Applied Statistics Using Stata

Advance your expertise with multilevel, panel, and time-series analysis, and explore factor analysis, structural equation modeling, and programming in Stata. This course equips learners to handle sophisticated statistical models and automate analyses for dynamic reporting. This course covers a range of advanced statistical techniques, including multilevel and panel data analysis, time-series modeling, exploratory and confirmatory factor analysis, and structural equation modeling. Learners will also explore specialized methods such as negative binomial regression, handling missing data, and variable transformation. The course concludes with an introduction to programming in Stata and creating reproducible, dynamic reports, empowering participants to streamline and document their analytical workflows. The course integrates detailed explanations of advanced statistical models with practical Stata applications, guiding learners through complex analyses and programming tasks. Emphasis is placed on real-world scenarios and best practices for efficient, reproducible research. This course is part three of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Applied Statistics Using Stata, by Mehmet Mehmetoglu and Tor Georg Jakobsen. Copyright ©2022 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.

Explore advanced regression methods, including dummy-variable and interaction effects, and master the assumptions and diagnostics necessary for robust statistical modeling in Stata. This course deepens your understanding of regression analysis and prepares you to handle complex data relationships. This course delves into advanced regression techniques such as dummy-variable regression, interaction and moderation effects, and the critical evaluation of regression assumptions. Learners will gain expertise in diagnosing and addressing issues like multicollinearity, non-linearity, and influential observations. The course also introduces logistic regression and survival analysis, equipping participants to analyze a wide range of data types and research questions. Through a blend of conceptual explanations and practical Stata demonstrations, the course emphasizes the application of advanced regression methods and diagnostic tools. Learners build on their foundational skills to tackle more complex analytical challenges with confidence. This course is part two of a three-course Specialization designed to build a complete and cohesive understanding of the subject. While it offers valuable skills on its own, you'll gain the most benefit by progressing through all three courses as a structured learning journey. This course is based on Applied Statistics Using Stata, by Mehmet Mehmetoglu and Tor Georg Jakobsen. Copyright ©2022 by Sage Publications Limited. All rights reserved, including rights for text and data mining and training of artificial technologies or similar technologies. Published by Sage Publications Limited, London. Used by arrangement with Sage Publications Limited.