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

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  1. Jan 20, 2013 #1
    The Basic Practice of Statistics by David Moore

    Table of Contents:
    Code (Text):

    [*] To the Instructor: About This Book
    [*] To the Student: Statistical Thinking
    [*] Exploring Data
    [*] Picturing Distributions with Graphs
    [*] Individuals and variables
    [*] Categorical variables: pie charts and bar graphs
    [*] Quantitative variables: histograms
    [*] Interpreting histograms
    [*] Quantitative variables: stemplots
    [*] Time plots
    [*] Describing Distributions with Numbers
    [*] 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
    [*] The normal distributions
    [*] 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
    [*] Scatterplots and Correlation
    [*] Explanatory and response variables
    [*] Displaying relationships: scatterplots
    [*] Interpreting scatterplots
    [*] Adding categorical variables to scatterplots
    [*] Measuring linear association: correlation
    [*] Facts about correlation
    [*] Regression
    [*] 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
    [*] Two-Way Tables
    [*] Marginal distributions
    [*] Conditional distributions
    [*] Simpson's paradox
    [*] Exploring Data: Part I Review
    [*] Part I summary
    [*] Review exercises
    [*] Supplementary exercises
    [*] EESEE case studies
    [*] From Exploration to Inference
    [*] Producing Data: Sampling
    [*] Observation versus experiment
    [*] Sampling
    [*] How to sample badly
    [*] Simple random samples
    [*] Other sampling designs
    [*] Cautions about sample surveys
    [*] Inference about the population
    [*] Producing Data: Experiments
    [*] Experiments
    [*] How to experiment badly
    [*] Randomized comparative experiments
    [*] The logic of randomized comparative experiments
    [*] Cautions about experimentation
    [*] Matched pairs and other block designs
    [*] Commentary: Data Ethics
    [*] Institutional review boards
    [*] Informed consent
    [*] Confidentiality
    [*] Clinical trials
    [*] Behavioral and social science experiments
    [*] Introducing Probability
    [*] The idea of probability
    [*] Probability models
    [*] Probability rules
    [*] Discrete probability models
    [*] Continuous probability models
    [*] Random variables
    [*] Personal probability
    [*] Sampling Distributions
    [*] 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
    [*] General Rules of Probability
    [*] Independence and the multiplication rule
    [*] The general addition rule
    [*] Conditional probability
    [*] The general multiplication rule
    [*] Independence
    [*] Tree diagrams
    [*] Binomial Distributions
    [*] 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
    [*] Confidence Intervals: The Basics
    [*] Estimating with confidence
    [*] Confidence intervals for the mean [itex]\mu[/itex]
    [*] How confidence intervals behave
    [*] Choosing the sample size
    [*] Tests of Significance: The Basics
    [*] 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
    [*] Inference in Practice
    [*] 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
    [*] From Exploration to Inference: Part II Review
    [*] Part II summary
    [*] Review exercises
    [*] Supplementary exercises
    [*] Optional exercises
    [*] EESEE case studies
    [*] Inference about Variables
    [*] Inference about a Population Mean
    [*] 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
    [*] Two-Sample Problems
    [*] 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
    [*] Inference about a Population Proportion
    [*] 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
    [*] Comparing Two Proportions
    [*] 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
    [*] Inference about Variables: Part III Review
    [*] Part III summary
    [*] Review exercises
    [*] Supplementary exercises
    [*] EESEE case studies
    [*] Inference about Relationships
    [*] Two Categorical Variables: The Chi-Square Test
    [*] 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
    [*] Inference for Regression
    [*] 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
    [*] One-Way Analysis of Variance: Comparing Several Means
    [*] 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
    [*] Statistical Thinking Revisited
    [*] Notes and Data Sources
    [*] Tables
    [*] 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]
    [*] Answers to Selected Exercises
    [*] Index
    [*] Optional Companion Chapters (on the BPS CD and online)
    [*] Nonparametric Tests
    [*] 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
    [*] Statistical Process Control
    [*] 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
    [*] Multiple Regression
    [*] 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
    [*] Two-Way Analysis of Variance (available online only)
    [*] 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
    Last edited: May 6, 2017
  2. jcsd
  3. Jan 22, 2013 #2

    I like Serena

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    Homework Helper

    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
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