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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鈥檒l 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鈥檒l 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.