About this Course
The final course in the program introduces the principles and methods of statistical inference for categorical data and censored survival data. You’ll learn to use R to load, manipulate and analyze categorical and binary data with continuous or binary outcomes. We’ll construct contingency tables and use R to calculate various risk measures and confidence intervals around those measures. You’ll learn to fit logistic regression models to data, then calculate model coefficients, odds ratios and formulate and test hypotheses about them. We’ll use product limit measures (Kaplan-Meier), Cox regression models and log-rank tests to calculate confidence measures, perform hypothesis test and draw conclusions in a variety of cases.
What You’ll Learn
- Manipulation and analysis of categorical and binary data structures using R
- How to determine odds ratios, relative risk and associated confidence intervals
- Hypothesis testing and measurement of confidence intervals using simple logistic regression models and Cox regression models
- How to use survival data in analysis, interpretation and hypothesis testing
Get Hands-On Experience
You will complete a data analysis project using real-world examples to manipulate data, display and report results, and draw appropriate conclusions.