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Primer of Biostatistics

ISBN-10: 0070242682

ISBN-13: 9780070242685

Edition: 4th 1997

Authors: Stanton A. Glantz

List price: $34.95
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Description:

Extremely popular, this student-friendly text presents the practical areas of statistics in terms of their relevance to medicine and the life sciences. Includes many illustrative examples and challenging problems that reinforce the author' s unique and intuitive approach to the subject. The new edition features a new two-color design, examples taken from current biomedical literature, and review questions within each chapter.
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Book details

List price: $34.95
Edition: 4th
Copyright year: 1997
Publisher: McGraw-Hill Professional Publishing
Binding: Paperback
Pages: 473
Size: 5.39" wide x 7.91" long x 0.75" tall
Weight: 1.034
Language: English

Location of Tables for Tests of Significance
Preface
Biostatistics and Clinical Practicep. 1
The Changing Medical Environmentp. 1
What Do Statistical Procedures Tell You?p. 4
Why Not Depend on the Journals?p. 6
Why Has the Problem Persisted?p. 9
How to Summarize Datap. 10
The Meanp. 12
Measures of Variabilityp. 13
The Normal Distributionp. 14
Percentilesp. 15
How to Make Estimates from a Limited Samplep. 20
How Good Are These Estimates?p. 21
How to Test for Difference between Groupsp. 31
The General Approachp. 31
Two Different Estimates of the Population Variancep. 36
What Is a "Big" F?p. 38
Three Examplesp. 46
The Special Case of Two Groups: The t Testp. 65
The General Approachp. 67
The Standard Deviation of a Difference or a Sump. 69
Use of t to Test Hypotheses about Two Groupsp. 72
What If the Two Samples Are Not the Same Size?p. 79
The Examples Revisitedp. 80
The t Test Is an Analysis of Variancep. 84
Common Errors in the Use of the t Test and How to Compensate for Themp. 86
How to Use t Tests to Isolate Differences between Groups in Analysis of Variancep. 89
Other Approaches to Multiple Comparison Testing: The Student-Newman-Keuls Testp. 95
Which Multiple Comparison Procedure Should You Use?p. 101
Multiple Comparisons against a Single Controlp. 101
The Meaning of Pp. 107
How to Analyze Rates and Proportionsp. 113
Back to Marsp. 114
Estimating Proportions from Samplesp. 119
Hypothesis Tests for Proportionsp. 123
Another Approach to Testing Nominal Data: Analysis of Contingency Tablesp. 132
Chi-Square Applications to Experiments with More Than Two Treatments or Outcomesp. 139
The Fisher Exact Testp. 144
Measures of Association Between Two Nominal Variablesp. 149
What Does "Not Significant" Really Mean?p. 164
An Effective Diureticp. 165
Two Types of Errorsp. 169
What Determines a Test's Power?p. 171
Power and Sample Size for Analysis of Variancep. 184
Power and Sample Size for Comparing Two Proportionsp. 188
Power and Sample Size for Relative Risk and Odds Ratiop. 192
Power and Sample Size for Contingency Tablesp. 193
Practical Problems in Using Powerp. 195
What Difference Does It Make?p. 195
Confidence Intervalsp. 199
The Size of the Treatment Effect Measured as the Difference of Two Meansp. 200
The Effective Diureticp. 203
What Does "Confidence" Mean?p. 207
Confidence Intervals Can Be Used to Test Hypothesesp. 209
Confidence Interval for the Population Meanp. 211
The Size of the Treatment Effect Measured as the Difference of Two Rates or Proportionsp. 212
Confidence Interval for Rates and Proportionsp. 217
Confidence Intervals for Relative Risk and Odds Ratiop. 222
Confidence Interval for the Entire Populationp. 224
How to Test for Trendsp. 230
More about the Martiansp. 231
How to Estimate the Trend from a Samplep. 238
How to Compare Two Regression Linesp. 254
Correlation and Correlation Coefficientsp. 262
The Spearman Rank Correlation Coefficientp. 273
Power and Sample Size in Regression and Correlationp. 280
Comparing Two Different Measurements of the Same Thing: The Bland-Altman Methodp. 282
Experiments When Each Subject Receives More than One Treatmentp. 298
Experiments When Subjects Are Observed before and after a Single Treatment: The Paired t Testp. 299
Another Approach to Analysis of Variancep. 307
Experiments When Subjects Are Observed after Many Treatments: Repeated-Measures Analysis of Variancep. 318
Experiments When Outcomes Are Measured on a Nominal Scale: McNemar's Testp. 330
Alternatives to Analysis of Variance and the t Test Based on Ranksp. 339
How to Choose between Parametric and Nonparametric Methodsp. 340
Two Different Samples: The Mann-Whitney Rank-Sum Testp. 343
Each Subject Observed before and after One Treatment: The Wilcoxon Signed-Rank Testp. 354
Experiments with Three or More Groups When Each Group Contains Different Individuals: The Kruskal-Wallis Statisticp. 362
Experiments in Which Each Subject Receives More than One Treatment: The Friedman Testp. 370
How to Analyze Survival Datap. 387
Censoring on Plutop. 388
Estimating the Survival Curvep. 391
Comparing Two Survival Curvesp. 400
Gehan's Testp. 409
Power and Sample Sizep. 411
What Do the Data Really Show?p. 416
When to Use Which Testp. 417
Randomize and Controlp. 419
Does Randomization Ensure Correct Conclusions?p. 427
Problems with the Populationp. 432
How You Can Improve Thingsp. 434
App. A: Computational Formsp. 438
App. B: Power Chartsp. 444
App. C: Answers to Exercisesp. 453
Indexp. 469
Table of Contents provided by Blackwell. All Rights Reserved.