This course is designed for participants who plan to use R for the management, coding, analysis and visualization of qualitative data. The course’s content is spread over seven modules and includes: Basics of Applied Statistical Modelling, Essentials of the R Programming, Statistical Tools, Probability Distributions, Statistical Inference, Relationship between Two Different Quantitative Variables and Multivariate Analysis. The course is entirely hands-on and uses sample data to learn R basics and advanced features.

Duration

5 days

Who Should Attend

Statistician, analyst, or a budding data scientist and beginners who want to learn how to analyse data with R,

Course Objective

• Analyse t data by applying appropriate statistical techniques.
• Interpret the statistical analysis.
• Identify statistical techniques a best suited to data and questions.
• Strong foundation in fundamental statistical concepts
• Implement different statistical analysis in R and interpret the results.
• Build intuitive data visualizations.
• Carry out formalized hypothesis testing.
• Implement linear modelling techniques such multiple regressions and GLMs
• Implement advanced regression analysis and multivariate analysis!

Course Content

MODULE ONE: Basics of Applied Statistical Modelling

• Introduction to the Instructor and Course
• Data & Code Used in the Course
• Statistics in the Real World
• Designing Studies & Collecting Good Quality Data
• Different Types of Data

MODULE TWO: Essentials of the R Programming

• Rationale for this section
• Introduction to the R Statistical Software & R Studio
• Different Data Structures in R
• Reading in Data from Different Sources
• Indexing and Subletting of Data
• Data Cleaning: Removing Missing Values
• Exploratory Data Analysis in R

MODULE THREE: Statistical Tools

• Quantitative Data
• Measures of Center
• Measures of Variation
• Charting & Graphing Continuous Data
• Charting & Graphing Discrete Data
• Deriving Insights from Qualitative/Nominal Data

MODULE FOUR: Probability Distributions

• Data Distribution: Normal Distribution
• Checking For Normal Distribution
• Standard Normal Distribution and Z-scores
• Confidence Interval-Theory
• Confidence Interval-Computation in R

MODULE FIVE: Statistical Inference

• Hypothesis Testing
• T-tests: Application in R
• Non-Parametric Alternatives to T-Tests
• One-way ANOVA
• Non-parametric version of One-way ANOVA
• Two-way ANOVA
• Power Test for Detecting Effect

MODULE SIX: Relationship between Two Different Quantitative Variables

• Explore the Relationship Between Two Quantitative Variables
• Correlation
• Linear Regression-Theory
• Linear Regression-Implementation in R
• Conditions of Linear Regression
• Multi-collinearity
• Linear Regression and ANOVA
• Linear Regression With Categorical Variables and Interaction Terms
• Analysis of Covariance (ANCOVA)
• Selecting the Most Suitable Regression Model
• Violation of Linear Regression Conditions: Transform Variables
• Other Regression Techniques When Conditions of OLS Are Not Met
• Regression: Standardized Major Axis (SMA) Regression
• Polynomial and Non-linear regression
• Linear Mixed Effect Models
• Generalized Regression Model (GLM)
• Logistic Regression in R
• Poisson Regression in R
• Goodness of fit testing

MODULE SEVEN: Multivariate Analysis

• Introduction Multivariate Analysis
• Cluster Analysis/Unsupervised Learning
• Principal Component Analysis (PCA)
• Linear Discriminant Analysis (LDA)
• Correspondence Analysis
• Similarity & Dissimilarity Across Sites
• Non-metric multi-dimensional scaling (NMDS)
• Multivariate Analysis of Variance (MANOVA)

General Notes

• This course is delivered by our seasoned trainers who have vast experience as expert professionals in the respective fields of practice. 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.
• Upon successful completion of this course, participants will be issued with a certificate.