9781319042578

The Basic Practice of Statistics

by ; ;
  • ISBN13:

    9781319042578

  • ISBN10:

    1319042570

  • Edition: 8th
  • Format: Hardcover
  • Copyright: 2017-12-22
  • Publisher: W. H. Freeman

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Supplemental Materials

What is included with this book?

Summary

Prepare yourself to carry out regular statistical procedures and follow statistical reasoning regardless of your field of study as Basic Practice of Statistics readies you for success in college and beyond.


Table of Contents

PART I: EXPLORING DATA


Chapter 0 Getting Started


Where data comes from matters


Always look at the data


Variation is everywhere


What lies ahead in this book


Chapter 1 Picturing Distributions with Graphs


1.1 Individuals and variables


1.2 Categorical variables: Pie charts and bar graphs


1.3 Quantitative variables: Histograms


1.4 Interpreting histograms


1.5 Quantitative variables: Stemplots


1.6 Time plots


Chapter 2 Describing Distributions with Numbers


2.1 Measuring center: The mean


2.2 Measuring center: The median


2.3 Comparing the mean and the median


2.4 Measuring spread: The quartiles


2.5 The five-number summary and boxplots


2.6 Spotting suspected outliers*


2.7 Measuring spread: The standard deviation


2.8 Choosing measures of center and spread


2.9 Using technology


2.10 Organizing a statistical problem


Chapter 3 The Normal Distributions


3.1 Density curves


3.2 Describing density curves


3.3 Normal distributions


3.4 The 68-95-99.7 rule


3.5 The standard Normal distribution


3.6 Finding Normal proportions


3.7 Using the standard Normal table


3.8 Finding a value given a proportion


Chapter 4 Scatterplots and Correlation


4.1 Explanatory and response variables


4.2 Displaying relationships: Scatterplots


4.3 Interpreting scatterplots


4.4 Adding categorical variables to scatterplots


4.5 Measuring linear association: Correlation


4.6 Facts about correlation


Chapter 5 Regression


5.1 Regression lines


5.2 The least-squares regression line


5.3 Using technology


5.4 Facts about least-squares regression


5.5 Residuals


5.6 Influential observations


5.7 Cautions about correlation and regression


5.8 Association does not imply causation


5.9 Correlation, prediction, and big data*


Chapter 6 Two-Way Tables*


6.1 Marginal distributions


6.2 Conditional distributions


6.3 Simpson's paradox


Chapter 7 Exploring Data: Part I Review


Part I Summary


Test Yourself


Supplementary Exercises



PART II: PRODUCING DATA


Chapter 8 Producing Data: Sampling


8.1 Population versus sample


8.2 How to sample badly


8.3 Simple random samples


8.4 Inference about the population


8.5 Other sampling designs


8.6 Cautions about sample surveys


8.7 The impact of technology


Chapter 9 Producing Data: Experiments


9.1 Observation versus experiment


9.2 Subjects, factors, treatments


9.3 How to experiment badly


9.4 Randomized comparative experiments


9.5 The logic of randomized comparative experiments


9.6 Cautions about experimentation


9.7 Matched pairs and other block designs


Chapter 10 Data Ethics*


10.1 Institutional review boards


10.2 Informed consent


10.3 Confidentiality


10.4 Clinical trials


10.5 Behavioral and social science experiments


Chapter 11 Producing Data: Part II Review


Part II summary


Test yourself


Supplementary exercises



PART III: FROM DATA PRODUCTION TO INFERENCE


Chapter 12 Introducing Probability


12.1 The idea of probability


12.2 The search for randomness*


12.3 Probability models


12.4 Probability rules


12.5 Discrete probability models


12.6 Continuous probability models


12.7 Random variables


12.8 Personal probability*


Chapter 13 General Rules of Probability*


13.1 The general addition rule


13.2 Independence and the multiplication rule


13.3 Conditional probability


13.4 The general multiplication rule


13.5 Showing events are independent


13.6 Tree diagrams


13.7 Bayes' rule*


Chapter 14 Binomial Distributions*


14.1 The binomial setting and binomial distributions


14.2 Binomial distributions in statistical sampling


14.3 Binomial probabilities


14.4 Using technology


14.5 Binomial mean and standard deviation


14.6 The Normal approximation to binomial distributions


Chapter 15 Sampling Distributions


15.1 Parameters and statistics


15.2 Statistical estimation and the law of large numbers


15.3 Sampling distributions


15.4 The sampling distribution of x


15.5 The central limit theorem


15.6 Sampling distributions and statistical significance*


Chapter 16 Confidence Intervals: The Basics


16.1 The reasoning of statistical estimation


16.2 Margin of error and confidence level


16.3 Confidence intervals for a population mean


16.4 How confidence intervals behave


Chapter 17 Tests of Significance: The Basics


17.1 The reasoning of tests of significance


17.2 Stating hypotheses


17.3 P-value and statistical significance


17.4 Tests for a population mean


17.5 Significance from a table*


Chapter 18 Inference in Practice


18.1 Conditions for inference in practice


18.2 Cautions about confidence intervals


18.3 Cautions about significance tests


18.4 Planning studies: Sample size for confidence intervals


18.5 Planning studies: The power of a statistical test*


Chapter 19 From Data Production to Inference: Part III Review


Part III Summary


Review Exercises


Test Yourself


Supplementary Exercises



PART IV: INFERENCE ABOUT VARIABLES


Chapter 20 Inference about a Population Mean


20.1 Conditions for inference about a mean


20.2 The t distributions


20.3 The one-sample t confidence interval


20.4 The one-sample t test


20.5 Using technology


20.6 Matched pairs t procedures


20.7 Robustness of t procedures


Chapter 21 Comparing Two Means


21.1 Two-sample problems


21.2 Comparing two population means


21.3 Two-sample t procedures


21.4 Using technology


21.5 Robustness again


21.6 Details of the t approximation*


21.7 Avoid the pooled two-sample t procedures*


21.8 Avoid inference about standard deviations*


Chapter 22 Inference about a Population Proportion


22.1 The sample proportion


22.2 Large-sample confidence intervals for a proportion


22.3 Choosing the sample size


22.4 Significance tests for a proportion


22.5 Plus four confidence intervals for a proportion*


Chapter 23 Comparing Two Proportions


23.1 Two-sample problems: Proportions


23.2 The sampling distribution of a difference between proportions


23.3 Large-sample confidence intervals for comparing proportions


23.4 Using technology


23.5 Significance tests for comparing proportions


23.6 Plus four confidence intervals for comparing proportions*


Chapter 24 Inference about Variables: Part IV Review


Part IV summary


Test yourself


Supplementary exercises



PART V: INFERENCE ABOUT RELATIONSHIPS


Chapter 25 Two Categorical Variables: The Chi-Square Test


25.1 Two-way tables


25.2 The problem of multiple comparisons


25.3 Expected counts in two-way tables


25.4 The chi-square test statistic


25.5 Using technology


25.6 The chi-square distributions


25.7 Cell counts required for the chi-square test


25.8 Uses of the chi-square test: Independence and homogeneity


25.9 The chi-square test for goodness of fit*


Chapter 26 Inference for Regression


26.1 Conditions for regression inference


26.2 Estimating the parameters


26.3 Using technology


26.4 Testing the hypothesis of no linear relationship


26.5 Testing lack of correlation


26.6 Confidence intervals for the regression slope


26.7 Inference about prediction


26.8 Checking the conditions for inference


Chapter 27 One-Way Analysis of Variance:


Comparing Several Means


27.1 Comparing several means


27.2 The analysis of variance F test


27.3 Using technology


27.4 The idea of analysis of variance


27.5 Conditions for ANOVA


27.6 F distributions and degrees of freedom


27.7 Follow-up analysis: Tukey pairwise multiple comparisons


27.8 Some details of ANOVA*



[Back matter print text]


Notes and Data Sources


Tables


TABLE A Standard Normal probabilities


TABLE B Random digits


TABLE C t distribution critical values


TABLE D Chi-square distribution critical values


TABLE E Critical values of the correlation r


Answers to Selected Exercises


Index



PART VI: OPTIONAL COMPANION CHAPTERS


(available online)


Chapter 28 Nonparametric Tests


28.1 Comparing two samples: The Wilcoxon rank sum test


28.2 The Normal approximation for W


28.3 Using technology


28.4 What hypotheses does Wilcoxon test?


28.5 Dealing with ties in rank tests


28.6 Matched pairs: The Wilcoxon signed rank test


28.7 The Normal approximation for W+


28.8 Dealing with ties in the signed rank test


28.9 Comparing several samples: The Kruskal-Wallis test


28.10 Hypotheses and conditions for the Kruskal-Wallis test


28.11 The Kruskal-Wallis test statistic


Chapter 29 Multiple Regression


29.1 Parallel regression lines


29.2 Estimating parameters


29.3 Using technology


29.4 Inference for multiple regression


29.5 Interaction


29.6 The multiple linear regression model


29.7 The woes of regression coefficients


29.8 A case study for multiple regression


29.9 Inference for regression parameters


29.10 Checking the conditions for inference


Chapter 30 More about Analysis of Variance


30.1 Beyond one-way ANOVA


30.2 Two-way ANOVA: Conditions, main effects, and interaction


30.3 Inference for two-way ANOVA


30.4 Some details of two-way ANOVA*


Chapter 31 Statistical Process Control


31.1 Processes


31.2 Describing processes


31.3 The idea of statistical process control


31.4 x charts for process monitoring


31.5 s charts for process monitoring


31.6 Using control charts


31.6 Setting up control charts


31.7 Comments on statistical control


31.8 Don't confuse control with capability!


31.9 Control charts for sample proportions


31.10 Control limits for p charts


Chapter 32 Resampling: Permutation Tests and the Bootstrap


32.1 Randomization in experiments as a basis for inference


32.2 Permutation tests for comparing two treatments with software


32.3 Generating bootstrap samples


32.4 Bootstrap standard errors and confidence intervals

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