Quantitative Data Management And Analysis With R Course

  • Overview

INTRODUCTION

This course is intended for participants who intend to organize, code, analyze, and visualize qualitative data using R. Basics of Applied Statistical Modeling, Essentials of R Programming, Statistical Tools, Probability Distributions, Statistical Inference, Relationship between Two Different Quantitative Variables, and Multivariate Analysis are among the topics covered in the course's seven modules. The course uses real data to teach R's fundamentals and more complex capabilities, and it is totally hands-on.

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 OBJECTIVES:

Determine which statistical procedures are most suited to the data and questions in order to:

  • Analyze the data using the relevant statistical techniques
  • Interpret the statistical analysis
  • Strong foundation in fundamental statistical concepts;
  • Ability to apply various statistical analyses in R and interpret the results;
  • Ability to build logical data visualizations;
  • Ability to conduct formalized hypothesis testing;
  • Ability to apply multiple regression and generalized linear models (GLMs);
  • Ability to apply 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

  • Our seasoned instructors, who have years of experience as seasoned professionals in their respective fields of work, will be teaching this course. A combination of practical exercises, theory, group projects, and case studies are used to teach the course.
  • The participants receive training manuals and supplementary reading materials.
  • Participants who complete this course successfully will receive a certificate.
  • We can also create a course specifically for your organization to match your needs. To learn more, get in touch with us at training@dealsontrainers.org.
  • The training will take place at DEALSON TRAINERS IN NAIROBI, KENYA in Nairobi, Kenya.
  • The training fee includes lunch, course materials, and lodging for the training session. Upon request, we may arrange for our participants' lodging and transportation to the airport.
  • Payment must be made to our bank account before the training begins, and documentation of payment should be emailed to training@dealsontrainers.org

Course Schedule:
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