Master STATA: Complete Regression & Modeling Guide

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

In this module, we will explore the generation of random numbers and simulation techniques in STATA. You will learn how to create simulated datasets and apply Monte Carlo simulations.

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.

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 and apply both non-parametric and parametric survival models.

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.

In this module, we will focus on Difference-in-Differences analysis, a powerful tool for causal inference in observational studies. You'll learn how to estimate treatment effects.

In this module, we will explore instrumental variable regression techniques, which are crucial for addressing endogeneity in regression analysis.

In this module, we will explore epidemiological table analysis, focusing on rate data, cumulative incidence, and case-control studies.

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.

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.