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Introductory Statistics: Exploring the World Through Data 4th Edition By Gould R.

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Pages: 800
File size: 20.4 MB
Format: Digital PDF
Course: Education
Category: eBook[PDF]

About this ebook

Introductory Statistics: Exploring the World Through Data (4th Edition) by Robert Gould and Colleen Ryan covers data collection, descriptive statistics, probability modeling, and inferential statistics. [1, 2, 3, 4, 5]

The book is structured around the "Data Cycle" method, which teaches students how to Ask Questions, Consider Data, Analyze Data, and Interpret Data. [1]

Table of Contents and Core Topics

Descriptive Statistics & Data Gathering

Chapter 1: Introduction to Data – Variables, data classification, storing data, organizing categorical data, and understanding causality. [1]
Chapter 2: Picturing Variation with Graphs – Bar charts, pie charts, histograms, dot plots, and stem-and-leaf plots. [1, 2]
Chapter 3: Numerical Summaries of Center and Variation – Mean, median, mode, variance, standard deviation, and boxplots. [1, 2]
Chapter 4: Regression Analysis: Exploring Associations between Variables – Scatterplots, linear correlation, line of best fit, and regression models. [1, 2, 3, 4]

Probability & Modeling

Chapter 5: Modeling Variation with Probability – Basic probability definitions, sample space, and probability rules.
Chapter 6: Modeling Random Events: The Normal and Binomial Models – Continuous variables, normal distributions, discrete variables, and binomial experiments. [1, 2, 3, 4, 5]

Inferential Statistics

Chapter 7: Survey Sampling and Inference – Sampling distributions, sample design, bias, and the Central Limit Theorem.
Chapter 8: Hypothesis Testing for Population Proportions – Core concepts of hypothesis tests, p-values, significance levels, and z-tests for single proportions.
Chapter 9: Inferring Population Means – Student’s t-distribution, confidence intervals, and hypothesis tests for a single mean or matched pairs. [1, 2, 3, 4, 5]

Advanced Statistical Inference

Chapter 10: Associations between Categorical Variables – Chi-square test for goodness-of-fit and independence.
Chapter 11: Multiple Comparisons and Analysis of Variance – One-way ANOVA to evaluate and compare three or more population means.
Chapter 12: Experimental Design: Controlling Variation – Principles of designed experiments, confounding variables, and variance control.
Chapter 13: Inference without Normality – Non-parametric tests and bootstrapping for skewed datasets.
Chapter 14: Inference for Regression – Evaluating the slope of population regression models and predictions. [1, 2, 3, 4]

Technology Tools Covered

The textbook includes tailored "Tech Tips" at the conclusion of relevant sections. These step-by-step guides explain how to run data calculations across several major computing tools: [1]