Descripciones mejoradas de Syndetics:

Accessible to medicine- and/or public policy-related audiences, as well as most statisticians.
* Emphasis on outliers is discussed by way of detection and treatment.
* Resampling statistics software is incorporated throughout.
* Motivating applications are presented in light of honest theory.
* Plentiful exercises are sprinkled throughout.

Tabla de contenidos provista por Syndetics

• Preface(p. xv)
• 1. What is Statistics? How is it Applied in the Health Sciences?(p. 1)
• 1.1 Definitions of Statistics and Statisticians(p. 2)
• 1.2 Why Study Statistics?(p. 3)
• 1.3 Types of Studies(p. 8)
• 1.3.1 Surveys and Cross-Sectional Studies(p. 9)
• 1.3.2 Retrospective Studies(p. 10)
• 1.3.3 Prospective Studies(p. 10)
• 1.3.4 Experimental Studies and Quality Control(p. 10)
• 1.3.5 Clinical Trials(p. 12)
• 1.3.6 Epidemiological Studies(p. 14)
• 1.3.7 Pharmacoeconomic Studies and Quality of Life(p. 16)
• 1.4 Exercises(p. 18)
• 1.5 Additional Reading(p. 19)
• 2. Defining Populations and Selecting Samples(p. 22)
• 2.1 What are Populations and Samples?(p. 22)
• 2.2 Why Select a Sample?(p. 23)
• 2.3 How Samples Can be Selected(p. 25)
• 2.3.1 Simple Random Sampling(p. 25)
• 2.3.2 Convenience Sampling(p. 25)
• 2.3.3 Systematic Sampling(p. 26)
• 2.3.4 Stratified Random Sampling(p. 28)
• 2.3.5 Cluster Sampling(p. 28)
• 2.3.6 Bootstrap Sampling(p. 29)
• 2.4 How to Select a Simple Random Sample(p. 29)
• 2.5 How to Select a Bootstrap Sample(p. 39)
• 2.6 Why Does Random Sampling Work?(p. 41)
• 2.7 Exercises(p. 41)
• 2.8 Additional Reading(p. 45)
• 3. Systematic Organization and Display of Data(p. 46)
• 3.1 Types of Data(p. 46)
• 3.1.1 Qualitative(p. 47)
• 3.1.2 Quantitative(p. 47)
• 3.2 Frequency Tables and Histograms(p. 48)
• 3.3 Graphical Methods(p. 51)
• 3.3.1 Frequency Histograms(p. 51)
• 3.3.2 Frequency Polygons(p. 53)
• 3.3.3 Cumulative Frequency Polygon(p. 54)
• 3.3.4 Stem-and-Leaf Diagrams(p. 56)
• 3.3.5 Box-and-Whisker Plots(p. 58)
• 3.3.6 Bar Charts and Pie Charts(p. 61)
• 3.1 Exercises(p. 63)
• 3.1 Additional Reading(p. 67)
• 4. Summary Statistics(p. 68)
• 4.1 Measures of Central Tendency(p. 61)
• 4.1.1 The Arithmetic Mean(p. 68)
• 4.1.2 The Median(p. 70)
• 4.1.3 The Mode(p. 73)
• 4.1.4 The Geometric Mean(p. 73)
• 4.1.5 The Harmonic Mean(p. 74)
• 4.1.6 Which Measure Should You Use?(p. 75)
• 4.2 Measures of Dispersion(p. 76)
• 4.2.1 Range(p. 78)
• 4.2.2 Mean Absolute Deviation(p. 78)
• 4.2.3 Population Variance and Standard Deviation(p. 79)
• 4.2.4 Sample Variance and Standard Deviation(p. 82)
• 4.2.5 Calculating the Variance and Standard Deviation from Group Data(p. 84)
• 4.3 Coefficient of Variation (CV) and Coefficient of Dispersion (CD)(p. 85)
• 4.4 Exercises(p. 88)
• 4.5 Additional Reading(p. 91)
• 5. Basic Probability(p. 92)
• 5.1 What is Probability?(p. 92)
• 5.2 Elementary Sets as Events and Their Complements(p. 95)
• 5.3 Independent and Disjoint Events(p. 95)
• 5.4 Probability Rules(p. 98)
• 5.5 Permutations and Combinations(p. 100)
• 5.6 Probability Distributions(p. 103)
• 5.7 The Binomial Distribution(p. 109)
• 5.8 The Monty Hall Problem(p. 110)
• 5.9 A Quality Assurance Problem(p. 113)
• 5.10 Exercises(p. 115)
• 5.11 Additional Reading(p. 120)
• 6. The Normal Distribution(p. 121)
• 6.1 The Importance of the Normal Distribution in Statistics(p. 121)
• 6.2 Properties of Normal Distributions(p. 122)
• 6.3 Tabulating Areas under the Standard Normal Distribution(p. 124)
• 6.4 Exercises(p. 129)
• 6.5 Additional Reading(p. 132)
• 7. Sampling Distributions for Means(p. 133)
• 7.1 Population Distributions and the Distribution of Sample Averages from the Population(p. 133)
• 7.2 The Central Limit Theorem(p. 141)
• 7.3 Standard Error of the Mean(p. 143)
• 7.4 Z Distribution Obtained When Standard Deviation Is Known(p. 144)
• 7.5 Student's t Distribution Obtained When Standard Deviation Is Unknown(p. 144)
• 7.6 Assumptions Required for t Distribution(p. 147)
• 7.7 Exercises(p. 147)
• 7.8 Additional Reading(p. 149)
• 8. Estimating Population Means(p. 150)
• 8.1 Estimation Versus Hypothesis Testing(p. 150)
• 8.2 Point Estimates(p. 151)
• 8.3 Confidence Intervals(p. 153)
• 8.4 Confidence Intervals for a Single Population Mean(p. 154)
• 8.5 Z and t Statistics for Two Independent Samples(p. 159)
• 8.6 Confidence Intervals for the Difference between Means from Two Independent Samples (Variance Known)(p. 161)
• 8.7 Confidence Intervals for the Difference between Means from Two Independent Samples (Variance Unknown)(p. 161)
• 8.8 Bootstrap Principle(p. 166)
• 8.9 Bootstrap Percentile Method Confidence Intervals(p. 167)
• 8.10 Sample Size Determination for Confidence Intervals(p. 176)
• 8.11 Exercises(p. 179)
• 8.12 Additional Reading(p. 181)
• 9. Tests of Hypotheses(p. 182)
• 9.1 Terminology(p. 182)
• 9.2 Neyman-Pearson Test Formulation(p. 183)
• 9.3 Test of a Mean (Single Sample, Population Variance Known)(p. 186)
• 9.4 Test of a Mean (Single sample, Population Variance Unknown)(p. 187)
• 9.5 One-Tailed Versus Two-Tailed Tests(p. 188)
• 9.6 p-Values(p. 191)
• 9.7 Type I and Type II Errors(p. 191)
• 9.8 The Power Function(p. 192)
• 9.9 Two-Sample t Test (Independent Samples with a Common Variance)(p. 193)
• 9.10 Paired t Test(p. 195)
• 9.11 Relationship between Confidence Intervals and Hypothesis Tests(p. 199)
• 9.12 Bootstrap Percentile Method Test(p. 200)
• 9.13 Sample Size Determination for Hypothesis Tests(p. 201)
• 9.14 Sensitivity and Specificity in Medical Diagnosis(p. 202)
• 9.15 Meta-Analysis(p. 204)
• 9.16 Bayesian Methods(p. 207)
• 9.17 Group Sequential Methods(p. 209)
• 9.18 Missing Data and Imputation(p. 210)
• 9.19 Exercises(p. 212)
• 9.20 Additional Reading(p. 215)
• 10. Inferences Regarding Proportions(p. 217)
• 10.1 Why Are Proportions Important?(p. 217)
• 10.2 Mean and Standard Deviation for the Binomial Distribution(p. 218)
• 10.3 Normal Approximation to the Binomial(p. 221)
• 10.4 Hypothesis Test for a Single Binomial Proportion(p. 222)
• 10.5 Testing the Difference between Two Proportions(p. 224)
• 10.6 Confidence Intervals for Proportions(p. 225)
• 10.7 Sample Size Determination--Confidence Intervals and Hypothesis Tests(p. 227)
• 10.8 Exercises(p. 228)
• 10.9 Additional Reading(p. 229)
• 11. Categorical Data and Chi-Square Tests(p. 231)
• 11.1 Understanding Chi-Square(p. 232)
• 11.2 Chi-Square Distributions and Tables(p. 233)
• 11.3 Testing Independence between Two Variables(p. 233)
• 11.4 Testing for Homogeneity(p. 236)
• 11.5 Testing for Differences between two Proportions(p. 237)
• 11.6 The Special Case of 2 x 2 Contingency Table(p. 238)
• 11.7 Simpson's Paradox in the 2 x 2 Table(p. 239)
• 11.8 McNemar's Test for Correlated Proportions(p. 241)
• 11.9 Relative Risk and Odds Ratios(p. 242)
• 11.10 Goodness of Fit Tests--Fitting Hypothesized Probability Distributions(p. 244)
• 11.11 Limitations to Chi-Square and Exact Alternatives(p. 246)
• 11.12 Exercises(p. 247)
• 11.13 Additional Reading(p. 250)
• 12. Correlation, Linear Regression, and Logistic Regression(p. 251)
• 12.1 Relationships between Two Variables(p. 252)
• 12.2 Uses of Correlation and Regression(p. 252)
• 12.3 The Scatter Diagram(p. 254)
• 12.4 Pearson's Product Moment Correlation Coefficient and Its Sample Estimate(p. 256)
• 12.5 Testing Hypotheses about the Correlation Coefficient(p. 258)
• 12.6 The Correlation Matrix(p. 259)
• 12.7 Regression Analysis and Least Squares Inference Regarding the Slope and Intercept of a Regression Line(p. 259)
• 12.8 Sensitivity to Outliers, Outlier Rejection, and Robust Regression(p. 264)
• 12.9 Galton and Regression toward the Mean(p. 271)
• 12.10 Multiple Regression(p. 277)
• 12.11 Logistic Regression(p. 283)
• 12.12 Exercises(p. 287)
• 12.13 Additional Reading(p. 293)
• 13. One-Way Analysis of Variance(p. 295)
• 13.1 Purpose of One-Way Analysis of Variance(p. 296)
• 13.2 Decomposing the Variance and Its Meaning(p. 297)
• 13.3 Necessary Assumptions(p. 298)
• 13.4 F Distribution and Applications(p. 298)
• 13.5 Multiple Comparisons(p. 301)
• 13.5.1 General Discussion(p. 301)
• 13.5.2 Tukey's Honest Significant Difference (HSD) Test(p. 301)
• 13.6 Exercises(p. 302)
• 13.7 Additional Reading(p. 307)
• 14. Nonparametric Methods(p. 308)
• 14.1 Advantages and Disadvantages of Nonparametric Versus Parametric Methods(p. 308)
• 14.2 Procedures for Ranking Data(p. 309)
• 14.3 Wilcoxon Rank-Sum Test(p. 311)
• 14.4 Wilcoxon Signed-Rank Test(p. 314)
• 14.5 Sign Test(p. 317)
• 14.6 Kruskal-Wallis Test: One-Way ANOVA by Ranks(p. 319)
• 14.7 Spearman's Rank-Order Correlation Coefficient(p. 322)
• 14.8 Permutation Tests(p. 324)
• 14.8.1 Introducing Permutation Methods(p. 324)
• 14.8.2 Fisher's Exact Test(p. 327)
• 14.9 Insensitivity of Rank Tests to Outliers(p. 330)
• 14.10 Exercises(p. 331)
• 14.11 Additional Reading(p. 334)
• 15. Analysis of Survival Times(p. 336)
• 15.1 Introduction to Survival Times(p. 336)
• 15.2 Survival Probabilities(p. 338)
• 15.2.1 Introduction(p. 338)
• 15.2.2 Life Tables(p. 339)
• 15.2.3 The Kaplan-Meier Curve(p. 341)
• 15.2.4 Parametric Survival Curves(p. 344)
• 15.2.5 Cure Rate Models(p. 348)
• 15.3 Comparing Two or More Survival Curves--The Log Rank Test(p. 349)
• 15.4 Exercises(p. 352)
• 15.5 Additional Reading(p. 354)
• 16. Software Packages for Statistical Analysis(p. 356)
• 16.1 General-Purpose Packages(p. 356)
• 16.2 Exact Methods(p. 359)
• 16.3 Sample Size Determination(p. 359)
• 16.4 Why You Should Avoid Excel(p. 360)
• 16.5 References(p. 361)
• Postscript(p. 362)
• Appendices(p. 363)
• A Percentage Points, F-Distribution ([alpha] = 0.05)(p. 363)
• B Studentized Range Statistics(p. 364)
• C Quantiles of the Wilcoxon Signed-Rank Test Statistic(p. 366)
• D x[superscript 2] Distribution(p. 368)
• E Table of the Standard Normal Distribution(p. 370)
• F Percentage Points, Student's t Distribution(p. 371)
• G Answers to Selected Exercises(p. 373)
• Index(p. 401)

Notas de autor provistas por Syndetics

Michael R. Chernick, PhD, is the Assistant Director of Biostatistics at Novo Nordisk Pharmaceuticals, Inc., in Princeton, New Jersey
Robert Friis, PhD, is Professor and Chair of the Department of Health Science at California State University, Long Beach