Elementary Calculus
AppliedTextbook
Introduction to Statistical Thinking
Not rated yetRate this bookIntroduction to Statistical Thinking uses a limited set of R tools to support conceptual learning. Benjamin Yakir develops descriptive statistics and probability before statistical inference, estimation, testing, comparisons and applied case studies.
FreeAbout this book
The preface identifies college students with limited mathematical background as its primary audience. It asks them to participate actively, practise and think, while assuming basic computer skills and access. R is used selectively so that software learning supports the statistical discussion instead of overwhelming it. The treatment of probability likewise concentrates on ideas needed for inference rather than a separate axiomatic course. The book was written for a pair of University of the People courses. The reviewed preface is dated June 2011, which is more precise for this edition than the imported 2010 catalogue date.
The attached edition contains 380 PDF pages, including front matter. Cover: source edition title page rendered from the PDF. The textbook and its original notices are retained; Academic Master’s study material is separate from the source text.
Book summary
Study overview of the reviewed opening
Yakir’s preface describes an attempt to teach statistical reasoning to students who may not begin with enthusiasm or extensive mathematical preparation. Active engagement is still expected. The computing environment is introduced through a restricted set of commands, balancing the benefit of practical analysis against the effort required to learn software.
The first part combines descriptive statistics with probability concepts needed to understand inference. Variation, distributions and sampling lead to an integrating chapter. The second part introduces estimation, intervals, hypothesis tests and comparisons, then uses regression-related settings and case studies to connect those tools.
The preface identifies both the course context and limits of its coverage. It explains that the original two-course structure constrained chapter selection, and acknowledges topics not treated fully. The page’s overview reflects that framing rather than promising comprehensive statistics coverage. It does not provide current R installation instructions or a detailed summary of every analysis. Chapter references and examples should be read in the context of the hosted edition.
Book contents
- List of Tables
- List of Figures
- Introduction
- Sampling and Data Structures
- Descriptive Statistics
- Probability
- Random Variables
- The Normal Random Variable
- The Sampling Distribution
- Overview and Integration
- Introduction to Statistical Inference
- Point Estimation
- Confidence Intervals
- Testing Hypothesis
- Comparing Two Samples
- Linear Regression
- A Bernoulli Response
- Case Studies
- Exercise Solutions
Questions for study
The following prompts are Academic Master’s editorial material.
- How can software support a conceptual course without dominating it?
- Why does inference depend on understanding sampling?
- Which limits should be noted when using a textbook for a new course?
Sources and coverage
Preface read in full (PDF pages 13–16); contents and dated preface checked.
About access & copyright
Check this edition’s source and licence before reusing its files or content. A library listing does not grant additional rights.
Read our Library Access & Copyright policyConceptual emphasis
Recognise the intended statistical learning goal.
Selective computing
Relate limited R use to the course approach.
Learning progression
Connect description and probability with inference.
Scope awareness
Identify the preface’s stated constraints.
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