A basic understanding of statistics is required, with some familiarity with regression analysis and statistical software being advantageous.
Overview
Syllabus
- Introduction
- In this module, we will introduce STATA as a powerful tool for statistical analysis and data modeling. You'll gain a basic understanding of its interface and features, setting the stage for the advanced regression techniques covered in later sections.
- Linear Regression
- In this module, we will explore the essentials of linear regression, focusing on its importance in data analysis and statistical modeling. You will learn about key concepts such as the lines of best fit, OLS estimation, and the Gauss-Markov assumptions, with practical examples in STATA to reinforce the theoretical knowledge.
- Non-Linear Regression
- In this module, we will dive into the world of non-linear regression, explaining its importance in modeling complex relationships. You will explore techniques like maximum likelihood estimation, Logit, and Probit models, along with practical STATA applications to handle real-world, non-linear data scenarios.
- Regression Modelling
- In this module, we will focus on refining your regression modeling skills. You will learn how to model non-linear relationships, apply interaction effects, and incorporate time dynamics in regression models. Additionally, we’ll address practical challenges such as multicollinearity and missing data, providing hands-on STATA examples for each concept.
- Introduction to Stata - Getting Started
- In this module, we will introduce you to the fundamentals of using STATA. You will become familiar with its interface, learn how to use the help function, and understand the basic command syntax. Additionally, we will cover essential file management practices like working with .do files and importing data for your analyses.
- Introduction to Stata - Exploring Data
- In this module, we will explore techniques to effectively view and summarize raw data in STATA. You'll learn how to deal with missing values, create tables, and conduct distributional analysis. Additionally, we will introduce the use of weights to refine data interpretation in statistical modeling.
- Introduction to Stata - Manipulating Data
- In this module, we will cover essential techniques for manipulating data in STATA. You'll learn how to recode, generate, and label variables, handle string data, and combine datasets. Additionally, we will explore the use of macros, loops, and subscripting over groups to streamline your data management workflow.
- Introduction to Stata - Visualizing Data
- In this module, we will focus on visualizing data using STATA's powerful graphing tools. You will learn how to create different types of charts and graphs, customize their appearance, and combine them for clearer data interpretation. Special topics include graphing distributions, jittering scatter plots, and visualizing interaction effects.
- Introduction to Stata: Testing Means, Correlations, and Analysis of Variance (ANOVA)
- In this module, we will examine techniques for testing relationships and differences in data. You'll learn how to test associations between categorical variables, conduct mean comparison tests, and explore correlations. We will also cover ANOVA to help you analyze variance across different groups using STATA.
- Introduction to Stata: Linear Regression
- In this module, we will explore linear regression in depth, focusing on OLS regression, factor variables, and hypothesis testing. You'll learn how to present and graph regression estimates, as well as apply advanced methods like Oaxaca decomposition and constrained linear regression. Practical examples in STATA will reinforce these concepts.
- Introduction to Stata: Categorical Choice Model
- In this module, we will focus on categorical choice models, starting with binary choice models like Logit and Probit regression. You'll learn how to perform diagnostics and interpret the outputs, and explore more advanced techniques such as ordered and multinomial choice models for multi-category outcomes.
- Fractional/Proportional Variable Models
- In this module, we will examine models designed for fractional and proportional variables. You will learn how to implement fractional logit and beta regression in STATA, along with zero-inflated beta regression for datasets containing many zero values, providing a robust approach to analyzing proportion data.
- Introduction to Stata: Random Numbers and Simulation
- In this module, we will explore the generation of random numbers and simulation techniques in STATA. You will learn how to create simulated datasets, analyze violations of key statistical assumptions, and apply Monte Carlo simulations to assess model behavior under various scenarios.
- Introduction to Stata: Count Data Models
- In this module, we will delve into count data models, exploring methods to analyze outcomes that represent counts. You’ll learn how to use Poisson and Negative Binomial regressions, along with specialized techniques like truncated, censored, and hurdle count regression to manage complex count data in STATA.
- Introduction to Stata: Survival Analysis
- In this module, we will introduce survival analysis, a key method for analyzing time-to-event data. You'll learn how to prepare survival datasets, perform descriptive statistics, and apply both non-parametric and parametric survival models. The module also covers Cox Proportional Hazards models and diagnostics to evaluate model performance in STATA.
- Introduction to Stata: Panel Data
- In this module, we will explore panel data analysis, focusing on handling data across time and individuals. You’ll learn how to prepare panel datasets, use lags and leads, and apply both linear and non-linear panel estimators. Additionally, we will cover the Hausman test to determine the appropriate model for your data.
- Introduction to Stata: Difference-In-Differences Analysis
- In this module, we will focus on Difference-in-Differences (DiD) analysis, a powerful tool for causal inference in observational studies. You’ll learn how to estimate treatment effects, examine the parallel trend assumption, and apply alternative methods when this assumption does not hold, all within the STATA environment.
- Introduction to Stata: Instrumental Variable Regression
- In this module, we will explore instrumental variable regression techniques, which are crucial for addressing endogeneity in regression analysis. You’ll learn how to implement IV regression, manage models with multiple endogenous variables, and apply non-linear IV regression. We will also cover Heckman selection models to account for sample selection bias in STATA.
- Epidemiological Tables
- In this module, we will explore epidemiological table analysis, focusing on rate data, cumulative incidence, and case-control studies. You’ll learn how to handle different types of case-control data, including those with multiple exposures and matched designs, using STATA’s epidemiological tools.
- Introduction to Stata: Power Analysis
- In this module, we will focus on the essentials of power analysis, a critical step in study design. You will learn how to calculate required sample sizes, understand the role of power and effect size in statistical testing, and apply these concepts in the context of simple regression analysis using STATA.
- Introduction to Stata: Basic Matrix Operations
- In this module, we will introduce basic matrix operations in STATA, essential for more advanced statistical analysis. You will learn how to execute matrix functions, utilize sub-scripting, and apply these operations to real data, enhancing your ability to handle complex data structures efficiently.
Taught by
Packt - Course Instructors
