﻿ Inter-Rater Reliability

# Introductory Principal Component Analysis Using R by Kilem L. Gwet, PhD

In this book, you will learn how to perform Principal Component Analysis (PCA) using the R software. R is used through an Integrated Development Environment (IDE) called RStudio.

Although having some knowledge of R will help, this book assumes no specific prerequisite, except for good computer and analytical skills. PCA is a collection of advanced statistical techniques. Therefore, some familiarity with statistical thinking is essential. However, this book is a non-mathematical presentation of Principal Component Analysis, which is one of the essential skills that you must have to become a productive analyst in the new world of data science.

### Introductory Principal Component Analysis Using R by Kilem L. Gwet, PhD

Chapter 1: Basics of Principal Components

• statex77.csv: Data sets related to the 50 states of the United States of America (in csv format)
• mtcars.csv: Motor Trend Car Road Test (in csv format)
• iris.csv: Edgar Anderson’s Iris Data (in csv format)  -
• wdbc.data.csv: Breast Cancer Wisconsin (Diagnostic) Data Set (in csv format)
• x1x2data.csv: Measurements of 2 variables $X_1$ and $X_2$ taken on 50 subjects (in csv format)
• x1x2pcs.csv: Principal component scores calculated for 50 subjects (in csv format)

Chapter 2: Overview of R with RStudio

• employee.xls: Employee compensation data by gender, age and marital status in Excel.
• employee.csv: Employee compensation data by gender, age and marital status in CSV (Comma delimited) format.

Chapter 3: Computing Principal Components with R

• HWdata.xlsx: Excel file containing height and weight measurements of 15 individuals .

Chapter 4: Visualization of Principal Components

• wdbc.data.csv: Wisconsin Diagnostic Breast Cancer Dataset, in csv format.
• wdbc.xlsx: Wisconsin Diagnostic Breast Cancer Dataset, in Excel format.
• employee_demo.csv: Demographic data of 7 employess (Employee Id, Gender, Age and Marital status).
• employee_name.csv: Names of 7 employees along with their State of residency, Employment Status, and Employee Id
• gdp_by_state.csv: 2020 US Quarterly GDP Data (in millions of dollars) and 2019 Population Data by State.

Chapter 5: Basic Use of Principal Components

• employee_demo.csv: Demographic data of 7 employess (Employee Id, Gender, Age and Marital status).

Chapter 6: Statistical Analysis Based on Principal Components

• employee_demo.csv: Demographic data of 7 employess (Employee Id, Gender, Age and Marital status).