Course Description:

This practical  summer school course on "Associative inference using statistical Modeling in R" is designed for researchers, data analysts, and statisticians seeking to enhance their skills in applying statistical models to real-world data. The course will cover a range of statistical modeling techniques, from basic linear regression to more advanced methods such as generalized linear models and mixed-effects models. Participants will gain practical experience through hands-on exercises using R, one of the most powerful tools for statistical analysis and data visualization. The course will provide a blend of theoretical knowledge and practical application to ensure participants can effectively use these techniques in their own work.

Prerequisites:

Participants should be familiar with R software and have a basic background in statistics, including understanding of basic statistical concepts such as mean, variance, and simple linear regression.

Learning Outcomes:

By the end of the course, participants will be able to:

•  Understand and apply various statistical models using R.

•  Interpret the results of statistical analyses.

•  Diagnose and address potential issues in model fitting e.g interactions, confounding, multicollinearity, outliers

•  Use R to visualize data and model outputs.

•  Apply statistical modeling techniques to real-world datasets.

Course Timetable

Day 1: Introduction to Statistical Modeling

Time Session

09:00 - 10:30  Welcome and Course Introduction

           - Overview of Statistical Modeling

           - Introduction to R for Modeling

10:30 - 10:45  Break

10:45 - 12:00  Simple Linear Regression

           - Assumptions and Interpretation

           - Hands-on Session

12:00 - 01:00  Lunch

01:00 - 02:30  Multiple Linear Regression

           - Assumptions and Diagnostics

           - Hands-on Session

02:30 - 02:45  Break

02:45 - 04:00  Model Selection and Validation

           - AIC, BIC, Cross-Validation

           - Hands-on Session

Day 2: Advanced Regression Techniques

Time Session

09:00 - 10:30  Generalized Linear Models (GLMs)

           - Logistic Regression

           - Poisson Regression

- Zero-Inflated Poisson

10:30 - 10:45  Break

10:45 - 12:00  GLMs (Continued)

           - Hands-on Session

12:00 - 01:00  Lunch

01:00 - 02:30  Mixed-Effects Models

           - Random Intercepts and Slopes

           - Hands-on Session

02:30 - 02:45  Break

02:45 - 04:00  Diagnostics and Model Evaluation

           - Residual Analysis

           - Hands-on Session

Day 3: Specialized Topics and Applications

Time Session

09:00 - 10:30  Time Series Analysis

           - ARIMA Models

           - Hands-on Session

10:30 - 10:45  Break

10:45 - 12:00  Survival Analysis

           - Cox Proportional Hazards Model

           - Hands-on Session

12:00 - 01:00  Lunch

01:00 - 02:30  Bayesian Statistical Modeling

           - Introduction to Bayesian Methods

           - Hands-on Session

02:30 - 02:45  Break

02:45 - 04:00  Case Studies and Wrap-up

           - Real-world Applications

           - Q&A and Course Feedback

Instructors:

Dr. E. Nji, Ph.D. (Statistics)

Dr. E. Ndah Ph.D. (Data Science)

Dates: 

14,15 16 and 23 August 2024: 9h-16h

Participants are required to bring their own laptops with R software and the necessary packages pre-installed (including tidyverse, glm, lme4, forecast, and survival) for the hands-on sessions.