# Prob/Stats Introduction to the Practice of Statistics by David Moore

## For those who have used this book

100.0%

0 vote(s)
0.0%

0 vote(s)
0.0%
4. ### Strongly don't Recommend

0 vote(s)
0.0%
1. Jan 20, 2013

### Greg Bernhardt

The Basic Practice of Statistics by David Moore

Code (Text):

[LIST]
[*] 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
[*] 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
[/LIST]
[*] Regression
[LIST]
[*] Regression lines
[*] The least-squares regression line
[*] Using technology
[*] Residuals
[*] Influential observations
[*] Cautions about correlation and regression
[*] Association does not imply causation
[/LIST]
[*] Two-Way Tables
[LIST]
[*] Marginal distributions
[*] Conditional distributions
[/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
[*] Simple random samples
[*] Other sampling designs
[/LIST]
[*] Producing Data: Experiments
[LIST]
[*] Experiments
[*] Randomized comparative experiments
[*] The logic of randomized comparative experiments
[*] 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 $\overline{x}$
[*] The central limit theorem
[*] Statistical process control
[*] $\overline{x}$ charts
[/LIST]
[*] General Rules of Probability
[LIST]
[*] Independence and the multiplication 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 $\mu$
[*] 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 $z$ procedures
[*] 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]
[LIST]
[*] Inference about a Population Mean
[LIST]
[*] Conditions for inference
[*] The $t$ distributions
[*] The one-sample $t$ confidence interval
[*] The one-sample $t$ test
[*] Using technology
[*] Matched pairs $t$ procedures
[*] Robustness of $t$ procedures
[/LIST]
[*] Two-Sample Problems
[LIST]
[*] Two-sample problems
[*] Comparing two population means
[*] Two-sample $t$ procedures
[*] Examples of the two-sample $t$ procedures
[*] Using technology
[*] Robustness again
[*] Details of the $t$ approximation
[*] Avoid the pooled two-sample $t$ procedures
[*] Avoid inference about standard deviations
[*] The $F$ test for comparing two standard deviations
[/LIST]
[*] Inference about a Population Proportion
[LIST]
[*] The sample proportion $\hat{p}$
[*] The sampling distribution of $\hat{p}$
[*] 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]
[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 $z$ 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
[*] Checking the conditions for inference
[/LIST]
[*] One-Way Analysis of Variance: Comparing Several Means
[LIST]
[*] comparing several means
[*] The analysis of variance $F$ test
[*] Using technology
[*] The idea of analysis of variance
[*] Conditions for ANOVA
[*] $F$ 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
[*] $t$ distribution critical values
[*] $F$ distribution critical values
[*] Chi-square distribution critical values
[*] Critical values of the correlation $r$
[/LIST]
[*] 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 $W$
[*] Using technology
[*] What hypotheses does Wilcoxon test
[*] Dealing with ties in rank tests
[*] Matched pairs: the Wilcoxon signed rank test
[*] The normal approximation for $W^+$
[*] 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
[*] $\overline{x}$ charts for process monitoring
[*] $s$ charts for process monitoring
[*] Using control charts
[*] Setting up control charts
[*] Don't confuse control with capability!
[*] Control charts for sample proportions
[*] Control limits for $p$ 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]

Last edited: May 6, 2017
2. Jan 22, 2013

### I like Serena

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: Jan 22, 2013