Quantitative Data Management and Analysis with R course

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)