Course Overview

R is a powerful programming language and software environment specifically designed for statistical computing and graphics.  It's widely used by statisticians, data analysts, and data scientists for developing statistical software and performing data analysis. As an open-source tool, R offers a vast array of packages and functions, which makes it a versatile choice for handling a wide range of data analysis tasks.

Unlock the power of R, a leading statistical software for data visualization and analysis. Perfect for researchers, statisticians, and data miners, this course will equip you with the essential skills to manage and analyze quantitative data efficiently. In our data-driven world, the ability to make sense of complex datasets is invaluable. This course provides a robust foundation in data management, statistical analysis, and visualization using R, an open-source software renowned for its versatility.

Who Should Attend?

This course is ideal for:

• Statisticians and analysts seeking to enhance their data analysis skills.
• Budding data scientists and beginners eager to learn R for quantitative analysis.
• Anyone interested in developing a strong foundation in data visualization and statistical computing.

Course Objectives

By the end of this course, you will be able to:

• Understand R: Learn to use R and RStudio as powerful tools for quantitative data analysis.
• Data Import and Export: Master the techniques for importing and exporting data in various formats.
• Descriptive Statistics: Explore basic statistics for summarizing and describing variable distributions.
• Data Manipulation: Gain essential skills in data wrangling, including subset selection and data transformation.
• Visual Representation: Create compelling visualizations using scatter graphs, bar charts, and histograms.
• Advanced Analysis: Perform sophisticated statistical data analyses and handle complex datasets.
• Predictive Modeling: Learn to implement and analyze predictive regression models.

Course Content

Introduction to Statistical Analysis

• Basic steps of the research process
• Differences between populations and samples
• Experimental vs. non-experimental research designs
• Independent vs. dependent variables

Introduction to R Software for Statistical Computing

• Overview of R Studio IDE
• Installing and managing R packages
• Creating objects, data types, and data structures
• Data sorting and directory management
• Direct data entry and importing data from other software

Data Wrangling and Cleaning in R

• Working with variables and handling missing values
• Transforming continuous to categorical variables
• Data frame operations: sub-setting, appending, merging

Exploratory Data Analysis (EDA) in R

• Creating frequency tables and cross tabulations
• Descriptive statistics for continuous variables

Data Visualization Using R Base Package

• Introduction to graphs and charts in R
• Customizing graph attributes
• Visualizing relationships between variables

Mean Comparison Tests in R

• One Sample T Test, Independent Samples T Test, Paired Samples T Test
• One-way analysis of variance (ANOVA)

Tests of Associations and Predictive Regression Models

• Chi-Square, Pearson's Correlation, Spearman's Rank-Order Correlation
• Linear, Multiple Linear, and Logistic Regression models