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Using R for Introductory Statistics in Social Sciences

Name: Using R for Introductory Statistics in Social Sciences
Brand: Springer, Berlin
SKU: 9783032171443
Price: 2457 CZK
Availability: InStock

Autor: Mark A. Perkins

Jazyk:

Vazba: Pevná

Vydavatel: Springer, Berlin

Dostupnost: Skladem u dodavatele

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This book teaches R programming for fundamental statistical and data analysis skills, specifically t...

Informace o knize

Autor

Mark A. Perkins

Jazyk

Angličtina

Vazba

Kniha - Pevná

Vydáno

2026

Stránek

242

EAN

9783032171443

Enbook ID

50211281

Vydavatel

Springer, Berlin

Hmotnost

449

Rozměry

155 x 235

Kompletní popis

This book teaches R programming for fundamental statistical and data analysis skills, specifically tailored to social scientists and others new to quantitative research. Traditionally, this audience has relied on costly software packages such as SPSS, STATA, and SAS. However, R is a free, open-source alternative that, with proper guidance, is accessible and powerful for their needs. Many existing resources, whether books or online, are overly technical or difficult to follow. This book fills that gap by offering a concise, practical guide to mastering essential statistical processes, equipping readers with skills they can use throughout their careers.

Data analysts, institutional researchers, and other professionals will use the book to perform statistical analyses and generate reports for their organizations. The included code both in the book and online helps them apply techniques to their own data. Readers will gain the following skills:

Install and set up R and RStudio; write R scripts and create R Markdown documents.
Understand variable types, measurement scales, and the basics of descriptive and inferential statistics.
Conduct chi-square tests, t-tests, ANOVA, regression, and time series analysis in R.
Assess and interpret statistical outputs; write results in APA format.
Visualize data using ggplot2 and related libraries; create publication-ready charts and tables
Evaluate statistical assumptions and apply techniques responsibly to real-world datasets
Communicate findings clearly and concisely using professional standards

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