Regression Modeling Fundamentals

Go to Course: https://www.coursera.org/learn/regression-modeling-sas

Introduction

### Course Review: Regression Modeling Fundamentals on Coursera If you’re looking to enhance your statistical analysis skills using SAS, the course "Regression Modeling Fundamentals" on Coursera is a particularly valuable resource for SAS/STAT software users. This introductory course delves into essential statistical techniques, making it an ideal starting point for those new to regression modeling and hypothesis testing. Here’s a comprehensive review of what you can expect from this course. #### Course Overview The course kicks off with a refresher on fundamental concepts from an introductory statistics course, specifically focusing on hypothesis testing. This module sets the stage by helping learners familiarize themselves with the dataset they will analyze throughout the course. Preparing data for practice and analysis is critical to building a strong foundation, and this module adeptly addresses that. #### Week-by-Week Breakdown 1. **Model Building and Effect Selection:** This module is particularly enlightening as it introduces various tools for model selection, enabling participants to streamline their focus on candidate models. By leveraging prior knowledge and research priorities, learners are guided through the process of choosing the most suitable model. The emphasis on qualitative decision-making equips students with the skills needed to navigate complex datasets confidently. 2. **Model Post-Fitting for Inference:** Once the models are fitted, the next logical step is to ensure their validity. This module covers the crucial aspect of model diagnostics, helping you verify assumptions and identify potential issues like outliers and collinearity. Understanding how to examine residuals and diagnose problems critically enhances your analytical capabilities and ensures more reliable results in your interpretive work. 3. **Model Building for Scoring and Prediction:** Shifting gears from inferential statistics to predictive modeling, this module broadens the learner's toolbox. Instead of relying solely on p-values, the course introduces the concept of model assessment through honest evaluation, which is crucial for making accurate predictions. This lesson culminates in strategies for deploying models for future data predictions, which is essential for practical applications in various industries. 4. **Categorical Data Analysis:** The final module tackles categorical data analysis, particularly focusing on logistic regression. You'll learn to explore relationships between predictors and binary responses through hypothesis tests and ultimately build your logistic regression model. This is an exciting segment of the course that emphasizes both classification and prediction, providing a crucial skill set for working in data science and analytics. #### Why You Should Enroll "Regression Modeling Fundamentals" is not just a course; it’s an investment in your analytical future. Here are a few reasons why I highly recommend it: - **Comprehensive Content:** The course covers fundamental statistical methods vital for anyone involved in data analysis. It emphasizes practical skills that can be directly applied to real-world scenarios. - **Practical Approach:** Using SAS/STAT software, the course provides hands-on experiences that help bridge the gap between theory and application. You’ll work with datasets to gain direct experience with the methodologies discussed. - **Expert Instruction:** The course is designed with clarity in mind and is facilitated by seasoned professionals in the field. Their knowledge and experience ensure that you understand not just the "how" but also the "why" behind each technique. - **Flexible Learning:** Being hosted on Coursera allows flexibility in learning, making it accessible to a global audience of learners who can engage with the material at their own pace. #### Final Thoughts Overall, if you are a SAS user looking to deepen your understanding of regression modeling, I wholeheartedly recommend "Regression Modeling Fundamentals" on Coursera. With a mix of theory, practical applications, and rigorous modeling techniques, it equips you with the skills needed to analyze data effectively and make informed decisions based on your analyses. Take the plunge, and you won’t be disappointed!

Syllabus

Course Overview (Review from Introduction to Statistics: Hypothesis Testing)

In this module you learn about the course and the data you analyze in this course. Then you set up the data you need to do the practices in the course.

In this module you explore several tools for model selection. These tools help limit the number of candidate models so that you can choose an appropriate model that's based on your expertise and research priorities.

In this module you learn to verify the assumptions of the model and diagnose problems that you encounter in linear regression. You learn to examine residuals, identify outliers that are numerically distant from the bulk of the data, and identify influential observations that unduly affect the regression model. Finally, you learn to diagnose collinearity to avoid inflated standard errors and parameter instability in the model.

In this module you learn how to transition from inferential statistics to predictive modeling. Instead of using p-values, you learn about assessing models using honest assessment. After you choose the best performing model, you learn about ways to deploy the model to predict new data.

In this module you look for associations between predictors and a binary response using hypothesis tests. Then you build a logistic regression model and learn about how to characterize the relationship between the response and predictors. Finally, you learn how to use logistic regression to build a model, or classifier, to predict unknown cases.