Applying the Mathematical MASS Model with R

Unlock Powerful Statistical Analysis with R and the MASS Package

Dive into advanced statistical techniques using R and the widely-used MASS package. This course builds upon the legacy of "Modern Applied Statistics with S" by Venables and Ripley, the foundational text that paved the way for R's dominance in statistical analysis. The MASS package, named after this influential book, offers a wealth of tools for sophisticated data modeling.

Key Skills You'll Gain:

• Hypothesis Testing: Master the classic t-test for estimating means in various scenarios. Understand and apply ANOVA (Analysis of Variance) to assess statistical differences across different data categories. Learn about one-sample t-tests, two-sample t-tests, and one-way ANOVA for comparing means based on categorical variables.
• Classification Techniques: Explore Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA). These powerful classification methods re-orient data to maximize separation between different classes. Discover when QDA is more suitable than LDA, particularly when input variables have differing covariances.
• Robust Regression: Learn to build reliable regression models even with outliers using three robust regression techniques: Huber, Bisquare, and Hampel. These methods minimize the influence of extreme values, leading to more accurate results.
• Multi-State Modeling & Survival Analysis: Model complex systems evolving through a series of states using multi-state models. Understand the distinction between transition probabilities and transition intensities. Apply these concepts to survival models, widely used in medical research to analyze treatment outcomes and model hazard rates and survival probabilities.

The MASS Package in R:

This course provides hands-on experience using the datasets, predictive models, and specialized functions within the MASS package in R. You'll learn to leverage these tools for a wide range of statistical applications.

Who is this course for?

This course is ideal for data analysts, statisticians, researchers, and anyone seeking to expand their statistical toolkit using R. You'll gain practical skills in applying advanced statistical techniques to real-world problems.