Normal view MARC view

Introduction to the practice of statistics

Author: Moore, David S. ; McCabe, GeorgePublisher: Freeman, 2006.Edition: 5th ed.Language: EnglishDescription: 800 p. + appendices : Graphs/Photos ; 26 cm. includes CD-ROM / DVDISBN: 071676282XType of document: BookNote: 1 CD-ROM included inside back cover of the book Bibliography/Index: Includes bibliographical references and index
Tags: No tags from this library for this title. Log in to add tags.
Item type Current location Collection Call number Status Date due Barcode Item holds
Book Asia Campus
Main Collection
Print QA276 .M66 2006
(Browse shelf)
Available 900174336
Total holds: 0

1 CD-ROM included inside back cover of the book

Includes bibliographical references and index


Introduction to the practice of statistics T Teachers: About This Book o T Students: What Is Statistics? o About the Authors Xlll ... xxxi xxxv Properties of the standard deviation Choosing measures of center and spread Changing the unit of measurement S ~ ~ c l i o n Summary 1.2 Section '1.2 Exercises Looking at Data CHAPTER II 1 1.3 Density Curves and Normal Distributions Density curves Measuring center and spread for density curves Normal distributions The 68-95-99.7 rule Standardizing observations Normal distribution calculations Using the standard normal table Inverse normal calculations Normal quantile plots t k y o n d it-it. basics: derisity estirnaiion Section 1.3 Summary Scctivri 1.3 Iixercises Chapter I F xercises EFSI-E a -. Studies . w Looking at Data-Distri butions Introduction Variables Measurement: know your variables 3 4 4 6 Displaying Distributions with Graphs Graphs for categorical variables Data analysis in action: don't hang up on me Stemplots Histograms Examining distributions Dealing with outliers Time plots Rcyond the bas~cs: dcroniposrng Imr serIc15 7 7 9 11 14 17 18 19 21 23 25 CHAPTER 12 S e c t m 1 1 'wrnrn~~ry S c c h n 1 I L'xtlrc~stls Looking at Data-Relationships Introduction Examining relationships 1.2 Describing Distributions with Numbers Measuring center: the mean Measuring center: the median Mean versus median Measuring spread: the quartiles The five-number summary and boxplots The 1.5 x IQR rule for suspected outliers Measuring spread: the standard deviation *Sections marked with an asterisk are optional. 40 40 42 43 44 46 47 49 Interpreting scatterplots Adding categorical variables to scatterplots More examples of scatterplots R c p i d thr b m i 5 . sr dtierplnt smoothcrs Categorical explanatory variables S r t Iron 2 1 Surnniary S t r tion 2.1 Lxcrt iscs 2.2 Correlation The correlation r Properties of correlation Sectirm 2.2 Summary Section 2.2 Exercises Design of Experiments Comparative experiments Randomization Randomized comparative experiments How to randomize Cautions about experimentation Matched pairs designs Block designs St.( tiori 3.2 Summdry Seitiorl 3.2 Sucrcrst.~ Least-Squares Regression Fitting a line to data Prediction Least-squares regression Interpreting the regression line Correlation and regression "Understanding r2 Beyond h e basics: t r a n s f )rrning relationships Section 2.3 Summary Section 2.3 Exercises Sampling Design Simple random samples Stratified samples Multistage samples Cautions about sample surveys Set tion 3 3 Summary Scrtion 3.3 Fxtlrcises Cautions about Correlation and Regression Residuals Outliers and influential observations Beware the lurking variable Beware correlations based on averaged data The restricted-range problem Beyond the basics: data mining Section 2.4 Surnrndry Sedion 2.4 Exercises Toward Statistical Inference Sampling variability Sampling distributions Bias and variability Sampling from large populations Why randomize? Beyond Ihe basics: capti~re-recapture sampling Section 3.4 Summary Seclion 3.4 Excrciscs Chapter .5 t x c w s e s W E E Case Stirdies The Question of Causation Explaining association: causation Explaining association: common response Explaining association: confounding Establishing causation Seclion 2.5 Surnniary . Section 2 5 Excrciscs Chapter 2 txcrcises EESEE Case Studies Probability and lnference Probability: The Study of Randomness Introduction 4.1 Randomness The-language of probability Thinking about randomness The uses of probabilitv CHAPTER 13 Producing Data Introduction 3.1 First Steps Where to find data: the library and the Internet Sampling Experiments Section 4.1 Summary Section 4.1 Exercises CHAPTER 259 15 4.2 Probability Models Sample spaces Intuitive probability Probability rules Assigning probabilities: finite number of outcomes Assigning probabilities: equally likely outcomes lndependenceand the multiplication rule Applying the probability rules Sectiori 4.2 Surnmary Section 4.2 Exercises Sampling Distributions lntroduction 5.1 Sampling Distributions for Counts and Proportions The binomial distributions for sample counts Binomial distributions in statistical sampling Finding binomial probabilities: software and tables Binomial mean and standard deviation Sample proportions Normal approximation for counts and proportions The continuity correction* Binomial formulas* Section 5.1 Summary Section 5.1 Exercises 4.3 Random Variables Discrete random variables Continuous random variables Normal distributions as probability distributions Section 4.3 Summary Section 4.3 Lxercises 277 278 282 284 286 286 5.2 The Sampling Distribution of a Sample Mean The mean and standard deviation of The central limit theorem A few more facts 4.4 Means and Variances of Random Variables The mean of a random variable Statistical estimation and the law of large numbers Thinking about the law of large numbers Beyond the basics: more laws of large numbers 291 x Beyond the basics: Weibull distributions Section 5.2 Summary Section 5.2 Exercises Chapter 5 Exercises EfjSEE Case Studies Rules for means The variance of a random variable Rules for variances Section 4.4 Sumlnary Section 4.4 Exercises Introduction to Inference lntroduction 311 6.1 Estimating with Confidence Statistical confidence Confidence intervals Confidence interval for a population mean How confidence intervals behave Choosing the sample size Some cautions Beyond lhc basics. thc bootslrap Section 6.1 Summary Section 6.1 Cxercises 4.5 General Probability Rules* General addition rules Conditional probability General multiplication rules Tree diagrams Bayes's rule lndependence again Section 4.5 Summary Section 4.5 Exer~ises Chapter 4 txercises W E E Case Studies 6.2 Tests of Significance The reasoning of significance tests Stating hypotheses Test statistics P-values Statistical significance Tests for a population mean Two-sided significance tests and confidence intervals P-values versus fixed a Section 6.2 Summary Section 6.2 Cxerciscs lnference for nonnormal populations* Section 7.1 Summary Section 7.1 Exercises Comparing Two Means The two-sample z statistic The two-sample t procedures The two-sample t significance test The two-sample t confidence interval Robustness of the two-sample procedures lnference for small samples Software approximation for the degrees of freedom* The pooled two-sample t procedures* Section 72 Summary Section 7.2 Exer~ises Use and Abuse of Tests Choosing a level of significance What statistical significance does not mean Don't ignore lack of significance Statistical inference is not valid for all sets of data Beware of searching for significance Scctiun 0.3 S ~ ~ r n m a r y 5t.c tion 6.3 Exercises Optional Topics in Comparing Distributions* lnference for population spread The F test for equality of spread Robustness of normal inference procedures The power of the two-sample t test Scrtion 7.3 Summdry Section 7.3 I:'xcrc.ises Chdptttr 7 Exercises EFStF Case Studies Power and lnference as a Decision* Power Increasing the power lnference as decision* Two types of error Error probabilities The common practice of testing hypotheses Scclion 6.4 S ~ ~ m ~ n a r y Sectiorl 6.4 f-xert ises Chapter 6 Exercises EESFE Case Studies 524 533 CHAPTER 18 535 536 lnference for Proportions Introduction lnference for a Single Proportion Large-sample confidence interval for a single proportion Plus four confidence interval for a single proportion Significance test for a single proportion Confidence intervals provide additional information Choosing a sample size Section 8.1 Summary Section 8.1 Exercises CHAPTER 17 lnference for Distributions Introduction 7.1 lnference for the Mean of a Population The t distributions The one-sample t confidence interval The one-sample t test Matched pairs t procedures Robustness of the t procedures The power of the t test* Comparing Two Proportions ~ a r ~ e - j a mconfidence interval ~le for a difference in proportions Plus four confidence interval for a difference in proportions Significance test for a difference in proportions Beyond the basics: relative risk Section 8.2 Summary Section 8.2 Exercises Chapter 8 Exercises EESEE Case Studies CHAPTER 1 10 lnference for Regression l ntroduction 10.1 Simple Linear Regression Statistical model for linear regression Data for simple linear regression Estimating the regression parameters Confidence intervals and significance tests Confidence intervals for mean response Prediction intervals Beyond the basics: nonlinear regression Section 10.1 Summary Topics in lnference CHAPTER 19 Analysis of Two-Way Tables lntroduction 9.1 Data Analysis for Two-Way Tables The two-way table Marginal distributions Describing relations in two-way tables Conditional distributions Simpson's paradox The perils of aggregation S r c t i w L).l S~mrnary 10.2 More Detail about Simple Linear Regression* Analysis of variance for regression The ANOVA F test Calculations for regression inference lnference for correlation S e ~ t i n n10.2 Sumrwary Chapter 10 Cxcrciscs LCSFE Casc Sludics Multiple Regression lntroduction 11.1 lnference for Multiple Regression Population multiple regression equation Data for multiple regression Multiple linear regression model Estimation of the multiple regression parameters Confidence intervals and significance tests for regression coefficients ANOVA table for multiple regression Squared multiple correlation R~ 9.2 lnference for Two-Way Tables The hypothesis: no association Expected cell counts The chi-square test The chi-square test and the z test Reyond the b ~ s i st mcto-analysis c Scction 9.2 Sumt~li~ry 9.3 Formulas and Models for Two-Way Tables Computations Computing conditional distributions Computing expected cell counts The x2 statistic and its P-value Models for two-way tables Concluding remarks Scction 9.3 S ~ m r n a r y 11.2 A Case Study Preliminary analysis Relationships between pairs of variables -Regression on high school grades Interpretation of results 9.4 Goodness of Fit* Residuals Refining the model Regression on SAT scores Regression using all variables Test for a collection of regression coefficients Beyond Ihe basics: multiple logistic regression Chapter 11 Sutnrridry Chapter 11 Fxerciscs EESEF Case Studies 13.2 lnference for Two-way ANOVA The ANOVA table for two-way ANOVA Chapter 13 Summary Chapter 13 Exerciscs Data Appendix Tables Solutions to Selected Exercises Notes and Data Sources Index CHAPTER ( 12 One-way Analysis of Variance lntroduction 12.1 Inference for One-way Analysis of Variance Data for one-way ANOVA Comparing means The two-sample t statistic An overview of ANOVA The ANOVA model Estimates of population parameters Testing hypotheses in one-way ANOVA The ANOVA table The F test Additional chapters are on CD-ROM and available in a separate print supplement. CHAPTER ( 14 Bootstrap Methods and permutation Tests lntroduction Software 14.1 The Bootstrap Idea The big idea: resampling and the bootstrap distribution Thinking about the bootstrap idea Using software Section 14.1 Summary Section 14.1 Exercises 12.2 Comparing the Means Contrasts Multiple comparisons Software Power* Chapter 12 Summary Chapter 12 Exercises EESEE Case Studies 14.2 First Steps in Using the Bootstrap Bootstrap t confidence intervals Bootstrapping to compare two groups Beyond the basics: the bootstrap for a scatterplot s m o o t l w Section 14.2 Summary Section 14.2 Exercises - Two-way Analysis o f Variance - Introduction 13.1 The Two-way ANQVA Model Advantages of two-way ANOVA The two-way ANOVA model Main effects and interactions 14.3 How Accurate Is a Bootstrap Distribution?* Bootstrapping small samples Bootstrapping a sample median Section 14.3 Summary Section 14.3 Exercises Bootstrap Confidence Intervals Bootstrap percentile confidence intervals Confidence intervals for the correlation More accurate bootstrap confidence intervals: BCa and tilting Section 14 4 Sumrnary Section 14.4 Exercises CHAPTER ) 16 16-1 Logistic Regression lntroduction 16.1 The Logistic Regression M o d e l Binomial distributions and odds Model for logistic regression Fitting and interpreting the logistic regression model 16.2 lnference f o r Logistic Regression Confidence intervals and significance tests Multiple logistic regression 14-63 14-hC) Chapter 16 Summary Chapter 16 Exercises Chapter I b Notes Significance Testing Using Permutation Tests Using software Permutation tests in practice Permutation tests in other settings Section 14.5 Sumrnary Section 14.5 Exercises Chapter 14 Lxerciws Chapter 14 Note5 L ~onpara'metric Tests lntroduction The Wilcoxon Rank Sum Test The rank transformation The Wilcoxon rank sum test The normal approximation What hypotheses does Wilcoxon test? Ties Rank, t, and permutation tests S c c t ~ o n15.1 Surnniary Section 15 l t x e r r i w s 15-1 15-2 15-3 15-4 15-5 15-7 15-8 15-10 15-13 1.5- 14 15-14 I Statistics for Quality: Control a n d capabdity lntroduction 17.1 Processes a n d Statistical Process Control Describing processes Statistical process control x charts for process monitoring s charts for process monitoring S e c t ~ o n17.1 Sumnidry Sedion 17.1 t-xercisrs 15.2 The Wilcoxon Signed Rank Test The normal approximation Ties Scition 15.2 Siammclry Section 15.2 Exercises 17.2 U s i n g Control Charts T and R charts Additional out-of-control signals Setting up control charts Comments on statistical control Don't confuse control with capability! c c t l o n 17.2 Summary Section 172 t x e r c w s 17-23 17-23 17-24 17-26 17-30 17-34 17-35 17-56 15.3 The Kruskal-Wallis Test Hypotheses and assumptions The Kruskal-N'allis test Scc t i o n 15 3 S u n l m q Scc t l o n I?3 l ~ e r c ~ s e s C Ciapter 15 Exerc i s m Chapter 15 Note\ 17.3 Process Capability Indexes The capability indexes Cp and Cpk Cautions about capability indexes Stlctron 17.3 Surnmdry Seclion 173 Lxercisrs 17-41 17-44 17-46 17-48 17 48 17.4 Control Charts for Sample Proportions Control limits for p charts 17-53 17-54 S t ~ c t i n ~ l ,~iltnmary 174 S t ~ t i u n17.4 F.xcrcrscs I h p t t ~17 F~~rt.isc.5 r c t q t c r 17 Notcs

There are no comments for this item.

Log in to your account to post a comment.
Koha 18.11 - INSEAD Catalogue
Home | Contact Us | What's Koha?