STATISTICAL DATA ANALYSIS IN R-LANGUAGE

 Date: 24th-26^th,November, 2025

 INTRODUCTION

R is a powerful, open-source programming language and environment specifically designed for statistical computing and graphics. It is widely used for statistical data analysis due to its extensive range of packages, flexibility, and robust data visualization capabilities.  . 

 A statistical data analysis course in R covers programming fundamentals, data handling, exploratory data analysis, and key statistical techniques. The content is practical and hands-on, teaching students how to use the R programming language and its powerful packages to conduct and interpret statistical analyses. 

Course Contents

1. Introduction to R and RStudio

• R and RStudio environment: Learn to install and navigate R and the RStudio
  Integrated Development Environment (IDE).
• R fundamentals: Understand basic R operations, functions, data types, and control structures (e.g., loops and conditional statements).
• R packages: Learn to install, load, and manage add-on libraries like the tidyverse suite (dplyr, ggplot2, readr). 

2. Data Management and Manipulation

• Importing data: Practice importing datasets from various sources, such as CSV, Excel, and text files.
• Data structures: Work with R’s data structures, including vectors, matrices, data frames, and tibbles.
• Data wrangling: Learn to clean, transform, and prepare data for analysis by:
  • Handling missing values.
  • Renaming, adding, or modifying variables.
  • Subsetting, appending, and merging data frames. 

3. Descriptive Statistics and Visualization

• Exploratory Data Analysis (EDA): Use EDA to summarize the main characteristics of a dataset, often with visual methods.
• Summary statistics: Calculate measures of central tendency (mean, median), measures of dispersion (variance, standard deviation), and distributions.
• Frequency tables: Create frequency and proportion tables for categorical variables.
• Data visualization: Create various plots to visualize data and relationships using both base R and ggplot2:
  • Histograms and density plots
  • Boxplots
  • Bar charts and pie charts
  • Scatter plots 

4. Probability and Distributions

• Probability distributions: Learn about common discrete (e.g., binomial, Poisson) and continuous (e.g., normal, t, F) probability distributions.
• Sampling distributions: Understand the concept of sampling distributions, including the Central Limit Theorem. 

5. Inferential Statistics

• Hypothesis testing: Conduct and interpret one-sample and two-sample hypothesis tests.
• Confidence and prediction intervals: Construct confidence and prediction intervals for various statistics, including the mean, variance, and proportion.
• Comparison tests: Perform statistical tests to compare means between groups, such as one-sample t-tests, independent samples t-tests, paired samples t-tests, and Analysis of Variance (ANOVA).
• Non-parametric tests: Explore non-parametric alternatives when data assumptions are not met. 

6. Regression Analysis

• Linear regression: Learn to build and interpret simple and multiple linear regression models to predict a continuous outcome.
• Logistic regression: Use binary and ordinal logistic regression for classification problems with categorical outcomes.
• Model building: Learn techniques for selecting the best regression model, such as backward elimination.
• Model assumptions: Check and evaluate model assumptions through residual analysis

Duration: Four (4) days           Fee: N300,000