Overview

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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.

Syllabus

Dummy-Variable Regression
- This module covers the fundamentals of dummy-variable regression, including how to compare group means, interpret F-tests, and apply regression techniques with categorical variables. Learners will explore practical applications through Stata, understanding the relationship between regression and ANOVA/ANCOVA. The module equips students with the skills to analyze data with multiple categorical predictors.
Interaction/Moderation Effects Using Regression
- This module covers the fundamentals of interaction and moderation effects in regression analysis, including how to model and interpret these effects using different variable types. Learners will gain hands-on knowledge of the product-term approach and explore how variables interact in real-world data scenarios. The module also explains the importance of variable centering and scaling in improving model interpretation.
Linear Regression Assumptions and Diagnostics
- This module covers essential assumptions of linear regression, including linearity, absence of multicollinearity, and proper error distribution. Learners will gain practical skills in diagnosing and addressing common regression issues using Stata, ensuring more accurate and reliable model results.
Logistic Regression
- This module covers the fundamentals of logistic regression, including its application for different types of outcomes, model interpretation, and comparison with alternative methods like probit and ordinal logistic regression. Learners will gain skills in analyzing data using Stata, understanding statistical assumptions, and interpreting complex regression outputs.
Survival Analysis
- This module introduces the fundamentals of survival analysis, including life tables, hazard functions, and proportional hazard models. Learners will gain the ability to analyze event timing and risk factors, as well as understand the impact of censoring and covariates on survival data.