Introduction to the Practice of Statistics by David Moore

In summary, The Basic Practice of Statistics by David Moore covers statistical thinking, exploring data, describing distributions with numbers, normal distributions, scatterplots and correlation, regression, two-way tables, and more. It also includes chapters on producing data, probability, sampling distributions, general rules of probability, binomial distributions, confidence intervals, tests of significance, and inference about relationships. The book also covers nonparametric tests, statistical process control, multiple regression, and two-way analysis of variance (available online only). It requires prerequisites of Calculus 1, 2, 3, and Linear Algebra.

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The Basic Practice of Statistics by David Moore


Table of Contents:
Code:
[LIST]
[*] To the Instructor: About This Book
[*] To the Student: Statistical Thinking
[*] Exploring Data
[LIST]
[*] Picturing Distributions with Graphs
[LIST]
[*] Individuals and variables
[*] Categorical variables: pie charts and bar graphs
[*] Quantitative variables: histograms
[*] Interpreting histograms
[*] Quantitative variables: stemplots
[*] Time plots
[/LIST]
[*] Describing Distributions with Numbers
[LIST]
[*] Measuring center: the mean
[*] Measuring center: the median
[*] Comparing the mean and the median
[*] Measuring spread: the quartiles
[*] The five-number summary and boxplots
[*] Spotting suspected outliers
[*] Measuring spread: the standard deviation
[*] Choosing measures of center and spread
[*] Using technology
[*] Organizing a statistical problem
[/LIST]
[*] The normal distributions
[LIST]
[*] Density curves
[*] Describing density curves
[*] Normal distributions
[*] The 68-95-99.7 rule
[*] The standard normal distribution
[*] Finding normal proportions
[*] Using the standard normal table
[*] Finding a value given proportion
[/LIST]
[*] Scatterplots and Correlation
[LIST]
[*] Explanatory and response variables
[*] Displaying relationships: scatterplots
[*] Interpreting scatterplots
[*] Adding categorical variables to scatterplots
[*] Measuring linear association: correlation
[*] Facts about correlation
[/LIST]
[*] Regression
[LIST]
[*] Regression lines
[*] The least-squares regression line
[*] Using technology
[*] Facts about least-squares regression
[*] Residuals
[*] Influential observations
[*] Cautions about correlation and regression
[*] Association does not imply causation
[/LIST]
[*] Two-Way Tables
[LIST]
[*] Marginal distributions
[*] Conditional distributions
[*] Simpson's paradox
[/LIST]
[*] Exploring Data: Part I Review
[LIST]
[*] Part I summary
[*] Review exercises
[*] Supplementary exercises
[*] EESEE case studies
[/LIST]
[/LIST]
[*] From Exploration to Inference
[LIST]
[*] Producing Data: Sampling
[LIST]
[*] Observation versus experiment
[*] Sampling
[*] How to sample badly
[*] Simple random samples
[*] Other sampling designs
[*] Cautions about sample surveys
[*] Inference about the population
[/LIST]
[*] Producing Data: Experiments
[LIST]
[*] Experiments
[*] How to experiment badly
[*] Randomized comparative experiments
[*] The logic of randomized comparative experiments
[*] Cautions about experimentation
[*] Matched pairs and other block designs
[/LIST]
[*] Commentary: Data Ethics
[LIST]
[*] Institutional review boards
[*] Informed consent
[*] Confidentiality
[*] Clinical trials
[*] Behavioral and social science experiments
[/LIST]
[*] Introducing Probability
[LIST]
[*] The idea of probability
[*] Probability models
[*] Probability rules
[*] Discrete probability models
[*] Continuous probability models
[*] Random variables
[*] Personal probability
[/LIST]
[*] Sampling Distributions
[LIST]
[*] Parameters and statistics
[*] Statistical estimation and the law of large numbers
[*] Sampling distributions
[*] The sampling distribution of [itex]\overline{x}[/itex]
[*] The central limit theorem
[*] Statistical process control
[*] [itex]\overline{x}[/itex] charts
[*] Thinking about process control
[/LIST]
[*] General Rules of Probability
[LIST]
[*] Independence and the multiplication rule
[*] The general addition rule
[*] Conditional probability
[*] The general multiplication rule
[*] Independence
[*] Tree diagrams
[/LIST]
[*] Binomial Distributions
[LIST]
[*] The binomial setting and binomial distributions
[*] Binomial distributions in statistical sampling
[*] Binomial probabilities
[*] Using technology
[*] Binomial mean and standard deviation
[*] Then normal approximation to binomial distributions
[/LIST]
[*] Confidence Intervals: The Basics
[LIST]
[*] Estimating with confidence
[*] Confidence intervals for the mean [itex]\mu[/itex]
[*] How confidence intervals behave
[*] Choosing the sample size
[/LIST]
[*] Tests of Significance: The Basics
[LIST]
[*] The reasoning of tests of significance
[*] Stating hypotheses
[*] Test statistics
[*] P-values
[*] Statistical significance
[*] Tests for a population mean
[*] Using tables of critical values
[*] Tests from confidence intervals
[/LIST]
[*] Inference in Practice
[LIST]
[*] Where did the data come from?
[*] Cautions about the [itex]z[/itex] procedures
[*] Cautions about confidence intervals
[*] Cautions about significance tests
[*] The power of a test
[*] Type I and Type II errors
[/LIST]
[*] From Exploration to Inference: Part II Review
[LIST]
[*] Part II summary
[*] Review exercises
[*] Supplementary exercises
[*] Optional exercises
[*] EESEE case studies
[/LIST]
[/LIST]
[*] Inference about Variables
[LIST]
[*] Inference about a Population Mean
[LIST]
[*] Conditions for inference
[*] The [itex]t[/itex] distributions
[*] The one-sample [itex]t[/itex] confidence interval
[*] The one-sample [itex]t[/itex] test
[*] Using technology
[*] Matched pairs [itex]t[/itex] procedures
[*] Robustness of [itex]t[/itex] procedures
[/LIST]
[*] Two-Sample Problems
[LIST]
[*] Two-sample problems
[*] Comparing two population means
[*] Two-sample [itex]t[/itex] procedures
[*] Examples of the two-sample [itex]t[/itex] procedures
[*] Using technology
[*] Robustness again
[*] Details of the [itex]t[/itex] approximation
[*] Avoid the pooled two-sample [itex]t[/itex] procedures
[*] Avoid inference about standard deviations
[*] The [itex]F[/itex] test for comparing two standard deviations
[/LIST]
[*] Inference about a Population Proportion
[LIST]
[*] The sample proportion [itex]\hat{p}[/itex]
[*] The sampling distribution of [itex]\hat{p}[/itex]
[*] Large-sample confidence intervals for a proportion
[*] Accurate confidence intervals for a proportion
[*] Choosing the sample size
[*] Significance tests for a proportion
[/LIST]
[*] Comparing Two Proportions
[LIST]
[*] Two-sample problems: proportions
[*] The sampling distribution of a difference between proportions
[*] Large-sample confidence intervals for comparing proportions
[*] Using technology
[*] Accurate confidence intervals for comparing proportions
[*] Significance tests for comparing proportions
[/LIST]
[*] Inference about Variables: Part III Review
[LIST]
[*] Part III summary
[*] Review exercises
[*] Supplementary exercises
[*] EESEE case studies
[/LIST]
[/LIST]
[*] Inference about Relationships
[LIST]
[*] Two Categorical Variables: The Chi-Square Test
[LIST]
[*] Two-way tables
[*] The problem of multiple comparisons
[*] Expected counts in two-way tables
[*] The chi-square test
[*] Using technology
[*] Cell counts required for the chi-square test
[*] Uses of the chi-square test
[*] The chi-square distributions
[*] The chi-square and the [itex]z[/itex] test
[*] The chi-square test for goodness of fit
[/LIST]
[*] Inference for Regression
[LIST]
[*] Conditions for regression inference
[*] Estimating the parameters
[*] Using technology
[*] Testing the hypothesis of no linear relationship
[*] Testing lack of correlation
[*] Confidence intervals for the regression slope
[*] Inference about prediction
[*] Checking the conditions for inference
[/LIST]
[*] One-Way Analysis of Variance: Comparing Several Means
[LIST]
[*] comparing several means
[*] The analysis of variance [itex]F[/itex] test
[*] Using technology
[*] The idea of analysis of variance
[*] Conditions for ANOVA
[*] [itex]F[/itex] distributions and degrees of freedom
[*] Some details of ANOVA: the two-sample case
[*] Some details of ANOVA
[/LIST]
[*] Statistical Thinking Revisited
[*] Notes and Data Sources
[*] Tables
[LIST]
[*] Standard normal probabilities
[*] Random digits
[*] [itex]t[/itex] distribution critical values
[*] [itex]F[/itex] distribution critical values
[*] Chi-square distribution critical values
[*] Critical values of the correlation [itex]r[/itex]
[/LIST]
[*] Answers to Selected Exercises
[*] Index
[/LIST]
[*] Optional Companion Chapters (on the BPS CD and online)
[LIST]
[*] Nonparametric Tests
[LIST]
[*] Comparing two samples: the Wilcoxon rank sum test
[*] The normal approximation for [itex]W[/itex]
[*] Using technology
[*] What hypotheses does Wilcoxon test
[*] Dealing with ties in rank tests
[*] Matched pairs: the Wilcoxon signed rank test
[*] The normal approximation for [itex]W^+[/itex]
[*] Dealing with ties in the signed rank test
[*] Comparing several samples: the Kruskal-Wallis test
[/LIST]
[*] Statistical Process Control
[LIST]
[*] Processes
[*] Describing processes
[*] The idea of statistical process control
[*] [itex]\overline{x}[/itex] charts for process monitoring
[*] [itex]s[/itex] charts for process monitoring
[*] Using control charts
[*] Setting up control charts
[*] Comments on statistical control
[*] Don't confuse control with capability!
[*] Control charts for sample proportions
[*] Control limits for [itex]p[/itex] charts
[/LIST]
[*] Multiple Regression
[LIST]
[*] Parallel regression lines
[*] Estimating parameters
[*] Using technology
[*] Inference for multiple regression
[*] Interaction
[*] The multiple linear regression model
[*] The woes of regression coefficients
[*] A case study for multiple regression
[*] Inference for regression parameters
[*] Checking the conditions for inference
[/LIST]
[*] Two-Way Analysis of Variance (available online only)
[LIST]
[*] Extending the one-way ANOVA model
[*] Two-way ANOVA models
[*] Using technology
[*] Inference for two-way ANOVA
[*] Inference for a randomized block design
[*] Multiple comparisons
[*] Contrasts
[*] Conditions for two-way ANOVA
[/LIST]
[/LIST]
[/LIST]
 
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  • #2
I have tutored quite a few psychology students that were using this book by now.
It taught me quite a bit about applied statistics while I was teaching them.
It's known as Moore, McCabe, and Craig, or MMC for short.

Btw, I believe high school math is sufficient as prerequisite.
That is what my students have anyway.
 
Last edited:

FAQ: Introduction to the Practice of Statistics by David Moore

What is the main goal of "Introduction to the Practice of Statistics by David Moore"?

The main goal of "Introduction to the Practice of Statistics by David Moore" is to provide an accessible and comprehensive introduction to the field of statistics. It aims to help readers understand the fundamental principles and techniques of statistics and how they can be applied in various real-world situations.

Who is the target audience for this book?

This book is primarily targeted towards undergraduate students in fields such as mathematics, science, engineering, and social sciences who have little or no prior background in statistics. However, it can also be useful for anyone interested in learning the basics of statistics.

What topics are covered in this book?

This book covers a wide range of topics including data collection and organization, descriptive statistics, probability, sampling and sampling distributions, hypothesis testing, correlation and regression, and analysis of variance. It also includes a chapter on the use of statistical software.

Does this book require any prior knowledge of statistics?

No, this book does not require any prior knowledge of statistics. It starts with the basics and gradually builds upon them, making it suitable for readers with no prior background in the subject.

Are there any resources available to supplement the book?

Yes, there are various resources available to supplement the book, including online data sets, practice exercises, and interactive applets. The book also comes with a companion website that provides additional resources and support for both students and instructors.

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