Introduction to the Practice of Statistics w/CD-ROM

by Moore, David S.; McCabe, George

• ISBN13: 9780716764007
• ISBN10: 0716764008
• Edition: 5th
• Format: Display
• Copyright: 2005-02-01
• Publisher: W. H. Freeman

Summary

With its focus on data analysis, statistical reasoning, and the way statisticians actually work, Introduction to the Practice of Statistics (IPS) helped bring the power of critical thinking and practical applications to today's statistics classroom. Unlike more traditional ?plug and chug? /formula driven texts, IPS de-emphasizes probability and gives students a deeper understanding of statistics.

Table of Contents

To Teachers: About This Book	xiii
To Students: What Is Statistics?	xxxi
About the Authors	xxxv
PART 1 Looking at Data	1	(250)
Looking at Data---Distributions	3	(98)
Introduction	4	(3)
Variables	4	(2)
Measurement: know your variables	6	(1)
Displaying Distributions with Graphs	7	(33)
Graphs for categorical variables	7	(2)
Data analysis in action: don't hang up on me	9	(2)
Stemplots	11	(3)
Histograms	14	(3)
Examining distributions	17	(1)
Dealing with outliers	18	(1)
Time plots	19	(2)
Beyond the basics: decomposing time series	21	(2)
Section 1.1 Summary	23	(2)
Section 1.1 Exercises	25	(15)
Describing Distributions with Numbers	40	(24)
Measuring center: the mean	40	(2)
Measuring center: the median	42	(1)
Mean versus median	43	(1)
Measuring spread: the quartiles	44	(2)
The five-number summary and boxplots	46	(1)
The 1.5 x IQR rule for suspected outliers	47	(2)
Measuring spread: the standard deviation	49	(2)
Properties of the standard deviation	51	(1)
Choosing measures of center and spread	52	(1)
Changing the unit of measurement	53	(2)
Section 1.2 Summary	55	(1)
Section 1.2 Exercises	56	(8)
Density Curves and Normal Distributions	64	(37)
Density curves	65	(3)
Measuring center and spread for density curves	68	(1)
Normal distributions	69	(2)
The 68--95--99.7 rule	71	(2)
Standardizing observations	73	(1)
Normal distribution calculations	74	(2)
Using the standard normal table	76	(2)
Inverse normal calculations	78	(2)
Normal quantile plots	80	(3)
Beyond the basics: density estimation	83	(1)
Section 1.3 Summary	83	(1)
Section 1.3 Exercises	84	(10)
Chapter 1 Exercises	94	(5)
EESEE Case Studies	99	(2)
Looking at Data---Relationships	101	(90)
Introduction	102	(2)
Examining relationships	102	(2)
Scatterplots	104	(19)
Interpreting scatterplots	105	(1)
Adding categorical variables to scatterplots	106	(1)
More examples of scatterplots	107	(3)
Beyond the basics: scatterplot smoothers	110	(1)
Categorical explanatory variables	111	(1)
Section 2.1 Summary	112	(1)
Section 2.1 Exercises	112	(11)
Correlation	123	(9)
The correlation r	124	(1)
Properties of correlation	124	(3)
Section 2.2 Summary	127	(1)
Section 2.2 Exercises	127	(5)
Least-Squares Regression	132	(19)
Fitting a line to data	133	(1)
Prediction	134	(1)
Least-squares regression	135	(3)
Interpreting the regression line	138	(1)
Correlation and regression	138	(4)
Understanding r2	142	(1)
Beyond the basics: transforming relationships	143	(2)
Section 2.3 Summary	145	(1)
Section 2.3 Exercises	145	(6)
Cautions about Correlation and Regression	151	(22)
Residuals	151	(3)
Outliers and influential observations	154	(4)
Beware the lurking variable	158	(3)
Beware correlations based on averaged data	161	(1)
The restricted-range problem	161	(1)
Beyond the basics: data mining	162	(1)
Section 2.4 Summary	163	(1)
Section 2.4 Exercises	163	(10)
The Question of Causation	173	(18)
Explaining association: causation	174	(1)
Explaining association: common response	175	(1)
Explaining association: confounding	176	(1)
Establishing causation	177	(1)
Section 2.5 Summary	178	(1)
Section 2.5 Exercises	179	(2)
Chapter 2 Exercises	181	(7)
EESEE Case Studies	188	(3)
Producing Data	191	(60)
Introduction	192	(1)
First Steps	192	(6)
Where to find data: the library and the Internet	193	(1)
Sampling	194	(1)
Experiments	195	(1)
Section 3.1 Summary	196	(1)
Section 3.1 Exercises	197	(1)
Design of Experiments	198	(20)
Comparative experiments	200	(1)
Randomization	201	(1)
Randomized comparative experiments	202	(1)
How to randomize	203	(3)
Cautions about experimentation	206	(1)
Matched pairs designs	207	(1)
Block designs	208	(1)
Section 3.2 Summary	209	(1)
Section 3.2 Exercises	210	(8)
Sampling Design	218	(13)
Simple random samples	219	(1)
Stratified samples	220	(1)
Multistage samples	221	(1)
Cautions about sample surveys	222	(2)
Section 3.3 Summary	224	(1)
Section 3.3 Exercises	225	(6)
Toward Statistical Inference	231	(20)
Sampling variability	232	(1)
Sampling distributions	233	(3)
Bias and variability	236	(2)
Sampling from large populations	238	(1)
Why randomize?	238	(1)
Beyond the basics: capture-recapture sampling	239	(1)
Section 3.4 Summary	240	(1)
Section 3.4 Exercises	240	(6)
Chapter 3 Exercises	246	(3)
EESEE Case Studies	249	(2)
PART 2 Probability and Inference	251	(328)
Probability: The Study of Randomness	253	(80)
Introduction	254	(1)
Randomness	254	(5)
The language of probability	255	(1)
Thinking about randomness	256	(1)
The uses of probability	256	(1)
Section 4.1 Summary	257	(1)
Section 4.1 Exercises	257	(2)
Probability Models	259	(18)
Sample spaces	259	(2)
Intuitive probability	261	(1)
Probability rules	262	(2)
Assigning probabilities: finite number of outcomes	264	(1)
Assigning probabilities: equally likely outcomes	265	(1)
Independence and the multiplication rule	266	(3)
Applying the probability rules	269	(1)
Section 4.2 Summary	270	(1)
Section 4.2 Exercises	271	(6)
Random Variables	277	(14)
Discrete random variables	278	(4)
Continuous random variables	282	(2)
Normal distributions as probability distributions	284	(2)
Section 4.3 Summary	286	(1)
Section 4.3 Exercises	286	(5)
Means and Variances of Random Variables	291	(20)
The mean of a random variable	291	(3)
Statistical estimation and the law of large numbers	294	(2)
Thinking about the law of large numbers	296	(1)
Beyond the basics: more laws of large numbers	297	(1)
Rules for means	298	(2)
The variance of a random variable	300	(1)
Rules for variances	301	(3)
Section 4.4 Summary	304	(1)
Section 4.4 Exercises	305	(6)
General Probability Rules	311	(22)
General addition rules	312	(3)
Conditional probability	315	(4)
General multiplication rules	319	(1)
Tree diagrams	320	(1)
Bayes's rule	321	(1)
Independence again	322	(1)
Section 4.5 Summary	323	(1)
Section 4.5 Exercises	323	(5)
Chapter 4 Exercises	328	(3)
EESEE Case Studies	331	(2)
Sampling Distributions	333	(48)
Introduction	334	(1)
Sampling Distributions for Counts and Proportions	335	(23)
The binomial distributions for sample counts	335	(1)
Binomial distributions in statistical sampling	336	(1)
Finding binomial probabilities: software and tables	337	(3)
Binomial mean and standard deviation	340	(1)
Sample proportions	341	(2)
Normal approximation for counts and proportions	343	(4)
The continuity correction	347	(1)
Binomial formulas	348	(2)
Section 5.1 Summary	350	(1)
Section 5.1 Exercises	351	(7)
The Sampling Distribution of a Sample Mean	358	(23)
The mean and standard deviation of x	360	(2)
The central limit theorem	362	(3)
A few more facts	365	(2)
Beyond the basics: Weibull distributions	367	(1)
Section 5.2 Summary	368	(1)
Section 5.2 Exercises	369	(6)
Chapter 5 Exercises	375	(3)
EESEE Case Studies	378	(3)
Introduction to Inference	381	(68)
Introduction	382	(1)
Estimating with Confidence	383	(17)
Statistical confidence	384	(1)
Confidence intervals	385	(2)
Confidence interval for a population mean	387	(3)
How confidence intervals behave	390	(1)
Choosing the sample size	391	(2)
Some cautions	393	(1)
Beyond the basics: the bootstrap	394	(1)
Section 6.1 Summary	395	(1)
Section 6.1 Exercises	396	(4)
Tests of Significance	400	(24)
The reasoning of significance tests	400	(2)
Stating hypotheses	402	(1)
Test statistics	403	(1)
P-values	404	(2)
Statistical significance	406	(3)
Tests for a population mean	409	(4)
Two-sided significance tests and confidence intervals	413	(2)
P-values versus fixed α	415	(1)
Section 6.2 Summary	416	(1)
Section 6.2 Exercises	416	(8)
Use and Abuse of Tests	424	(6)
Choosing a level of significance	424	(1)
What statistical significance does not mean	425	(1)
Don't ignore lack of significance	425	(1)
Statistical inference is not valid for all sets of data	426	(1)
Beware of searching for significance	427	(1)
Section 6.3 Summary	427	(1)
Section 6.3 Exercises	428	(2)
Power and Inference as a Decision	430	(19)
Power	430	(3)
Increasing the power	433	(2)
Inference as decision	435	(1)
Two types of error	435	(1)
Error probabilities	436	(2)
The common practice of testing hypotheses	438	(1)
Section 6.4 Summary	439	(1)
Section 6.4 Exercises	439	(2)
Chapter 6 Exercises	441	(5)
EESEE Case Studies	446	(3)
Inference for Distributions	449	(86)
Introduction	450	(1)
Inference for the Mean of a Population	450	(35)
The t distributions	450	(2)
The one-sample t confidence interval	452	(2)
The one-sample t test	454	(5)
Matched pairs t procedures	459	(3)
Robustness of the t procedures	462	(2)
The power of the t test	464	(1)
Inference for nonnormal populations	465	(5)
Section 7.1 Summary	470	(1)
Section 7.1 Exercises	471	(14)
Comparing Two Means	485	(30)
The two-sample z statistic	486	(2)
The two-sample t procedures	488	(1)
The two-sample t significance test	489	(3)
The two-sample t confidence interval	492	(1)
Robustness of the two-sample procedures	493	(2)
Inference for small samples	495	(3)
Software approximation for the degrees of freedom	498	(1)
The pooled two-sample t procedures	499	(4)
Section 7.2 Summary	503	(1)
Section 7.2 Exercises	504	(11)
Optional Topics in Comparing Distributions	515	(20)
Inference for population spread	516	(1)
The F test for equality of spread	516	(2)
Robustness of normal inference procedures	518	(1)
The power of the two-sample t test	519	(2)
Section 7.3 Summary	521	(1)
Section 7.3 Exercises	521	(3)
Chapter 7 Exercises	524	(9)
EESEE Case Studies	533	(2)
Inference for Proportions	535	(44)
Introduction	536	(1)
Inference for a Single Proportion	536	(19)
Large-sample confidence interval for a single proportion	536	(3)
Plus four confidence interval for a single proportion	539	(1)
Significance test for a single proportion	540	(3)
Confidence intervals provide additional information	543	(2)
Choosing a sample size	545	(3)
Section 8.1 Summary	548	(1)
Section 8.1 Exercises	549	(6)
Comparing Two Proportions	555	(24)
Large-sample confidence interval for a difference in proportions	556	(2)
Plus four confidence interval for a difference in proportions	558	(3)
Significance test for a difference in proportions	561	(2)
Beyond the basics: relative risk	563	(2)
Section 8.2 Summary	565	(1)
Section 8.2 Exercises	566	(4)
Chapter 8 Exercises	570	(8)
EESEE Case Studies	578	(1)
PART 3 Topics in Inference	579
Analysis of Two-Way Tables	581	(52)
Introduction	582	(1)
Data Analysis for Two-Way Tables	582	(9)
The two-way table	582	(2)
Marginal distributions	584	(1)
Describing relations in two-way tables	585	(1)
Conditional distributions	586	(2)
Simpson's paradox	588	(2)
The perils of aggregation	590	(1)
Section 9.1 Summary	590	(1)
Inference for Two-Way Tables	591	(9)
The hypothesis: no association	594	(1)
Expected cell counts	594	(1)
The chi-square test	595	(3)
The chi-square test and the z test	598	(1)
Beyond the basics: meta-analysis	598	(2)
Section 9.2 Summary	600	(1)
Formulas and Models for Two-Way Tables	600	(9)
Computations	600	(1)
Computing conditional distributions	601	(3)
Computing expected cell counts	604	(1)
The X2 statistic and its P-value	605	(1)
Models for two-way tables	606	(2)
Concluding remarks	608	(1)
Section 9.3 Summary	608	(1)
Goodness of Fit	609	(24)
Chapter 9 Exercises	612	(19)
EESEE Case Studies	631	(2)
Inference for Regression	633	(50)
Introduction	634	(1)
Simple Linear Regression	634	(18)
Statistical model for linear regression	634	(1)
Data for simple linear regression	635	(4)
Estimating the regression parameters	639	(5)
Confidence intervals and significance tests	644	(2)
Confidence intervals for mean response	646	(2)
Prediction intervals	648	(2)
Beyond the basics: nonlinear regression	650	(1)
Section 10.1 Summary	651	(1)
More Detail about Simple Linear Regression	652	(31)
Analysis of variance for regression	653	(2)
The ANOVA F test	655	(1)
Calculations for regression inference	656	(8)
Inference for correlation	664	(2)
Section 10.2 Summary	666	(1)
Chapter 10 Exercises	667	(14)
EESEE Case Studies	681	(2)
Multiple Regression	683	(36)
Introduction	684	(1)
Inference for Multiple Regression	684	(6)
Population multiple regression equation	684	(1)
Data for multiple regression	685	(1)
Multiple linear regression model	685	(1)
Estimation of the multiple regression parameters	686	(1)
Confidence intervals and significance tests for regression coefficients	687	(1)
ANOVA table for multiple regression	688	(2)
Squared multiple correlation R2	690	(1)
A Case Study	690	(29)
Preliminary analysis	690	(1)
Relationships between pairs of variables	691	(2)
Regression on high school grades	693	(2)
Interpretation of results	695	(1)
Residuals	695	(1)
Refining the model	696	(1)
Regression on SAT scores	697	(1)
Regression using all variables	698	(3)
Test for a collection of regression coefficients	701	(1)
Beyond the basics: multiple logistic regression	701	(1)
Chapter 11 Summary	702	(1)
Chapter 11 Exercises	703	(14)
EESEE Case Studies	717	(2)
One-Way Analysis of Variance	719	(52)
Introduction	720	(1)
Inference for One-Way Analysis of Variance	720	(17)
Data for one-way ANOVA	720	(1)
Comparing means	721	(2)
The two-sample t statistic	723	(1)
An overview of ANOVA	724	(2)
The ANOVA model	726	(2)
Estimates of population parameters	728	(2)
Testing hypotheses in one-way ANOVA	730	(2)
The ANOVA table	732	(2)
The F test	734	(3)
Comparing the Means	737	(34)
Contrasts	737	(5)
Multiple comparisons	742	(4)
Software	746	(2)
Power	748	(3)
Chapter 12 Summary	751	(1)
Chapter 12 Exercises	751	(18)
EESEE Case Studies	769	(2)
Two-Way Analysis of Variance	771
Introduction	772	(1)
The Two-Way ANOVA Model	772	(11)
Advantages of two-way ANOVA	772	(4)
The two-way ANOVA model	776	(1)
Main effects and interactions	777	(6)
Inference for Two-Way ANOVA	783
The ANOVA table for two-way ANOVA	783	(4)
Chapter 13 Summary	787	(1)
Chapter 13 Exercises	788
Data Appendix	1	(1)
Tables	1	(1)
Solutions to Selected Exercises	1	(1)
Notes and Data Sources	1	(1)
Index	1	(1)
Bootstrap Methods and Permutation Tests	1	(1)
Introduction	2	(1)
Software	2	(1)
The Bootstrap Idea	3	(10)
The big idea: resampling and the bootstrap distribution	5	(3)
Thinking about the bootstrap idea	8	(2)
Using software	10	(1)
Section 14.1 Summary	11	(1)
Section 14.1 Exercises	12	(1)
First Steps in Using the Bootstrap	13	(14)
Bootstrap t confidence intervals	14	(4)
Bootstrapping to compare two groups	18	(2)
Beyond the basics: the bootstrap for a scatterplot smoother	20	(3)
Section 14.2 Summary	23	(1)
Section 14.2 Exercises	23	(4)
How Accurate Is a Bootstrap Distribution?	27	(7)
Bootstrapping small samples	29	(2)
Bootstrapping a sample median	31	(2)
Section 14.3 Summary	33	(1)
Section 14.3 Exercises	33	(1)
Bootstrap Confidence Intervals	34	(12)
Bootstrap percentile confidence intervals	34	(1)
Confidence intervals for the correlation	35	(3)
More accurate bootstrap confidence intervals: BCa and tilting	38	(4)
Section 14.4 Summary	42	(1)
Section 14.4 Exercises	42	(4)
Significance Testing Using Permutation Tests	46
Using software	50	(1)
Permutation tests in practice	50	(4)
Permutation tests in other settings	54	(3)
Section 14.5 Summary	57	(1)
Section 14.5 Exercises	57	(6)
Chapter 14 Exercises	63	(6)
Chapter 14 Notes	69
Nonparametric Tests	1	(1)
Introduction	2	(1)
The Wilcoxon Rank Sum Test	3	(16)
The rank transformation	4	(1)
The Wilcoxon rank sum test	5	(2)
The normal approximation	7	(1)
What hypotheses does Wilcoxon test?	8	(2)
Ties	10	(3)
Rank, t, and permutation tests	13	(1)
Section 15.1 Summary	14	(1)
Section 15.1 Exercises	14	(5)
The Wilcoxon Signed Rank Test	19	(9)
The normal approximation	21	(1)
Ties	22	(2)
Section 15.2 Summary	24	(1)
Section 15.2 Exercises	24	(4)
The Kruskal-Wallis Test	28
Hypotheses and assumptions	29	(1)
The Kruskal-Wallis test	30	(2)
Section 15.3 Summary	32	(1)
Section 15.3 Exercises	33	(3)
Chapter 15 Exercises	36	(3)
Chapter 15 Notes	39
Logistic Regression	1	(1)
Introduction	2	(1)
The Logistic Regression Model	2	(5)
Binomial distributions and odds	2	(2)
Model for logistic regression	4	(2)
Fitting and interpreting the logistic regression model	6	(1)
Inference for Logistic Regression	7
Confidence intervals and significance tests	8	(7)
Multiple logistic regression	15	(1)
Chapter 16 Summary	16	(1)
Chapter 16 Exercises	16	(9)
Chapter 16 Notes	25
Statistics for Quality: Control and Capability	1	(62)
Introduction	2	(1)
Processes and Statistical Process Control	3	(20)
Describing processes	3	(3)
Statistical process control	6	(1)
x charts for process monitoring	7	(4)
s charts for process monitoring	11	(6)
Section 17.1 Summary	17	(1)
Section 17.1 Exercises	17	(6)
Using Control Charts	23	(18)
x and R charts	23	(1)
Additional out-of-control signals	24	(2)
Setting up control charts	26	(4)
Comments on statistical control	30	(4)
Don't confuse control with capability!	34	(1)
Section 17.2 Summary	35	(1)
Section 17.2 Exercises	36	(5)
Process Capability Indexes	41	(12)
The capability indexes Cp and Cpk	44	(2)
Cautions about capability indexes	46	(2)
Section 17.3 Summary	48	(1)
Section 17.3 Exercises	48	(5)
Control Charts for Sample Proportions	53	(10)
Control limits for p charts	54	(4)
Section 17.4 Summary	58	(1)
Section 17.4 Exercises	58	(2)
Chapter 17 Exercises	60	(3)
Chapter 17 Notes	63