Course Overview

Welcome to Statistical Computing and Non-Parametric Inference Using R (3 Credits: L = 2, P = 1).

Course Outcomes (COs)

On successful completion of this course, students will be able to:

  • CO1: Demonstrate the ability to use the R programming environment for data handling, workspace management, and manipulation of different data structures.
  • CO2: Compute and interpret descriptive statistical measures and perform basic statistical summaries using R.
  • CO3: Apply programming constructs such as conditional statements, loops, and user-defined functions in R to automate statistical computations.
  • CO4: Implement resampling techniques, contingency table analyses, and generate structured statistical summaries and reports using R.
  • CO5: Select, apply, and interpret appropriate non-parametric statistical tests for different data situations using R.

Course Structure

Unit Topic Hours
Unit 1 Introduction to R and Data Structures 8
Unit 2 Descriptive Statistics and Programming Fundamentals in R 10
Unit 3 Confidence Interval Estimation and Resampling Methods 12
Unit 4 Analysis of Contingency Tables and Compact Reporting 15
Unit 5 Non-Parametric Tests and Applications Using R 15

Interactive Tutorials (scnpir R Package)

Interactive learnr tutorials for this course are provided via the scnpir R package. Install and run them in RStudio:

if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("kskbhat/scnpir")

library(scnpir)
learnr::run_tutorial("unit1_r_basics", package = "scnpir")

References

  1. R Core Team. An introduction to R. Vienna: R Foundation for Statistical Computing; 2023.
  2. Wickham H, Grolemund G. R for data science: import, tidy, transform, visualize, and model data. Sebastopol (CA): O’Reilly Media; 2017.
  3. Venables WN, Ripley BD. Modern applied statistics with S. 4th ed. New York: Springer; 2002.
  4. Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman & Hall/CRC; 1993.
  5. Hollander M, Wolfe DA, Chicken E. Nonparametric statistical methods. 3rd ed. Hoboken (NJ): Wiley; 2014.
  6. Conover WJ. Practical nonparametric statistics. 3rd ed. New York: Wiley; 1999.
  7. Agresti A. Categorical data analysis. 3rd ed. Hoboken (NJ): Wiley; 2013.

Statistical Computing and Non-Parametric Inference Using R • Author: Dr. Shrikrishna Bhat K • Department of Applied Statistics and Data Science, PSPH, MAHE