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Learning Statistics with R: A tutorial for psychology students and other beginners
Paperback
General ComputersGeneral ScienceTest Prep
Publisher: Independently Published
Published: May 15 2018
Pages: 798
Weight: 3.42
Height: 1.59 Width: 8.00 Depth: 10.00
Language: English
The book was specifically created to bridge the gap between traditional introductory statistics and modern computational tools, making statistical analysis accessible to students and researchers without a formal computer science background.
Book Overview & Core Philosophy:Unlike standard math-heavy textbooks that focus purely on manual calculations, Learning Statistics with R takes a hands-on, computational approach. It teaches statistical theory alongside practical data manipulation in R, allowing readers to analyze real datasets right from the start.
Key elements of its approach include:
Conversational & Accessible Tone: Written in an engaging, humorous, and approachable style, avoiding unnecessary jargon to reduce statistics anxiety.
Dual Focus (Theory + Code): Explains why a statistical test works conceptually, then immediately demonstrates how to execute it in R.
Psychology & Social Science Context: Uses relatable examples and datasets drawn from psychology, human behavior, and everyday research scenarios.
The textbook is logically organized into progressive modules:
Section / TopicFocus Areas1. Getting Started with R: R/RStudio installation, basic syntax, data structures (vectors, data frames), and workspace management.2. Descriptive Statistics & Data Viz: Calculating measures of central tendency/spread (mean, median, standard deviation) and creating clean graphs (histograms, scatter plots, boxplots).
3. Probability & Sampling: Probability distributions (Binomial, Normal), $z$-scores, sampling distributions, and the Central Limit Theorem.
4. Inferential Statistics: Hypothesis testing principles, $p$-values, confidence intervals, effect sizes, and Type I/II errors.
5. Statistical Tests: Hands-on application of $z$-tests, Student's and Welch's $t$-tests (independent & paired), $X 2$ tests of independence, ANOVA, and Linear Regression. Who Is This Book For?
Psychology & Social Science Students: Designed specifically for undergraduates or postgraduates taking their first quantitative research methods course.
Beginner Data Analysts: Ideal for self-learners looking to build a strong foundation in both fundamental statistics and basic R scripting.
Researchers Transitioning to R: Helpful for academics moving away from legacy point-and-click software (like SPSS) toward open-source R code.
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