Statistical Analysis with R Essentials for Dummies.

Format:

Book

Author/Creator:

Schmuller, Joseph.

Series:

--For dummies.

--For dummies

Language:

English

Subjects (All):

Mathematical statistics--Data processing.

Mathematical statistics.

Statistics--Data processing.

Statistics.

R (Computer program language).

Physical Description:

1 online resource (195 pages)

Edition:

1st ed.

Place of Publication:

Newark : John Wiley & Sons, Incorporated, 2024.

Summary:

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. This book is to the point, focusing on the key topics readers need to know about this popular programming language. Great for supplementing classroom learning, reviewing for a certification, or staying knowledgeable on the job.

Contents:

Intro

Title Page

Copyright Page

Table of Contents

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

Samples and populations

Variables: Dependent and independent

Types of data

A little probability

Inferential Statistics: Testing Hypotheses

Null and alternative hypotheses

Two types of error

Chapter 2 Introducing R

Downloading R and RStudio

A Session with R

The working directory

So let's get started, already

Missing data

R Functions

User-Defined Functions

R Structures

Vectors

Numerical vectors

Matrices

Factors

Lists

Lists and statistics

Data frames

Extracting data from a data frame

for Loops and if Statements

Chapter 3 Digging Deeper Into R

Packages

More on Packages

R Formulas

Reading and Writing

Spreadsheets

CSV files

Text files

Chapter 4 Finding Your Center

Means: The Lure of Averages

The Average in R: mean()

What's your condition?

Eliminate signs forthwith()

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

Averaging squared deviations: Variance and how to calculate it

Sample variance

Variance in R

Back to the Roots: Standard Deviation

Population standard deviation

Sample 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?

Ranking in R

Tied scores

Nth smallest, Nth largest

Percentiles

Percent ranks

Creating Summaries

How Many?

The High and the Low.

Summarizing a Data Frame

Chapter 7 What's Normal?

Hitting the Curve

Digging deeper

Parameters of a normal distribution

Distributions in R

Normal density function

Cumulative density function

Quantiles of normal distributions

Random sampling

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

Applying the central limit theorem

Zs once more

Z-testing for two samples in R

t for Two

Like Peas in a Pod: Equal Variances

t-Testing in R

Working with two vectors

Working with a data frame and a formula

Like p's and q's: Unequal variances

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

After the ANOVA

Another word about contrasts

Contrasts 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!

Using regression for forecasting

Variation around the regression 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

EULA.

Notes:

Includes index.

Description based on publisher supplied metadata and other sources.

ISBN:

1-394-26344-9

OCLC:

1430211695