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Introduction to the practice of statistics / David S. Moore, George P. McCabe

Main Author Moore, David S. Coauthor MacCabe, George P. Country Estados Unidos. Edition 4th ed Publication New York : W.H. Freeman, imp. 2003 Description 934 p., pag. var. : il. ; 27 cm + 1 Cd-Rom ISBN 0-7167-9657-0
0-7167-9658-9 (CD-Rom)
CDU 519.2
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Item type Current location Call number Status Date due Barcode Item holds
Monografia Biblioteca Geral da Universidade do Minho
BGUM 519.2 - M Available 322135
Produtos Computador Biblioteca Geral da Universidade do Minho
BGUM 519.2 - M Available 322136
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Enhanced descriptions from Syndetics:

With its focus on data analysis and the way statisticians actually work, Introduction to the Practice of Statistics (IPS) helped revolutionize general statistics. Freed from an overload of computations, students were able to see statistics as a revelatory way of understanding the world, and as an essential tool for any number of academic and professional fields. Thoroughly revised and fully media-connected, the new edition of IPS continues the revolution. Book jacket.

Table of contents provided by Syndetics

  • To Teachers: About This Book (p. ix)
  • To Students: What Is Statistics? (p. xxi)
  • About the Authors (p. xxv)
  • Part I Data (p. 1)
  • Chapter 1 Looking at Data--Distributions (p. 3)
  • Introduction (p. 4)
  • Variables (p. 4)
  • 1.1 Displaying Distributions with Graphs (p. 5)
  • Graphs for categorical variables (p. 6)
  • Measuring the speed of light (p. 7)
  • Measurement (p. 8)
  • Variation (p. 9)
  • Stemplots (p. 10)
  • Examining distributions (p. 12)
  • Histograms (p. 14)
  • Dealing with outliers (p. 17)
  • Time plots (p. 18)
  • Beyond the basics: Decomposing time series (p. 20)
  • Summary (p. 21)
  • Section 1.1 Exercises (p. 22)
  • 1.2 Describing Distributions with Numbers (p. 38)
  • Measuring center: the mean (p. 39)
  • Measuring center: the median (p. 40)
  • Mean versus median (p. 41)
  • Measuring spread: the quartiles (p. 42)
  • The five-number summary and boxplots (p. 43)
  • The 1.5 X IQR criterion for suspected outliers (p. 46)
  • Measuring spread: the standard deviation (p. 48)
  • Properties of the standard deviation (p. 50)
  • Choosing measures of center and spread (p. 50)
  • Changing the unit of measurement (p. 51)
  • Summary (p. 54)
  • Section 1.2 Exercises (p. 55)
  • 1.3 The Normal Distributions (p. 63)
  • Density curves (p. 64)
  • Measuring center and spread for density curves (p. 67)
  • Normal distributions (p. 68)
  • The 68-95-99.7 rule (p. 70)
  • Standardizing observations (p. 71)
  • The standard normal distribution (p. 73)
  • Normal distribution calculations (p. 74)
  • Normal quantile plots (p. 78)
  • Beyond the basics: Density estimation (p. 82)
  • Summary (p. 83)
  • Section 1.3 Exercises (p. 84)
  • Chapter 1 Exercises (p. 93)
  • Chapter 2 Looking at Data--Relationships (p. 103)
  • Introduction (p. 104)
  • Examining relationships (p. 104)
  • 2.1 Scatterplots (p. 106)
  • Interpreting scatterplots (p. 107)
  • Adding categorical variables to scatterplots (p. 108)
  • More examples of scatterplots (p. 109)
  • Beyond the basics: Scatterplot smoothers (p. 112)
  • Categorical explanatory variables (p. 113)
  • Summary (p. 115)
  • Section 2.1 Exercises (p. 116)
  • 2.2 Correlation (p. 126)
  • The correlation r (p. 127)
  • Properties of correlation (p. 128)
  • Summary (p. 130)
  • Section 2.2 Exercises (p. 131)
  • 2.3 Least-Squares Regression (p. 135)
  • Fitting a line to data (p. 136)
  • Prediction (p. 138)
  • Least-squares regression (p. 139)
  • Interpreting the regression line (p. 141)
  • Correlation and regression (p. 143)
  • Understanding r[superscript 2] (p. 146)
  • Summary (p. 147)
  • Section 2.3 Exercises (p. 147)
  • 2.4 Cautions about Regression and Correlation (p. 154)
  • Residuals (p. 154)
  • Lurking variables (p. 158)
  • Outliers and influential observations (p. 160)
  • Beware the lurking variable (p. 164)
  • Beware correlations based on averaged data (p. 165)
  • The restricted-range problem (p. 166)
  • Beyond the basics: Data mining (p. 167)
  • Summary (p. 168)
  • Section 2.4 Exercises (p. 168)
  • 2.5 The Question of Causation (p. 179)
  • Explaining association: causation (p. 180)
  • Explaining association: common response (p. 181)
  • Explaining association: confounding (p. 181)
  • Establishing causation (p. 182)
  • Summary (p. 184)
  • Section 2.5 Exercises (p. 185)
  • 2.6 Transforming Relationships (p. 187)
  • First steps in transforming (p. 189)
  • The ladder of power transformations (p. 191)
  • Exponential growth (p. 195)
  • The logarithm transformation (p. 197)
  • Prediction in the exponential growth model (p. 200)
  • Power law models (p. 201)
  • Prediction in power law models (p. 202)
  • Summary (p. 203)
  • Section 2.6 Exercises (p. 203)
  • Chapter 2 Exercises (p. 211)
  • Chapter 3 Producing Data (p. 221)
  • Introduction (p. 222)
  • 3.1 First Steps (p. 222)
  • Where to find data: the library and the Internet (p. 223)
  • Sampling (p. 225)
  • Experiments (p. 225)
  • Summary (p. 226)
  • Section 3.1 Exercises (p. 227)
  • 3.2 Design of Experiments (p. 229)
  • Comparative experiments (p. 230)
  • Randomization (p. 232)
  • Randomized comparative experiments (p. 233)
  • How to randomize (p. 235)
  • Cautions about experimentation (p. 237)
  • Matched pairs designs (p. 238)
  • Block designs (p. 239)
  • Summary (p. 240)
  • Section 3.2 Exercises (p. 241)
  • 3.3 Sampling Design (p. 248)
  • Simple random samples (p. 249)
  • Stratified samples (p. 250)
  • Multistage samples (p. 251)
  • Cautions about sample surveys (p. 252)
  • Summary (p. 254)
  • Section 3.3 Exercises (p. 255)
  • 3.4 Toward Statistical Inference (p. 260)
  • Sampling variability (p. 261)
  • Sampling distributions (p. 262)
  • Bias and variability (p. 265)
  • Sampling from large populations (p. 267)
  • Why randomize? (p. 267)
  • Beyond the basics: Capture-recapture sampling (p. 268)
  • Summary (p. 269)
  • Section 3.4 Exercises (p. 269)
  • Chapter 3 Exercises (p. 274)
  • Part II Probability and Inference (p. 279)
  • Chapter 4 Probability--The Study of Randomness (p. 281)
  • Introduction (p. 282)
  • 4.1 Randomness (p. 282)
  • The language of probability (p. 283)
  • Thinking about randomness (p. 284)
  • The uses of probability (p. 284)
  • Summary (p. 285)
  • Section 4.1 Exercises (p. 285)
  • 4.2 Probability Models (p. 287)
  • Sample spaces (p. 288)
  • Intuitive probability (p. 289)
  • Probability rules (p. 290)
  • Assigning probabilities: finite number of outcomes (p. 292)
  • Assigning probabilities: equally likely outcomes (p. 293)
  • Independence and the multiplication rule (p. 294)
  • Applying the probability rules (p. 296)
  • Summary (p. 298)
  • Section 4.2 Exercises (p. 298)
  • 4.3 Random Variables (p. 305)
  • Discrete random variables (p. 305)
  • Continuous random variables (p. 309)
  • Normal distributions as probability distributions (p. 312)
  • Summary (p. 313)
  • Section 4.3 Exercises (p. 314)
  • 4.4 Means and Variances of Random Variables (p. 318)
  • The mean of a random variable (p. 318)
  • Statistical estimation and the law of large numbers (p. 321)
  • Thinking about the law of large numbers (p. 323)
  • Beyond the basics: More laws of large numbers (p. 325)
  • Rules for means (p. 326)
  • The variance of a random variable (p. 328)
  • Rules for variances (p. 329)
  • Summary (p. 332)
  • Section 4.4 Exercises (p. 333)
  • 4.5 General Probability Rules (p. 340)
  • General addition rules (p. 340)
  • Conditional probability (p. 343)
  • General multiplication rules (p. 347)
  • Tree diagrams (p. 348)
  • Bayes's rule (p. 349)
  • Independence again (p. 350)
  • Decision analysis (p. 350)
  • Summary (p. 352)
  • Section 4.5 Exercises (p. 353)
  • Chapter 4 Exercises (p. 359)
  • Chapter 5 Sampling Distributions (p. 365)
  • Introduction (p. 366)
  • 5.1 Sampling Distributions for Counts and Proportions (p. 367)
  • The binomial distributions for sample counts (p. 367)
  • Binomial distributions in statistical sampling (p. 369)
  • Finding binomial probabilities: tables (p. 370)
  • Binomial mean and standard deviation (p. 371)
  • Sample proportions (p. 373)
  • Normal approximation for counts and proportions (p. 375)
  • The continuity correction (p. 379)
  • Binomial formulas (p. 380)
  • Summary (p. 382)
  • Section 5.1 Exercises (p. 383)
  • 5.2 The Sampling Distribution of a Sample Mean (p. 391)
  • The mean and standard deviation of x (p. 393)
  • The sampling distribution of x (p. 395)
  • The central limit theorem (p. 397)
  • Beyond the basics: Weibull distributions (p. 400)
  • Summary (p. 402)
  • Section 5.2 Exercises (p. 402)
  • Chapter 5 Exercises (p. 409)
  • Chapter 6 Introduction to Inference (p. 415)
  • Introduction (p. 416)
  • 6.1 Estimating with Confidence (p. 417)
  • Statistical confidence (p. 417)
  • Confidence intervals (p. 419)
  • Confidence interval for a population mean (p. 420)
  • How confidence intervals behave (p. 423)
  • Choosing the sample size (p. 425)
  • Some cautions (p. 426)
  • Beyond the basics: The bootstrap (p. 427)
  • Summary (p. 428)
  • Section 6.1 Exercises (p. 429)
  • 6.2 Tests of Significance (p. 435)
  • The reasoning of significance tests (p. 436)
  • Stating hypotheses (p. 437)
  • Test statistics (p. 439)
  • P-values (p. 440)
  • Statistical significance (p. 441)
  • Tests for a population mean (p. 444)
  • Two-sided significance tests and confidence intervals (p. 447)
  • P-values versus fixed [alpha] (p. 449)
  • Summary (p. 451)
  • Section 6.2 Exercises (p. 452)
  • 6.3 Use and Abuse of Tests (p. 461)
  • Choosing a level of significance (p. 461)
  • What statistical significance doesn't mean (p. 463)
  • Don't ignore lack of significance (p. 463)
  • Statistical inference is not valid for all sets of data (p. 464)
  • Beware of searching for significance (p. 465)
  • Summary (p. 466)
  • Section 6.3 Exercises (p. 466)
  • 6.4 Power and Inference as a Decision (p. 469)
  • Power (p. 470)
  • Increasing the power (p. 472)
  • Inference as decision (p. 474)
  • Two types of error (p. 475)
  • Error probabilities (p. 476)
  • The common practice of testing hypotheses (p. 478)
  • Summary (p. 479)
  • Section 6.4 Exercises (p. 479)
  • Chapter 6 Exercises (p. 483)
  • Chapter 7 Inference for Distributions (p. 491)
  • Introduction (p. 492)
  • 7.1 Inference for the Mean of a Population (p. 492)
  • The t distributions (p. 492)
  • The one-sample t confidence interval (p. 494)
  • The one-sample t test (p. 496)
  • Matched pairs t procedures (p. 501)
  • Robustness of the t procedures (p. 504)
  • The power of the t test (p. 505)
  • Inference for nonnormal populations (p. 506)
  • Summary (p. 511)
  • Section 7.1 Exercises (p. 512)
  • 7.2 Comparing Two Means (p. 525)
  • The two-sample z statistic (p. 526)
  • The two-sample t procedures (p. 528)
  • The two-sample t significance test (p. 529)
  • The two-sample t confidence interval (p. 532)
  • Robustness of the two-sample procedures (p. 533)
  • Inference for small samples (p. 534)
  • Software approximation for the degrees of freedom (p. 536)
  • The pooled two-sample t procedures (p. 537)
  • Summary (p. 542)
  • Section 7.2 Exercises (p. 543)
  • 7.3 Optional Topics in Comparing Distributions (p. 553)
  • Inference for population spread (p. 553)
  • The F test for equality of spread (p. 554)
  • Robustness of normal inference procedures (p. 556)
  • The power of the two-sample t test (p. 557)
  • Summary (p. 559)
  • Section 7.3 Exercises (p. 559)
  • Chapter 7 Exercises (p. 561)
  • Chapter 8 Inference for Proportions (p. 571)
  • Introduction (p. 572)
  • 8.1 Inference for a Single Proportion (p. 572)
  • Confidence interval for a single proportion (p. 572)
  • Significance test for a single proportion (p. 575)
  • Confidence intervals provide additional information (p. 577)
  • Choosing a sample size (p. 578)
  • Summary (p. 581)
  • Section 8.1 Exercises (p. 582)
  • 8.2 Comparing Two Proportions (p. 587)
  • Confidence intervals (p. 588)
  • Significance tests (p. 591)
  • Beyond the basics: Relative risk (p. 593)
  • Summary (p. 595)
  • Section 8.2 Exercises (p. 595)
  • Chapter 8 Exercises (p. 601)
  • Part III Topics in Inference (p. 609)
  • Chapter 9 Analysis of Two-Way Tables (p. 611)
  • Introduction (p. 612)
  • 9.1 Data Analysis for Two-Way Tables (p. 612)
  • The two-way table (p. 612)
  • Marginal distributions (p. 614)
  • Describing relations in two-way tables (p. 615)
  • Conditional distributions (p. 615)
  • Simpson's paradox (p. 617)
  • The perils of aggregation (p. 619)
  • Summary (p. 619)
  • 9.2 Inference for Two-Way Tables (p. 620)
  • The hypothesis: no association (p. 623)
  • Expected cell counts (p. 623)
  • The chi-square test (p. 624)
  • The chi-square test and the z test (p. 626)
  • Beyond the basics: Meta-analysis (p. 626)
  • Summary (p. 628)
  • 9.3 Formulas and Models for Two-Way Tables (p. 629)
  • Computations (p. 629)
  • Computing conditional distributions (p. 630)
  • Computing expected cell counts (p. 632)
  • Computing the chi-square statistic (p. 632)
  • Models for two-way tables (p. 634)
  • Concluding remarks (p. 636)
  • Summary (p. 637)
  • Chapter 9 Exercises (p. 637)
  • Chapter 10 Inference for Regression (p. 657)
  • Introduction (p. 658)
  • 10.1 Simple Linear Regression (p. 658)
  • Statistical model for linear regression (p. 658)
  • Data for simple linear regression (p. 660)
  • Estimating the regression parameters (p. 662)
  • Confidence intervals and significance tests (p. 668)
  • Confidence intervals for mean response (p. 671)
  • Prediction intervals (p. 673)
  • Beyond the basics: Nonlinear regression (p. 675)
  • Summary (p. 676)
  • 10.2 More Detail about Simple Linear Regression (p. 678)
  • Analysis of variance for regression (p. 678)
  • The ANOVA F test (p. 680)
  • Calculations for regression inference (p. 682)
  • Inference for correlation (p. 688)
  • Summary (p. 690)
  • Chapter 10 Exercises (p. 691)
  • Chapter 11 Multiple Regression (p. 709)
  • Introduction (p. 710)
  • 11.1 Inference for Multiple Regression (p. 710)
  • Population multiple regression equation (p. 710)
  • Data for multiple regression (p. 711)
  • Multiple linear regression model (p. 711)
  • Estimation of the multiple regression parameters (p. 712)
  • Confidence intervals and significance tests for regression coefficients (p. 713)
  • ANOVA table for multiple regression (p. 715)
  • Squared multiple correlation R[superscript 2] (p. 716)
  • 11.2 A Case Study (p. 717)
  • Preliminary analysis (p. 717)
  • Relationships between pairs of variables (p. 719)
  • Regression on high school grades (p. 720)
  • Interpretation of results (p. 722)
  • Residuals (p. 722)
  • Refining the model (p. 723)
  • Regression on SAT scores (p. 724)
  • Regression using all variables (p. 725)
  • Test for a collection of regression coefficients (p. 728)
  • Beyond the basics: Multiple logistic regression (p. 728)
  • Summary (p. 729)
  • Chapter 11 Exercises (p. 731)
  • Chapter 12 One-Way Analysis of Variance (p. 745)
  • Introduction (p. 746)
  • 12.1 Inference for One-Way Analysis of Variance (p. 746)
  • Data for a one-way ANOVA (p. 746)
  • Comparing means (p. 747)
  • The two-sample t statistic (p. 749)
  • ANOVA hypotheses (p. 750)
  • The ANOVA model (p. 752)
  • Estimates of population parameters (p. 754)
  • Testing hypotheses in one-way ANOVA (p. 756)
  • The ANOVA table (p. 760)
  • The F test (p. 762)
  • 12.2 Comparing the Means (p. 765)
  • Contrasts (p. 765)
  • Multiple comparisons (p. 771)
  • Software (p. 775)
  • Power (p. 778)
  • Summary (p. 780)
  • Chapter 12 Exercises (p. 781)
  • Chapter 13 Two-Way Analysis of Variance (p. 801)
  • Introduction (p. 802)
  • 13.1 The Two-Way ANOVA Model (p. 802)
  • Advantages of two-way ANOVA (p. 802)
  • The two-way ANOVA model (p. 805)
  • Main effects and interactions (p. 806)
  • 13.2 Inference for Two-Way ANOVA (p. 811)
  • The ANOVA table for two-way ANOVA (p. 812)
  • Summary (p. 816)
  • Chapter 13 Exercises (p. 817)
  • Data Appendix (p. 1)
  • Tables (p. 1)
  • Solutions to Selected Exercises (p. 1)
  • Notes (p. 1)
  • Index (p. 1)
  • Chapter 14 Nonparametric Tests
  • Introduction
  • 14.1 The Wilcoxon Rank Sum Test
  • The rank transformation
  • The Wilcoxon rank sum test
  • The normal approximation
  • What hypotheses does Wilcoxon test?
  • Ties
  • Limitations of nonparametric tests
  • Summary
  • Section 14.1 Exercises
  • 14.2 The Wilcoxon Signed Rank Test
  • The normal approximation
  • Ties
  • Summary
  • Section 14.2 Exercises
  • 14.3 The Kruskal-Wallis Test
  • Hypotheses and assumptions
  • The Kruskal-Wallis test
  • Summary
  • Section 14.3 Exercises
  • Chapter 14 Exercises
  • Notes
  • Chapter 15 Logistic Regression
  • Introduction
  • 15.1 The Logistic Regression Model
  • Binomial distributions and odds
  • Model for logistic regression
  • Fitting and interpreting the logistic regression model
  • 15.2 Inference for Logistic Regression
  • Confidence intervals and significance tests
  • Multiple logistic regression
  • Summary
  • Chapter 15 Exercises
  • Notes

Author notes provided by Syndetics

David S. Moore is a professor of psychology at Pitzer College and at Claremont Graduate University. He received his doctorate in developmental psychology from Harvard University and did his postdoctoral work at the City University of New York.

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