Statistics for Data Analysis Using R

Course Curriculum

Chapter 1: 1. Getting Started with R and R Studio

Lecture 1: Introduction – Section 1

Lecture 2: Installing R and R Studio (Windows)

Lecture 3: The First Look of R and R Studio. R you ready?

Lecture 4: The First Look at the Functions in R

Lecture 5: Saving the R Script File

Lecture 6: Data Types in R

Lecture 7: Simple Mathematical Operations

Lecture 8: Download – Section 1 Notes and Codes

Lecture 9: Section 1 – Practice Assignment

Chapter 2: 2. Bonus Section: Descriptive Statistics Theory (lessons from my other course)

Lecture 1: Introduction – Section 2

Lecture 2: Understanding Basic Statistical Terms (Theory)

Lecture 3: Descriptive Statistics (Theory)

Lecture 4: Measurement of Central Tendency (Theory)

Lecture 5: Measurement of Variation (Theory)

Lecture 6: Download – Section 2 Slides

Chapter 3: 3. Descriptive Statistics Using R

Lecture 1: Introduction – Section 3

Lecture 2: Getting Help

Lecture 3: Measurement of Central Tendency – Mean (Using R)

Lecture 4: Measurement of Central Tendency – Median and Mode (Using R)

Lecture 5: Measurement of Variation – Range, IQR and Standard Deviation (Using R)

Lecture 6: Download – Section 3 Notes and Codes

Lecture 7: Section 3 – Practice Assignment

Chapter 4: 4. Vectors, Factors, Lists, Matrix and Data Frames in R

Lecture 1: Introduction – Section 4

Lecture 2: Introduction

Lecture 3: Vectors Explained

Lecture 4: Factors Explained

Lecture 5: Lists Explained

Lecture 6: Matrix Explained

Lecture 7: Data Frames Explained

Lecture 8: Download – Section 4 Notes and Codes

Lecture 9: Section 4 – Practice Assignment

Chapter 5: 5. Data Visualization

Lecture 1: Introduction – Section 5

Lecture 2: Your first plot in R

Lecture 3: *** Scatter Plot ***

Lecture 4: Add the Plot Main and Axis Lebel Text

Lecture 5: Let's Draw Some Lines on the Plot

Lecture 6: Change the Plot Characters (pch) from Circles to Plus Signs

Lecture 7: Let's Look at Filtered Data

Lecture 8: One is not enough, I want more plots on a single page!

Lecture 9: Add text to the plot

Lecture 10: Make plot colorful, and text bigger and bold

Lecture 11: Multiple pairs of scatter diagrams – when one plot is not enough!

Lecture 12: Time Series Plot

Lecture 13: *** Histogram ***

Lecture 14: *** Box and Whisker Plot ***

Lecture 15: Download – Section 5 Notes and Codes

Lecture 16: Section 5 – Practice Assignment

Chapter 6: 6. Descriptive Statistics Re-visited

Lecture 1: Introduction – Section 6

Lecture 2: Descriptive Statistics Using psych Package

Lecture 3: Download – Section 6 Notes and Codes

Chapter 7: 7. Bonus Section: Basic Probability Theory (lessons from my other course)

Lecture 1: Introduction – Section 7

Lecture 2: Probability Definition

Lecture 3: Probability – Union and Intersection

Lecture 4: Probability – The Law of Addition, Multiplication and Conditional Probability

Lecture 5: Factorial, Permutations and Combinations

Lecture 6: Download – Section 7 Slides

Chapter 8: 8. Probability Distributions

Lecture 1: Introduction – Section 8

Lecture 2: Central Limit Theorem (Theory)

Lecture 3: Central Limit Theorem Demonstration Using R

Lecture 4: *** Normal Probability Distribution (Theory) ***

Lecture 5: R Functions for Normal Distribution – rnorm, pnorm, qnorm and dnorm

Lecture 6: Plotting Normal Distribution Using R Functions

Lecture 7: Introducting "visualize" Package

Lecture 8: *** Binomial Probability Distribution (Theory) ***

Lecture 9: R Functions for Binomial Distribution – rbinom, pbinom, qbinom and dbinom

Lecture 10: Plotting Binomial Distribution Using R Functions

Lecture 11: Binomial Distribution using Visualize Package

Lecture 12: *** Poisson Distribution (Theory) ***

Lecture 13: R Functions for Poisson Distribution – rpois, ppois, qpois and dpois

Lecture 14: Plotting Poisson Distribution Using R Functions

Lecture 15: Poisson Distribution using Visualize Package

Lecture 16: Download – Section 8 Notes and Codes

Chapter 9: 9. Inferential Statistics – Hypothesis Tests

Lecture 1: Introduction – Section 9

Lecture 2: Types of Mean and Variance Tests

Lecture 3: Hypothesis Testing – Types of Errors (Theory)

Lecture 4: What is p value? (Theory)

Lecture 5: *** Hypothesis Testing – One Sample Z Test (Theory) ***

Lecture 6: One Sample z Test Using R

Lecture 7: One Sample z Test using BSDA Package

Lecture 8: *** One Sample t Test (Theory) ***

Lecture 9: One Sample t Test Using R

Lecture 10: Visualizing One Sample t Test Results using Visualize Package

Lecture 11: *** One Sample Variance Test – Chi Square Test (Theory) ***

Lecture 12: One Sample Variance Test Using Envstats Package

Lecture 13: Chi Square Distribution for One Sample Variance Test

Lecture 14: *** Two Sample Z Test (Theory) ***

Lecture 15: Two Sample Z Test Using R

Lecture 16: Visualizing Two Sample Z Test Using Visualize Package

Lecture 17: Two Sample Z Test for Populations with Different Means

Lecture 18: *** Two Sample t Test (Theory) ***

Lecture 19: Two Sample t Test (Equal Variance) Using R