Linear Regression in R for Public Health

Go to Course: https://www.coursera.org/learn/linear-regression-r-public-health

Introduction

### Course Review: Linear Regression in R for Public Health on Coursera In an increasingly data-driven world, the ability to analyze data effectively is paramount, especially in the field of public health. "Linear Regression in R for Public Health" is a course offered on Coursera that provides a robust foundation for understanding and applying linear regression techniques in the context of public health. #### Overview This course begins by framing public health not only as a field dedicated to preventing disease and promoting well-being but also as a domain where statistical models play a crucial role in deciphering the complex relationship between various factors and health outcomes. The course aims to equip students with the skills to create statistical models from scratch, harnessing the power of the R programming language. #### Syllabus Breakdown 1. **Introduction to Linear Regression**: - The course kicks off with a critical exploration of correlation, paving the way for a comprehensive understanding of linear regression. Here, learners will generate Pearson’s and Spearman’s correlation coefficients in R, grasping how these measures assess relationships between predictors and outcomes. Learning about model assumptions introduces learners to the foundational principles of rigorous statistical analysis. 2. **Linear Regression in R**: - Using a COPD dataset, students will embark on a practical journey through basic descriptive analyses and the application of correlation techniques in R. The course provides step-by-step guidance on building linear regression models, initially focusing on single predictors and gradually progressing to models with multiple predictors, critically assessing whether model assumptions hold true throughout. 3. **Multiple Regression and Interaction**: - The content deepens with the introduction of binary and categorical variables into regression models. Students will learn about correlation checks among predictors and the nuances of interactive terms. The course emphasizes paced instruction with worked examples and practice opportunities, which help in demystifying complex interactions. 4. **Model Building**: - Finally, the course culminates in a discussion about model building strategies. It highlights the decision-making process regarding which predictors to include in a regression model and critiques common automated model-building approaches. Learners will engage in fitting models using more robust and defensible methods, promoting a sound understanding of the intricacies involved in model selection. #### Why You Should Take This Course - **Target Audience**: Whether you're a public health professional, a student, or a data analyst, this course is designed to cater to various levels of expertise. It provides foundational knowledge and advanced concepts, making it ideal for both beginners and those looking to refresh their skills. - **Hands-On Learning**: The use of real-world data sets (like the COPD dataset) provides practical exposure and helps solidify theoretical knowledge through application. This experiential learning is crucial in a field like public health, where data interpretation can inform policy and practice. - **Expert Guidance**: The course instructors have a deep understanding of both statistical methodologies and public health, offering insights that go beyond textbook knowledge. - **Flexible Learning Environment**: As a Coursera course, learners can engage with the content at their own pace, allowing for flexibility amidst varying personal and professional commitments. #### Conclusion and Recommendation "Linear Regression in R for Public Health" is more than just an online course; it's an investment in your professional development in the public health sector. By bridging the gap between statistical theory and practical application, this course empowers you to make informed decisions driven by data analysis. If you're eager to enhance your skills in R and harness the full potential of linear regression in your public health endeavors, this course comes highly recommended.

Syllabus

INTRODUCTION TO LINEAR REGRESSION

Before jumping ahead to run a regression model, you need to understand a related concept: correlation. This week you’ll learn what it means and how to generate Pearson’s and Spearman’s correlation coefficients in R to assess the strength of the association between a risk factor or predictor and the patient outcome. Then you’ll be introduced to linear regression and the concept of model assumptions, a key idea underpinning so much of statistical analysis.

You’ll be introduced to the COPD data set that you’ll use throughout the course and will run basic descriptive analyses. You’ll also practise running correlations in R. Next, you’ll see how to run a linear regression model, firstly with one and then with several predictors, and examine whether model assumptions hold.

Now you’ll see how to extend the linear regression model to include binary and categorical variables as predictors and learn how to check the correlation between predictors. Then you’ll see how predictors can interact with each other and how to incorporate the necessary interaction terms into the model and interpret them. Different kinds of interactions exist and can be challenging to interpret, so we will take it slowly with worked examples and opportunities to practise.

The last part of the course looks at how to build a regression model when you have a choice of what predictors to include in it. It describes commonly used automated procedures for model building and shows you why they are so problematic. Lastly, you’ll have the chance to fit some models using a more defensible and robust approach.