About the Course

Course Outline

Introduction to Statistical Analysis 

• Explain the basic steps of the research process
• Explain differences between populations and samples
• Explain differences between experimental and non-experimental research designs
• Explain differences between independent and dependent variables

Introduction to R software for statistical computing 

• Overview of the R Studio IDE
• Installing, loading and updating R packages
• Creating objects in R
• Data types
• Data structures
• Sorting vectors and data frames
• Directory management commands
• Direct data entry in R (for small data sets)
• Importing data from other software
• Decision structures (if, if-else, if-else if-else)
• Repetitive structures (for and while loops)
• Other important programming functions (break, next, warn, stop)

Data Wrangling and Cleaning in R 

• Working with variables
• Transform continuous variables to categorical variables
• Add new variables to data frames
• Handling missing values
• Sub-setting data frames
• Appending and merging data frames
• Spit data frames
• Stack and unstack data frames

Explanatory Data Analysis (EDA) in R 

• Creating tables of frequencies and proportions
• Cross tabulations of categorical variables
• Descriptive statistics for continuous variables

Data Visualization using R base package

• Introduction to graphs and charts in R
• Customizing graph attributes (titles, axes, text, legends)
• Graphs for categorical variables
• Graphs for continuous variables
• Graphs to investigate relationship 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 in R

• Chi-Square test of independence
• Pearson’s Correlation
• Spearman’s Rank-Order Correlation

Predictive Regression Models using R

• Linear Regression
• Multiple Linear Regression
• Binary Logistic Regression
• Ordinal Logistic Regression

Training Approach 

This Training on Statistical Data Analysis using R is delivered by our seasoned trainers who have vast experience as expert professionals using R programming language. The course is taught through a mix of practical activities, theory, group works and case studies.

Training manuals and additional reference materials are provided to the participants.

Certification 

Upon successful completion of this Training on Statistical Data Analysis using R, participants will be issued with a certificate certified by the National Industrial Training Authority (NITA).

Tailor-Made Course 

We can also do this as a tailor-made course to meet organization-wide needs. A training needs assessment will be done on the training participants to collect data on the existing skills, knowledge gaps, training expectations, and tailor-made needs.