# Quantitative Data Management and Analysis with R Course

Training

Start date

11/05/2020

End date

15/05/2020

## Overview

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)

General Notes

• All our courses can be Tailor-made to participants needs
• The participant must be conversant with English
• Presentations are well guided, practical exercise, web based tutorials and group work. Our facilitators are expert with more than 10years of experience.
• Upon completion of training the participant will be issued with Foscore development center certificate (FDC-K)
• Training will be done at Foscore development center (FDC-K) center in Nairobi Kenya. We also offer more than five participants training at requested location within Kenya, more than ten participant within east Africa and more than twenty participant all over the world.
• Course duration is flexible and the contents can be modified to fit any number of days.
• The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and a Certificate of successful completion of Training. Participants will be responsible for their own travel expenses and arrangements, airport transfers, visa application dinners, health/accident insurance and other personal expenses.
• Accommodation, pickup, freight booking and Visa processing arrangement, are done on request, at discounted prices.
• One year free Consultation and Coaching provided after the course.
• Register as a group of more than two and enjoy discount of (10% to 50%) plus free five hour adventure drive to the National game park.
• Payment should be done two week before commence of the training, to FOSCORE DEVELOPMENT CENTER account, so as to enable us prepare better for you.
• For any enquiry to: [email protected] or +254712260031
• Click to view course content and register as individual or group using this link- https://tinyurl.com/sr3er8j
• Click to view Research and Data Analysis courses calendar– http://bit.ly/2rfjxn4
• Click to view all Courses calendar - http://bit.ly/2mB2p5w

## What you will learn

• 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