Public Health Data Analysis with Logistic Regression in R

Public health data is often complex and unstructured, yet it is essential for predicting disease trends and improving community health outcomes. Navigating the nuances of real-world patient records requires a specific set of analytical skills to ensure results are both accurate and actionable. You will learn how to navigate these challenges by applying logistic regression to real-world scenarios, such as predicting chronic conditions from patient characteristics. By reading through detailed explanations and practical code examples, you will transform raw data into meaningful statistical insights. What you'll learn: - Understand the fundamental concepts of binary outcomes and the logic behind regression models. - Clean and transform real-world health data using modern R packages and workflows. - Calculate and interpret odds ratios to communicate disease risk factors effectively. - Apply modern techniques for handling missing data and imbalanced health datasets. - Evaluate model accuracy and reliability using clinical performance metrics. - Practice building models that translate raw patient data into clear health insights. The course begins with core statistical definitions and foundational terminology before walking through the practical steps of data preparation, model construction, and results interpretation. Each concept is reinforced through written R code examples designed for clarity and application. This course is designed for beginners in public health, nursing, or data analysis who want to apply R to health-related problems. No prior experience with statistical modeling is required. Gain the skills to turn messy health data into clear predictive models.