Quantitative Data Management and Analysis with R course

Quantitative Data Management and Analysis with R course on 5th to 9th December 2022

course calendar

 

Introduction

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 analyze data with R,

Course Objective:

·         Analyze 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)