Statistical Analysis with R Essentials For Dummies

✍ Scribed by Joseph Schmuller

• Publisher: Wiley-Scrivener
• Year: 2024
• Tongue: English
• Leaves: 192
• Series: learning made easy
• Category: Library

✦ Synopsis

The easy way to get started coding and analyzing data in the R programming language

Statistical Analysis with R Essentials For Dummies is your reference to all the core concepts about R—the widely used, open-source programming language and data analysis tool. This no-nonsense book gets right to the point, eliminating review material, wordy explanations, and fluff. Understand all you need to know about the foundations of R, swiftly and clearly. Perfect for a brush-up on the basics or as an everyday desk reference on the job, this is the reliable little book you can always turn to for answers.

Get a quick and thorough intro to the basic concepts of coding for data analysis in R

Review what you've already learned or pick up essential new skills

Perform statistical analysis for school, business, and beyond with R programming

Keep this concise reference book handy for jogging your memory as you work

This book is to the point,...

✦ Table of Contents

Cover
Table of Contents
Title Page
Copyright
Introduction
About This Book
Foolish Assumptions
Icons Used in This Book
Where to Go from Here
Chapter 1: Data, Statistics, and Decisions
The Statistical (and Related) Notions You Just Have to Know
Inferential Statistics: Testing Hypotheses
Chapter 2: Introducing R
Downloading R and RStudio
A Session with R
R Functions
User-Defined Functions
R Structures
for Loops and if Statements
Chapter 3: Digging Deeper Into R
Packages
More on Packages
R Formulas
Reading and Writing
Chapter 4: Finding Your Center
Means: The Lure of Averages
The Average in R: mean()
Medians: Caught in the Middle
The Median in R: median()
Statistics à la Mode
The Mode in R
Chapter 5: Deviating from the Average
Measuring Variation
Back to the Roots: Standard Deviation
Standard Deviation in R
Conditions, Conditions, Conditions …
Chapter 6: Standards, Standings, and Summaries
Catching Some Zs
Standard Scores in R
Where Do You Stand?
Creating Summaries
How Many?
The High and the Low
Summarizing a Data Frame
Chapter 7: What’s Normal?
Hitting the Curve
Distributions in R
A Distinguished Member of the Family
Chapter 8: The Confidence Game: Estimation
Understanding Sampling Distributions
An EXTREMELY Important Idea: The Central Limit Theorem
Confidence: It Has its Limits!
Fit to a t
Chapter 9: One-Sample Hypothesis Testing
Hypotheses, Tests, and Errors
Hypothesis Tests and Sampling Distributions
Catching Some Z's Again
Z Testing in R
t for One
t Testing in R
Working with t-Distributions
Chapter 10: Two-Sample Hypothesis Testing
Hypotheses Built for Two
Sampling Distributions Revisited
t for Two
Like Peas in a Pod: Equal Variances
t-Testing in R
A Matched Set: Hypothesis Testing for Paired Samples
Paired Sample t-testing in R
Chapter 11: Testing More Than Two Samples
Testing More Than Two
ANOVA in R
Another Kind of Hypothesis, Another Kind of Test
Getting Trendy
Trend Analysis in R
Chapter 12: Linear Regression
The Plot of Scatter
Regression: What a Line!
Testing Hypotheses about Regression
Linear Regression in R
Making Predictions
Chapter 13: Correlation: The Rise and Fall of Relationships
Understanding Correlation
Correlation and Regression
Testing Hypotheses About Correlation
Analyzing Correlation in R
Chapter 14: Ten Valuable Online Resources
R-bloggers
Posit
Quick-R
Stack Overflow
R Manuals
R Documentation
RDocumentation
YOU CANanalytics
Geocomputation with R
The R Journal
Index
About the Author
Connect with Dummies
End User License Agreement