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Biostatistical Analysis

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Edition:
5th
ISBN13:

9780130815422

ISBN10:
013081542X
Format:
Hardcover
Pub. Date:
1/1/2010
Publisher(s):
Prentice Hall
List Price: $138.66
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  • Biostatistical Analysis
    Biostatistical Analysis




Summary

The latest edition of this best-selling biostatistics book is both comprehensive and easy to read.It provides a broad and practical overview of the statistical analysis methods used by researchers to collect, summarize, analyze, and draw conclusions from biological research data. TheFourth Editioncan serve as either an introduction to the discipline for beginning studentsora comprehensive procedural reference for today's practitioners.

Table of Contents

PREFACE x
1 INTRODUCTION
1(15)
1.1 Types of Biological Data
2(3)
1.2 Accuracy and Significant Figures
5(1)
1.3 Frequency Distributions
6(7)
1.4 Cumulative Frequency Distributions
13(3)
2 POPULATIONS AND SAMPLES
16(4)
2.1 Populations
16(1)
2.2 Samples from Populations
17(1)
2.3 Random Sampling
17(1)
2.4 Parameters and Statistics
18(2)
3 MEASURES OF CENTRAL TENDENCY
20(12)
3.1 The Arithmetic Mean
20(3)
3.2 The Median
23(3)
3.3 Other Quantiles
26(1)
3.4 The Mode
27(1)
3.5 Other Measures of Central Tendency
28(1)
3.6 The Effect of Coding Data
29(2)
Exercises
31(1)
4 MEASURES OF DISPERSION AND VARIABILITY
32(16)
4.1 The Range
32(2)
4.2 Dispersion Measured with Quantiles
34(1)
4.3 The Mean Deviation
34(1)
4.4 The Variance
35(4)
4.5 The Standard Deviation
39(1)
4.6 The Coefficient of Variation
40(1)
4.7 Indices of Diversity
40(4)
4.8 The Effect of Coding Data
44(3)
Exercises
47(1)
5 PROBABILITIES
48(17)
5.1 Counting Possible Outcomes
49(1)
5.2 Permutations
50(4)
5.3 Combinations
54(2)
5.4 Sets
56(2)
5.5 Probability of an Event
58(1)
5.6 Adding Probabilities
59(2)
5.7 Multiplying Probabilities
61(2)
Exercises
63(2)
6 THE NORMAL DISTRIBUTION
65(26)
6.1 Symmetry and Kurtosis
67(5)
6.2 Proportions of a Normal Distribution
72(4)
6.3 The Distribution of Means
76(3)
6.4 Introduction to Statistical Hypothesis Testing
79(7)
6.5 Assessing Departures from Normality
86(3)
Exercises
89(2)
7 ONE-SAMPLE HYPOTHESES
91(31)
7.1 Two-Tailed Hypotheses Concerning the Mean
91(5)
7.2 One-Tailed Hypotheses Concerning the Mean
96(2)
7.3 Confidence Limits for the Population Mean
98(2)
7.4 Reporting Variability about the Mean
100(5)
7.5 Sample Size and Estimation of the Population Mean
105(1)
7.6 Power and Sample Size in Tests Concerning the Mean
105(3)
7.7 Sampling Finite Populations
108(2)
7.8 Confidence Limits for the Population Median
110(1)
7.9 Hypotheses Concerning the Median
110(1)
7.10 Confidence Limits for the Population Variance
110(2)
7.11 Hypotheses Concerning the Variance
112(1)
7.12 Power and Sample Size in Tests Concerning the Variance
113(1)
7.13 Hypotheses Concerning the Coefficient of Variation
114(1)
7.14 Hypotheses Concerning Symmetry and Kurtosis
115(5)
Exercises
120(2)
8 TWO-SAMPLE HYPOTHESES
122(39)
8.1 Testing for Difference between Two Means
122(7)
8.2 Confidence Limits for Population Means
129(2)
8.3 Sample Size and Estimation of the Difference between Two Population Means
131(1)
8.4 Power and Sample Size in Tests for Difference between Two Means
132(4)
8.5 Testing for Difference between Two Variances
136(3)
8.6 Confidence Interval for the Population Variance Ratio
139(1)
8.7 Sample Size and Power in Tests for Difference between Two Variances
140(1)
8.8 Testing for Difference between Two Coefficients of Variation
141(4)
8.9 Nonparametric Statistical Methods
145(1)
8.10 Two-Sample Rank Testing
146(9)
8.11 Testing for Difference between Two Medians
155(1)
8.12 The Effect of Coding
155(1)
8.13 Two-Sample Testing of Nominal-Scale Data
156(1)
8.14 Testing for Difference between Two Diversity Indices
156(3)
Exercises
159(2)
9 PAIRED-SAMPLE HYPOTHESES
161(16)
9.1 Testing Mean Difference
161(3)
9.2 Confidence Limits for the Population Mean Difference
164(1)
9.3 Power and Sample Size in Paired-Sample Testing of Means
164(1)
9.4 Testing for the Difference between Variances from Two Correlated Populations
164(1)
9.5 Paired-Sample Testing by Ranks
165(4)
9.6 Confidence Limits for the Population Median Difference
169(1)
9.7 Paired-Sample Testing of Nominal-Scale Data
169(6)
Exercises
175(2)
10 MULTISAMPLE HYPOTHESES: THE ANALYSIS OF VARIANCE
177(31)
10.1 Single-Factor Analysis of Variance
178(11)
10.2 Confidence Limits for Population Means
189(1)
10.3 Power and Sample Size in Analysis of Variance
189(6)
10.4 Nonparametric Analysis of Variance
195(5)
10.5 Testing for Difference among Several Medians
200(2)
10.6 Homogeneity of Variances
202(2)
10.7 Homogeneity of Coefficients of Variation
204(2)
10.8 The Effect of Coding
206(1)
10.9 Multisample Testing for Nominal-Scale Data
206(1)
Exercises
206(2)
11 MULTIPLE COMPARISONS
208(23)
11.1 The Tukey Test
210(4)
11.2 The Newman-Keuls Test
214(1)
11.3 Confidence Intervals Following Multiple Comparisons
215(2)
11.4 Comparisons of a Control Mean to Each Other Group Mean
217(2)
11.5 Scheffe's Multiple Contrasts
219(4)
11.6 Nonparametric Multiple Comparisons
223(3)
11.7 Nonparametric Multiple Contrasts
226(1)
11.8 Multiple Comparisons among Medians
226(2)
11.9 Multiple Comparisons among Variances
228(2)
Exercises
230(1)
12 TWO-FACTOR ANALYSIS OF VARIANCE
231(42)
12.1 Two-Factor Analysis of Variance with Equal Replication
232(13)
12.2 Two-Factor Analysis of Variance with Unequal Replication
245(3)
12.3 Two-Factor Analysis of Variance without Replication
248(2)
12.4 The Randomized Block Experimental Design
250(5)
12.5 Repeated-Measures Experimental Designs
255(5)
12.6 Multiple Comparisons and Confidence Intervals in Two-Factor Analysis of Variance
260(1)
12.7 Power and Sample Size in Two-Factor Analysis of Variance
261(2)
12.8 Nonparametric Randomized Block or Repeated-Measures Analysis of Variance
263(4)
12.9 Multiple Comparisons for Nonparametric Randomized Block or Repeated-Measures Analysis of Variance
267(1)
12.10 Dichotomous Nominal-Scale Data in Randomized Blocks or from Repeated Measures
268(2)
12.11 Multiple Comparisons with Dichotomous Randomized Block or Repeated-Measures Data
270(1)
12.12 Introduction to Analysis of Covariance
270(1)
Exercises
271(2)
13 DATA TRANSFORMATIONS
273(9)
13.1 The Logarithmic Transformation
275(1)
13.2 The Square Root Transformation
275(3)
13.3 The Arcsine Transformation
278(2)
13.4 Other Transformations
280(1)
Exercises
280(2)
14 MULTIWAY FACTORIAL ANALYSIS OF VARIANCE
282(21)
14.1 Three-Factor Analysis of Variance
283(3)
14.2 The Latin Square Experimental Design
286(1)
14.3 Higher-Order Factorial Analysis of Variance
287(1)
14.4 Blocked and Repeated-Measures Experimental Designs
288(10)
14.5 Factorial Analysis of Variance with Unequal Replication
298(1)
14.6 Multiple Comparisons and Confidence Intervals in Multiway Analysis of Variance
299(1)
14.7 Power and Sample Size in Multiway Analysis of Variance
300(1)
Exercises
300(3)
15 NESTED (HIERARCHICAL) ANALYSIS OF VARIANCE
303(9)
15.1 Nesting within One Main Factor
305(3)
15.2 Nesting in Factorial Experiments
308(2)
15.3 Multiple Comparisons and Confidence Intervals
310(1)
15.4 Power and Sample Size in Nested Analysis of Variance
311(1)
Exercises
311(1)
16 MULTIVARIATE ANALYSIS OF VARIANCE
312(12)
16.1 The Multivariate Normal Distribution
313(3)
16.2 Multivariate Analysis of Variance Hypothesis Testing
316(6)
16.3 Further Analysis
322(1)
16.4 Other Experimental Designs
322(1)
Exercises
323(1)
17 SIMPLE LINEAR REGRESSION
324(36)
17.1 Regression vs. Correlation
324(2)
17.2 The Simple Linear Regression Equation
326(7)
17.3 Testing the Significance of a Regression
333(4)
17.4 Confidence Intervals in Regression
337(5)
17.5 Inverse Prediction
342(2)
17.6 Interpretations of Regression Functions
344(1)
17.7 Regression with Replication and Testing for Linearity
345(5)
17.8 Power and Sample Size in Regression
350(1)
17.9 Regression through the Origin
351(2)
17.10 Data Transformations in Regression
353(4)
17.11 The Effect of Coding
357(1)
Exercises
358(2)
18 COMPARING SIMPLE LINEAR REGRESSION EQUATIONS
360(17)
18.1 Comparing Two Slopes
360(4)
18.2 Comparing Two Elevations
364(4)
18.3 Comparing Points on Two Regression Lines
368(1)
18.4 Comparing more than Two Slopes
369(3)
18.5 Comparing more than Two Elevations
372(1)
18.6 Multiple Comparisons among Slopes
372(1)
18.7 Multiple Comparisons among Elevations
373(1)
18.8 Multiple Comparisons of Points among Regression Lines
374(1)
18.9 An Overall Test for Coincidental Regressions
375(1)
Exercises
375(2)
19 SIMPLE LINEAR CORRELATION
377(36)
19.1 The Correlation Coefficient
377(4)
19.2 Hypotheses about the Correlation Coefficient
381(2)
19.3 Confidence Intervals for the Population Correlation Coefficient
383(2)
19.4 Power and Sample Size in Correlation
385(1)
19.5 Comparing Two Correlation Coefficients
386(2)
19.6 Power and Sample Size in Comparing Two Correlation Coefficients
388(2)
19.7 Comparing more than Two Correlation Coefficients
390(2)
19.8 Multiple Comparisons among Correlation Coefficients
392(3)
19.9 Rank Correlation
395(3)
19.10 Weighted Rank Correlation
398(3)
19.11 Correlation for Dichotomous Nominal-Scale Data
401(3)
19.12 Intraclass Correlation
404(3)
19.13 Concordance Correlation
407(3)
19.14 The Effect of Coding
410(1)
Exercises
410(3)
20 MULTIPLE REGRESSION AND CORRELATION
413(39)
20.1 Intermediate Computational Steps
414(5)
20.2 The Multiple Regression Equation
419(3)
20.3 Analysis of Variance of Multiple Regression or Correlation
422(2)
20.4 Hypotheses Concerning Partial Regression Coefficients
424(2)
20.5 Standardized Partial Regression Coefficients
426(1)
20.6 Partial Correlation
426(2)
20.7 Round-off Error and Coding Data
428(1)
20.8 Selection of Independent Variables
429(4)
20.9 Predicting Y Values
433(3)
20.10 Testing Difference between Two Partial Regression Coefficients
436(1)
20.11 "Dummy" Variables
436(1)
20.12 Interaction of Independent Variables
437(1)
20.13 Comparing Multiple Regression Equations
437(3)
20.14 Multiple Regression through the Origin
440(1)
20.15 Nonlinear Regression
440(2)
20.16 Descriptive vs. Predictive Models
442(1)
20.17 Concordance: Rank Correlation among Several Variables
443(7)
Exercises
450(2)
21 POLYNOMIAL REGRESSION
452(9)
21.1 Polynomial Curve Fitting
452(5)
21.2 Round-off Error and Coding Data
457(1)
21.3 Quadratic Regression
457(2)
Exercises
459(2)
22 TESTING FOR GOODNESS OF FIT
461(25)
22.1 Chi-Square Goodness of Fit
462(2)
22.2 Chi-Square Goodness of Fit for More than Two Categories
464(2)
22.3 Subdividing Chi-Square Analyses
466(2)
22.4 Chi-Square Correction for Continuity
468(2)
22.5 Bias in Chi-Square Calculations
470(1)
22.6 Heterogeneity Chi-Square
471(2)
22.7 The Log-Likehood Ratio
473(2)
22.8 Kolmogorov-Smirnov Goodness of Fit for Discrete Data
475(3)
22.9 Kolmogorov-Smirnov Goodness of Fit for Continuous Data
478(3)
22.10 Sample Size Required for Kolmogorov-Smirnov Goodness of Fit for Continuous Data
481(2)
Exercises
483(3)
23 CONTINGENCY TABLES
486(30)
23.1 Chi-Square Analysis of Contingency Tables
488(2)
23.2 Graphing Contingency Table Data
490(1)
23.3 The 2 x 2 Contingency Table
491(9)
23.4 Heterogeneity Testing of 2 x 2 Tables
500(2)
23.5 Subdividing Contingency Tables
502(2)
23.6 Bias in Chi-Square Contingency Table Analyses
504(1)
23.7 The Log-Likehood Ratio for Contingency Tables
505(1)
23.8 Three-Dimensional Contingency Tables
506(6)
23.9 Log-Linear Models for Multidimensional Contingency Tables
512(2)
Exercises
514(2)
24 MORE ON DICHOTOMOUS VARIABLES
516(55)
24.1 Binomial Probabilities
517(6)
24.2 The Hypergeometric Distribution
523(1)
24.3 Sampling a Binomial Population
524(3)
24.4 Confidence Limits for Population Proportions
527(3)
24.5 Goodness of Fit for the Binomial Distribution
530(3)
24.6 The Binomial Test
533(5)
24.7 The Sign Test
538(1)
24.8 Power of the Binomial and Sign Tests
539(3)
24.9 Confidence Interval for the Population Median
542(1)
24.10 The Fisher Exact Test
543(2)
24.11 Comparing Two Proportions
555(3)
24.12 Power and Sample Size in Comparing Two Proportions
558(4)
24.13 Comparing more than Two Proportions
562(1)
24.14 Multiple Comparisons for Proportions
563(2)
24.15 Trends among Proportions
565(3)
Exercises
568(3)
25 TESTING FOR RANDOMNESS
571(21)
25.1 Poisson Probabilities
571(3)
25.2 Confidence Limits for the Poisson Parameter
574(1)
25.3 Goodness of Fit of the Poisson Distribution
575(3)
25.4 The Binomial Test Revisited
578(4)
25.5 Comparing Two Poisson Counts
582(1)
25.6 Serial Randomness of Nominal-Scale Categories
583(3)
25.7 Serial Randomness of Measurements: Parametric Testing
586(1)
25.8 Serial Randomness of Measurements: Nonparametric Testing
587(3)
Exercises
590(2)
26 CIRCULAR DISTRIBUTIONS: DESCRIPTIVE STATISTICS
592(24)
26.1 Data on a Circular Scale
592(3)
26.2 Graphical Presentation of Circular Data
595(2)
26.3 Sines and Cosines of Circular Data
597(2)
26.4 The Mean Angle
599(3)
26.5 Angular Dispersion
602(3)
26.6 The Median and Modal Angles
605(1)
26.7 Confidence Limits for the Population Mean and Median Angles
605(2)
26.8 Diametrically Bimodal Distributions
607(1)
26.9 Second-Order Analysis: The Mean of Mean Angles
608(3)
26.10 Confidence Limits for the Second-Order Mean Angle
611(3)
Exercises
614(2)
27 CIRCULAR DISTRIBUTIONS: HYPOTHESIS TESTING
616
27.1 Testing Significance of the Mean Angle Unimodal Distributions
616(5)
27.2 Testing Significance of the Median Angle: Omnibus Test
621(3)
27.3 Testing Significance of the Median Angle: Binomial Test
624(1)
27.4 Testing Symmetry around the Median Angle
624(1)
27.5 Two-Sample and Multisample Testing of Mean Angles
625(5)
27.6 Nonparametric Two-Sample and Multisample Testing of Angles
630(5)
27.7 Two-Sample and Multisample Testing of Median Angles
635(1)
27.8 Two-Sample and Multisample Testing of Angular Distances
635(2)
27.9 Two-Sample and Multisample Testing of Angular Dispersion
637(1)
27.10 Parametric One-Sample Second-Order Analysis of Angles
638(1)
27.11 Nonparametric One-Sample Second-Order Analysis of Angles
639(2)
27.12 Parametric Two-Sample Second-Order Analysis of Angles
641(2)
27.13 Nonparametric Two-Sample Second-Order Analysis of Angles
643(2)
27.14 Parametric Paired-Sample Testing with Angles
645(2)
27.15 Nonparametric Paired-Sample Testing with Angles
647(2)
27.16 Parametric Angular Correlation and Regression
649(4)
27.17 Nonparametric Angular Correlation
653(1)
27.18 Goodness of Fit Testing for Circular Distributions
654(4)
27.19 Serial Randomness of Nominal-Scale Categories on a Circle
658(2)
Exercises
660
APPENDIX A ANALYSIS OF VARIANCE HYPOTHESIS TESTING
App1(10)
A.1 Determination of Appropriate F's and Degrees of Freedom
App1(4)
A.2 Two-Factor Analysis of Variance
App5(1)
A.3 Three-Factor Analysis of Variance
App6(1)
A.4 Nested Analysis of Variance
App7(1)
A.5 Split-Plot and Mixed Within-Subjects Analysis of Variance
App8(3)
APPENDIX B STATISTICAL TABLES AND GRAPHS
App11
Table B.1 Critical Values of Chi-Square Distribution
App12(5)
Table B.2 Proportions of the Normal Curve (One-Tailed)
App17(2)
Table B.3 Critical Values of the t Distribution
App19(2)
Table B.4 Critical Values of the F Distribution
App21(37)
Table B.5 Critical Values of the q Distribution
App58(16)
Table B.6 Critical Values of q' for the One-Tailed Dunnett's Test
App74(2)
Table B.7 Critical Values of q' for the Two-Tailed Dunnett's Test
App76(1)
Table B.8 Critical Values of d(max) for the Kolmogorov-Smirnov Goodness of Fit Test for Discrete or Grouped Data
App77(6)
Table B.9 Critical Values of D for the Kolmogorov-Smirnov Goodness of Fit Test for Continuous Distributions
App83(4)
Table B.10 Critical Values of D(Delta) for the Delta-Corrected Kolmogorov-Smirnov Goodness of Fit Test for Continuous Distribution
App87(2)
Table B.11 Critical Values of the Mann-Whitney U Distribution
App89(12)
Table B.12 Critical Values of the Wilcoxon T Distribution
App101(3)
Table B.13 Critical Values of the Kruskal-Wallis H Distribution
App104(2)
Table B.14 Critical Values of the Friedman X(2)(r) Distribution
App106(1)
Table B.15 Critical Values of Q for Nonparametric Multiple Comparison Testing
App107(1)
Table B.16 Critical Values of Q' for Nonparametric Multiple Comparison Testing with a Control
App108(1)
Table B.17 Critical Values of the Correlation Coefficient, r
App109(2)
Table B.18 Fisher's z Transformation for Correlation Coefficients, r
App111(2)
Table B.19 Correlation Coefficients, r, Corresponding to Fisher's z Transformation
App113(3)
Table B.20 Critical Values of the Spearman Rank Correlation Coefficient, r(s)
App116(2)
Table B.21 Critical Values of the Top-Down Correlation Coefficient, r(T)
App118(1)
Table B.22 Critical Values of the Symmetry Measure, g(1)
App119(2)
Table B.23 Critical Values of the Kurtosis Measure, g(2)
App121(3)
Table B.24 The Arcsine Transformation, p'
App124(3)
Table B.25 Proportions, p, Corresponding to Arcsine Transformations, p'
App127(2)
Table B.26a Binomial Coefficients, (n)C(X)
App129(3)
Table B.26b Proportions of the Binomial Distribution for p = q = 0.5
App132(1)
Table B.27 Critical Values of C for the Sign Test or for the Binomial Test with p = 0.5
App133(10)
Table B.28 Critical Values for Fisher's Exact Test
App143(28)
Table B.29 Critical Values for Runs Test
App171(9)
Table B.30 Critical Values of C for the Mean Square Successive Difference Test
App180(2)
Table B.31 Critical Values for the Runs Up and Down Test
App182(2)
Table B.32 Angular Deviation, s, As a Function of Vector Length, r
App184(2)
Table B.33 Circular Standard Deviation, s(0), As a Function of Vector Length, r
App186(2)
Table B.34 Critical Values of Rayleigh's z
App188(2)
Table B.35 Critical Values of u for the V Test of Circular Uniformity
App190(1)
Table B.36 Critical Values of m for the Hodges-Ajne Test
App191(2)
Table B.37 Correction Factor, K, for the Watson and Williams Test
App193(2)
Table B.38 Critical Values of Watson's U(2)
App195(3)
Table B.39 Critical Values of R' for the Moore Test of Circular Uniformity
App198(1)
Table B.40 Common Logarithms of Factorials
App199(2)
Table B.41 Ten Thousand Random Digits
App201(4)
Figure B.1 Power and Sample Size in Analysis of Variance
App205
ANSWERS TO EXERCISES Ans1
LITERATURE CITED L1
INDEX I1


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