Normal view

## Biostatistical analysis / Jerrold H. Zar.

Editor: Upper Saddle River, N.J. : Prentice Hall, c1999Edición: 4th edDescripción: xii, 663, 212 p. : il. ; 25 cmISBN: 013081542XOtra clasificación: 62-01 (62P10 92B15)
Contenidos:
```1 INTRODUCTION 
1.1 Types of Biological Data 
1.2 Accuracy and Significant Figures 
2 POPULATIONS AND SAMPLES 
2.1 Populations 
2.2 Samples from Populations 
3 MEASURES OF CENTRAL TENDENCY 
3.1 The Arithmetic Mean 
3.2 The Median 
3.3 Other Quantiles 
3.4 The Mode 
4 MEASURES OF DISPERSION AND VARIABILITY 
4.1 The Range 
4.2 Dispersion Measured with Quantiles 
4.3 The Mean Deviation 
4.4 The Variance 
4.5 The Standard Deviation 
5 PROBABILITIES 
5.1 Counting Possible Outcomes 
5.2 Permutations 
5.3 Combinations 
SA Sets 
6 THE NORMAL DISTRIBUTION 
6.1 Symmetry and Kurtosis 
6.2 Proportions of a Normal Distribution 
6.3 The Distribution of Means 
1.3 Frequency Distributions 
1.4 Cumulative Frequency Distributions 
2.3 Random Sampling 
2.4 Parameters and Statistics 
3.5 Other Measures of Central Tendency 
3.6 The Effect of Coding Data 
Exercises 
The Coefficient of Variation 
Indices of Diversity 
The Effect of Coding Data 
Exercises 
5.5 Probability of an Event 
5.7 Multiplying Probabilities 61 Exercises 
6.4 Introduction to Statistical Hypothesis Testing 
6.5 Assessing Departures from Normality 
Exercises 
7 ONE-SAMPLE HYPOTHESES 
7.1 Two-Tailed Hypotheses Concerning the Mean 
7.2 One-Tailed Hypotheses Concerning the Mean 
13 Confidence Limits for the Population Mean 
7.4 Reporting Variability about the Mean 
7.5 Sample Size and Estimation of the Population Mean 
7.6 Power and Sample Size in Tests Concerning the Mean 
7.7 Sampling Finite Populations 
7.8 Confidence Limits for the Population Median 
8 TWO-SAMPLE HYPOTHESES 
8.1 Testing for Difference between Two Means 
8.2 Confidence Limits for Population Means 
8.3 Sample Size and Estimation of the Difference between Two Population Means 
8.4 Power and Sample Size in Tests for Difference between Two Means 
8.5 Testing for Difference between Two Variances 
8.6 Confidence Interval for the Population Variance Ratio 
8.7 Sample Size and Power in Tests for Difference between Two Variances 
9 PAIRED-SAMPLE HYPOTHESES 
9.1 Testing Mean Difference 
9.2 Confidence Limits for the Population Mean Difference 
9.3 Power and Sample Size in Paired-Sample Testing of Means 
9.4 Testing for the Difference between Variances from Two Correlated Populations 
7.9 Hypotheses Concerning the Median 
7.10 Confidence Limits for the Population Variance 
7.11 Hypotheses Concerning the Variance 
7.12 Power and Sample Size in Tests Concerning the Variance 
7.13 Hypotheses Concerning the Coefficient of Variation 
7.14 Hypotheses Concerning Symmetry and Kurtosis 
Exercises 
8.8 Testing for Difference between Two Coefficients of Variation 
8.9 Nonparametric Statistical Methods 
8.10 Two-Sample Rank Testing 
8.11 Testing for Difference between Two Medians 
8.12 The Effect of Coding 
8.13 Two-Sample Testing of Nominal-Scale Data 
8.14 Testing for Difference between Two Diversity Indices 
Exercises 
9.5 Paired-Sample Testing by Ranks 
9.6 Confidence Limits for the Population Median Difference 
9.7 Paired-Sample Testing of Nominal-Scale Data 
Exercises 
10 MULTISAMPLE HYPOTHESES: THE ANALYSIS OF VARIANCE 
10.1 Single-Factor Analysis of Variance 
10.2 Confidence Limits for Population Means 
10.3 Power and Sample Size in Analysis of Variance 
10.4 Nonparametric Analysis of Variance 
10.5 Testing for Difference among Several Medians 
10.6 Homogeneity of Variances 
10.7 Homogeneity of Coefficients of Variation 
10.8 The Effect of Coding 
10.9 Multisample Testing for Nominal-Scale Data 
Exercises 
11 MULTIPLE COMPARISONS 
11.1 The Tukey Test 
11.2 The Newman-Keuls Test 
11.3 Confidence Intervals Following Multiple Comparisons 
114 Comparison of a Control Mean to Each Other Group Mean 
11.5 Scheffé's Multiple Contrasts 
11.6 Nonparametric Multiple Comparisons 
11.7 Nonparametric Multiple Contrasts 
11.8 Multiple Comparisons among Medians 
11.9 Multiple Comparisons among Variances 
Exercises 
12 TWO-FACTOR ANALYSIS OF VARIANCE 
12.1 Two-Factor Analysis of Variance with Equal Replication 
12.2 Two-Factor Analysis of Variance with Unequal Replication 
12.3 Two-Factor Analysis of Variance without Replication 
12.4 The Randomized Block Experimental Design 
12.5 Repeated-Measures Experimental Designs 
12.6 Multiple Comparisons and Confidence Intervals in Two-Factor Analysis of Variance 
12.7 Power and Sample Size in Two-Factor Analysis of Variance 
12.8 Nonparametric Randomized Block or Repeated-Measures Analysis of Variance 
12.9 Multiple Comparisons for Nonparametric Randomized Block or Repeated-Measures Analysis of Variance 
12.10 Dichotomous Nominal-Scale Data in Randomized Blocks or from Repeated Measures 
12.11 Multiple Comparisons with Dichotomous Randomized Block or Repeated-Measures Data 
12.12 Introduction to Analysis of Covariance 
Exercises 
13 DATA TRANSFORMATIONS 
13.1 The Logarithmic Transformation 
13.2 The Square Root Transformation 
133 The Arcsine Transformation 
13.4 Other Transformations 
Exercises 
14 MULTIWAY FACTORIAL ANALYSIS OF VARIANCE 
14.1 Three-Factor Analysis of Variance 
14.2 The Latin Square Experimental Design 
14.3 Higher-Order Factorial Analysis of Variance 
14.4 Blocked and Repeated-Measures Experimental Designs 
14.5 Factorial Analysis of Variance with Unequal Replication 
14.6 Multiple Comparisons and Confidence Intervals in Multiway Analysis of Variance 
14.7 Power and Sample Size in Multiway Analysis of Variance 
Exercises 
15 NESTED (HIERARCHICAL) ANALYSIS OF VARIANCE 
15.1 Nesting within One Main Factor 
15.2 Nesting in Factorial Experiments 
15.3 Multiple Comparisons and Confidence Intervals 
15.4 Power and Sample Size in Nested Analysis of Variance 
Exercise 
16 MULTIVARIATE ANALYSIS OF VARIANCE 
16.1 The Multivariate Normal Distribution 
16.2 Multivariate Analysis of Variance Hypothesis Testing 
17 SIMPLE LINEAR REGRESSION 
17.1 Regression vs. Correlation 
17.2 The Simple Linear Regression Equation 
17.3 Testing the Significance of a Regression 
17.4 Confidence Intervals in Regression 
17.5 Inverse Prediction 
17.6 Interpretations of Regression Functions 
17.7 Regression with Replication and Testing for Linearity 
16.3 Further Analysis 
16.4 Other Experimental Designs 
Exercises 
17.8 Power and Sample Size in Regression 
17.9 Regression through the Origin 
17.10 Data Transformations in Regression 
17.11 The Effect of Coding 357 Exercises 
18 COMPARING SIMPLE LINEAR REGRESSION EQUATIONS 
18.1 Comparing Two Slopes 
18.2 Comparing Two Elevations 
18.3 Comparing Points on Two Regression Lines 
18.4 Comparing more than Two Slopes 
18.5 Comparing more than Two Elevations 
18.6 Multiple Comparisons among Slopes 
Multiple Comparisons among Elevations 
Multiple Comparisons of Points among Regression Lines 
An Overall Test for Coincidental Regressions 
Exercises 
19 SIMPLE LINEAR CORRELATION 
19.1 The Correlation Coefficient 
19.2 Hypotheses about the Correlation Coefficient 
19.3 Confidence Intervals for the Population Correlation Coefficient 
19.4 Power and Sample Size in Correlation 
19.5 Comparing Two Correlation Coefficients 
19.6 Power and Sample Size in Comparing Two Correlation Coefficients 
19.7 Comparing more than Two Correlation Coefficients 
19.8 Multiple Comparisons among Correlation Coefficients 
19.9 Rank Correlation 
19.10 Weighted Rank Correlation 
19.11 Correlation for Dichotomous Nominal-Scale Data 
19.12 Intraclass Correlation 
19.13 Concordance Correlation 
19.14 The Effect of Coding 
Exercises ```
```20 MULTIPLE REGRESSION AND CORRELATION 
20.1 Intermediate Computational Steps 
20.2 The Multiple Regression Equation 
20.3 Analysis of Variance of Multiple Regression or Correlation 
20.4 Hypotheses Concerning Partial Regression Coefficients 
20.5 Standardized Partial Regression Coefficients 
20.6 Partial Correlation 
20.7 Round-off Error and Coding Data 
20.8 Selection of Independent Variables 
20.9 Predicting Y Values 
20.10 Testing Difference between Two Partial
Regression Coefficients 
20.11 “Dummy” Variables 
20.12 Interaction of Independent Variables 
20.13 Comparing Multiple Regression Equations 
20.14 Multiple Regression through the Origin 
20.15 Nonlinear Regression 
20.16 Descriptive vs. Predictive Models 
20.17 Concordance: Rank Correlation among Several Variables 
Exercises 
21 POLYNOMIAL REGRESSION 
21.1 Polynomial Curve Fitting 
21.2 Round-off Error and Coding Data 
Exercises 
22 TESTING FOR GOODNESS OF FIT 
22.1 Chi-Square Goodness of Fit 
22.2 Chi-Square Goodness of Fit for More than Two Categories 
22.3 Subdividing Chi-Square Analyses 
22.4 Chi-Square Correction for Continuity 
22.5 Bias in Chi-Square Calculations 
22.6 Heterogeneity Chi-Square 
22.7 The Log-Likelihood Ratio 
22.8 Kolmogorov-Smirnov Goodness of Fit for Discrete Data 
22.9 Kolmogorov-Smirnov Goodness of Fit for Continuous Data 
22.10 Sample Size Required for Kolmogorov-Smirnov Goodness of Fit for Continuous Data 
Exercises 
23 CONTINGENCY TABLES 
23.1 Chi-Square Analysis of Contingency Tablds 
23.2 Graphing Contingency Table Data 
23.3 The 2x2 Contingency Table 
23.4 Heterogeneity Testing of 2 x 2 Tables 
23.5 Subdividing Contingency Tables 
23.6 Bias ih Chi-Square Contingency Table Analyses 
24 MORE ON DICHOTOMOUS VARIABLES 
24.1 Binomial Probabilities 
24.2 The Hypergeometric Distribution 
24.3 Sampling a Binomial Population 
24.4 Confidence Limits for Population Proportions 
24.5 Goodness of Fit for the Binomial Distribution 
24.6 The Binomial Test 
24.7 The Sign Test 
24.8 Power of the Binomial and Sign Tests 
25 TESTING FOR RANDOMNESS 
25.1 Poisson Probabilities 
25.2 Confidence Limits for the Poisson Parameter 
25.3 Goodness of Fit of the Poisson Distribution 
25.4 The Binomial Test Revisited 
25.5 Comparing Two Poisson Counts 
25.6 Serial Randomness of Nominal-Scale Categories 
23.7 The Log-Likelihood Ratio for Contingency Tables 
23.8 Three-Dimensional Contingency Tables 
23.9 Log-Linear Models for Multidimensional Contingency Tables 
Exercises 
24.9 Confidence Interval for the Population Median 
24.10 The Fisher Exact Test 
24.11 Comparing Two Proportions 
24.12 Power and Sample Size in Comparing Two Proportions 
24.13 Comparing more than Two Proportions 
24.14 Multiple Comparisons for Proportions 
24.15 Trends among Proportions 
Exercises 
25.7 Serial Randomness of Measurements: Parametric Testing 
25.8 Serial Randomness of Measurements: Nonparametric Testing 
Exercises 
26 CIRCULAR DISTRIBUTIONS: DESCRIPTIVE STATISTICS 
26.1 Data on a Circular Scale 
26.2 Graphical Presentation of Circular Data 
26.3 Sines and Cosines of Circular Data 
26.4 The Mean Angle 
26.5 Angular Dispersion 
26.6 The Median and Modal Angles 
26.7 Confidence Limits for the Population Mean and Median Angles 
26.8 Diametrically Bimodal Distributions 
26.9 Second-Order Analysis: The Mean of Mean Angles 
26.10 Confidence Limits for the Second-Order Mean Angle 
Exercises 
27 CIRCULAR DISTRIBUTIONS: HYPOTHESIS TESTING 
[27.6]
27.1 Testing Significance of the Mean Angle: Unimodal Distributions 
27.2 Testing Significance of the Median Angle: Omnibus Test 
27.3 Testing Significance of the Median Angle: Binomial Test 
27.4 Testing Symmetry around the Median Angle 
27.5 Two-Sample and Multisample Testing of Mean Angles 
Nonparametric Two-Sample and Multisample Testing of Angles 
27.7 Two-Sample and Multisample Testing of Median Angles 
27.8 Two-Sample and Multisample Testing of Angular Distances 
27.9 Two-Sample and Multisample Testing of Angular Dispersion 
27.10 Parametric One-Sample Second-Order Analysis of Angles 
27 CIRCULAR DISTRIBUTIONS: HYPOTHESIS TESTING (continued)
27.11 Nonparametric One-Sample Second-Order Analysis of Angles 
27.12 Parametric Two-Sample Second-Order Analysis of Angles 
27.13 Nonparametric Two-Sample Second-Order Analysis of Angles 
27.14 Parametric Paired-Sample Testing with Angles 
27.15 Nonparametric Paired-Sample Testing with Angles 
27.16 Parametric Angular Correlation and Regression 
27.17 Nonparametric Angular Correlation 
27.18 Goodness of Fit Testing for Circular Distributions 
27.19 Serial Randomness of Nominal-Scale Categories on a Circle 
Exercises 
APPENDIX A ANALYSIS OF VARIANCE HYPOTHESIS TESTING Appt
A.1 Determination of Appropriate F’s and Degrees of Freedom Appl
A.2 Two-Factor Analysis of Variance App5
A.3 Three-Factor Analysis of Variance App6
A.4 Nested Analysis of Variance App7
A.5 Split-Plot and Mixed Within-Subjects Analysis of Variance App8
APPENDIX B STATISTICAL TABLES AND GRAPHS App11
Table B.l Critical Values of Chi-Square Distribution Appl2
Table B.2 Proportions of the Normal Curve (One-Tailed) Appl7
Table B.3 Critical Values of the t Distribution Appl9
Table B.4 Critical Values of the F Distribution App21
Table B.5 Critical Values of the q Distribution App58
Table B.6 Critical Values of q' for the One-Tailed Dunnett’s Test App74
Table B.7 Critical Values of q' for the Two-Tailed Dunnett’s Test App76
Table B.8 Critical Values of dmax for the Kolmogorov-Smirnov Goodness of Fit Test for Discrete or Grouped Data App77
Table B.9 Critical Values of D for the Kolmogorov-Smirnov Goodness of Fit Test for Continuous Distributions App83
Table B.10 Critical Values of D§ for the 5-Corrected Kolmogorov-Smirnov Goodness of Fit Test for Continuous Distributions App87
Table B.ll Critical Values of the Mann-Whitney U Distribution App89
Table B.12 Critical Values of the Wilcoxon T Distribution ApplOl
Table B.13 Critical Values of the Kruskal-Wallis H Distribution Appl04
Table B.14 Critical Values of the Friedman Xr Distribution Appl06
Table B.15 Critical Values of Q for Nonparametric Multiple Comparison Testing Appl07
Table B.16 Critical Values of Q' for Nonparametric Multiple Comparison Testing with a Control Appl08
Table B.17 Critical Values of the Correlation Coefficient, r Appl09
Table B.18 Fisher’s z Transformation for Correlation Coefficients, r Appl 
Table B.19 Correlation Coefficients, r, Corresponding to Fisher’s z Transformation Appl 
Table B.20 Critical Values of the Spearman Rank Correlation Coefficient, rs Appl 
Table B.21 Critical Values of the Top-Down Correlation Coefficient, rT Appl 
Table B.22 Critical Values of the Symmetry Measure, g1 Appl 
Table B.23 Critical Values of the Kurtosis Measure, 82 Appl21
Table B.24 The Arcsine Transformation, p' Appl24
Table B.25 Proportions, p, Corresponding to Arcsine Transformations, p' Appl27
Table B.26a Binomial Coefficients, n Cx Appl29
Table B.26b Proportions of the Binomial Distribution for p = q = 0.5 Appl32
Table B.27 Critical Values of C for the Sign Test or for the Binomial Test with p = 0.5 Appl33
Table B.28 Critical Values for Fisher’s Exact
Test Appl43
Table B.29 Critical Values for Runs Test Appl71
Table B.30 Critical Values of C for the Mean Square Successive Difference Test Appl80
Table B.31 Critical Values for the Runs Up and Down Test Appl82
APPENDIX B STATISTICAL TABLES AND GRAPHS (continued)
Table B.32 Angular Deviation, s, As a Function of Vector Length, r Appl84
Table B.33 Circular Standard Deviation, so, As a Function of Vector Length, r Appl86
Table B.34 Critical Values of Rayleigh’s z Appl88
Table B.35 Critical Values of u for the V Test of Circular Uniformity Appl90
Table B.36 Critical Values of m for the Hodges-Ajne Test Appl91
Table B.37 Correction Factor, K, for the Watson and Williams Test Appl93
Table B.38 Critical Values of Watson’s I/2 Appl95
Table B.39 Critical Values of R' for the Moore Test of Circular Uniformity Appl98
Table B.40 Common Logarithms of Factorials Appl99
Table B.41 Ten Thousand Random Digits App201 Figure B.l Power and Sample Size in Analysis of Variance App205
LITERATURE CITED L1
INDEX ``` Average rating: 0.0 (0 votes)
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1 INTRODUCTION  --
1.1 Types of Biological Data  --
1.2 Accuracy and Significant Figures  --
2 POPULATIONS AND SAMPLES  --
2.1 Populations  --
2.2 Samples from Populations  --
3 MEASURES OF CENTRAL TENDENCY  --
3.1 The Arithmetic Mean  --
3.2 The Median  --
3.3 Other Quantiles  --
3.4 The Mode  --
4 MEASURES OF DISPERSION AND VARIABILITY  --
4.1 The Range  --
4.2 Dispersion Measured with Quantiles  --
4.3 The Mean Deviation  --
4.4 The Variance  --
4.5 The Standard Deviation  --
5 PROBABILITIES  --
5.1 Counting Possible Outcomes  --
5.2 Permutations  --
5.3 Combinations  --
SA Sets  --
6 THE NORMAL DISTRIBUTION  --
6.1 Symmetry and Kurtosis  --
6.2 Proportions of a Normal Distribution  --
6.3 The Distribution of Means  --
1.3 Frequency Distributions  --
1.4 Cumulative Frequency Distributions  --
2.3 Random Sampling  --
2.4 Parameters and Statistics  --
3.5 Other Measures of Central Tendency  --
3.6 The Effect of Coding Data  --
Exercises  --
The Coefficient of Variation  --
Indices of Diversity  --
The Effect of Coding Data  --
Exercises  --
5.5 Probability of an Event  --
5.7 Multiplying Probabilities 61 Exercises  --
6.4 Introduction to Statistical Hypothesis Testing  --
6.5 Assessing Departures from Normality  --
Exercises  --
7 ONE-SAMPLE HYPOTHESES  --
7.1 Two-Tailed Hypotheses Concerning the Mean  --
7.2 One-Tailed Hypotheses Concerning the Mean  --
13 Confidence Limits for the Population Mean  --
7.4 Reporting Variability about the Mean  --
7.5 Sample Size and Estimation of the Population Mean  --
7.6 Power and Sample Size in Tests Concerning the Mean  --
7.7 Sampling Finite Populations  --
7.8 Confidence Limits for the Population Median  --
8 TWO-SAMPLE HYPOTHESES  --
8.1 Testing for Difference between Two Means  --
8.2 Confidence Limits for Population Means  --
8.3 Sample Size and Estimation of the Difference between Two Population Means  --
8.4 Power and Sample Size in Tests for Difference between Two Means  --
8.5 Testing for Difference between Two Variances  --
8.6 Confidence Interval for the Population Variance Ratio  --
8.7 Sample Size and Power in Tests for Difference between Two Variances  --
9 PAIRED-SAMPLE HYPOTHESES  --
9.1 Testing Mean Difference  --
9.2 Confidence Limits for the Population Mean Difference  --
9.3 Power and Sample Size in Paired-Sample Testing of Means  --
9.4 Testing for the Difference between Variances from Two Correlated Populations  --
7.9 Hypotheses Concerning the Median  --
7.10 Confidence Limits for the Population Variance  --
7.11 Hypotheses Concerning the Variance  --
7.12 Power and Sample Size in Tests Concerning the Variance  --
7.13 Hypotheses Concerning the Coefficient of Variation  --
7.14 Hypotheses Concerning Symmetry and Kurtosis  --
Exercises  --
8.8 Testing for Difference between Two Coefficients of Variation  --
8.9 Nonparametric Statistical Methods  --
8.10 Two-Sample Rank Testing  --
8.11 Testing for Difference between Two Medians  --
8.12 The Effect of Coding  --
8.13 Two-Sample Testing of Nominal-Scale Data  --
8.14 Testing for Difference between Two Diversity Indices  --
Exercises  --
9.5 Paired-Sample Testing by Ranks  --
9.6 Confidence Limits for the Population Median Difference  --
9.7 Paired-Sample Testing of Nominal-Scale Data  --
Exercises  --
10 MULTISAMPLE HYPOTHESES: THE ANALYSIS OF VARIANCE  --
10.1 Single-Factor Analysis of Variance  --
10.2 Confidence Limits for Population Means  --
10.3 Power and Sample Size in Analysis of Variance  --
10.4 Nonparametric Analysis of Variance  --
10.5 Testing for Difference among Several Medians  --
10.6 Homogeneity of Variances  --
10.7 Homogeneity of Coefficients of Variation  --
10.8 The Effect of Coding  --
10.9 Multisample Testing for Nominal-Scale Data  --
Exercises  --
11 MULTIPLE COMPARISONS  --
11.1 The Tukey Test  --
11.2 The Newman-Keuls Test  --
11.3 Confidence Intervals Following Multiple Comparisons  --
114 Comparison of a Control Mean to Each Other Group Mean  --
11.5 Scheffé's Multiple Contrasts  --
11.6 Nonparametric Multiple Comparisons  --
11.7 Nonparametric Multiple Contrasts  --
11.8 Multiple Comparisons among Medians  --
11.9 Multiple Comparisons among Variances  --
Exercises  --
12 TWO-FACTOR ANALYSIS OF VARIANCE  --
12.1 Two-Factor Analysis of Variance with Equal Replication  --
12.2 Two-Factor Analysis of Variance with Unequal Replication  --
12.3 Two-Factor Analysis of Variance without Replication  --
12.4 The Randomized Block Experimental Design  --
12.5 Repeated-Measures Experimental Designs  --
12.6 Multiple Comparisons and Confidence Intervals in Two-Factor Analysis of Variance  --
12.7 Power and Sample Size in Two-Factor Analysis of Variance  --
12.8 Nonparametric Randomized Block or Repeated-Measures Analysis of Variance  --
12.9 Multiple Comparisons for Nonparametric Randomized Block or Repeated-Measures Analysis of Variance  --
12.10 Dichotomous Nominal-Scale Data in Randomized Blocks or from Repeated Measures  --
12.11 Multiple Comparisons with Dichotomous Randomized Block or Repeated-Measures Data  --
12.12 Introduction to Analysis of Covariance  --
Exercises  --
13 DATA TRANSFORMATIONS  --
13.1 The Logarithmic Transformation  --
13.2 The Square Root Transformation  --
133 The Arcsine Transformation  --
13.4 Other Transformations  --
Exercises  --
14 MULTIWAY FACTORIAL ANALYSIS OF VARIANCE  --
14.1 Three-Factor Analysis of Variance  --
14.2 The Latin Square Experimental Design  --
14.3 Higher-Order Factorial Analysis of Variance  --
14.4 Blocked and Repeated-Measures Experimental Designs  --
14.5 Factorial Analysis of Variance with Unequal Replication  --
14.6 Multiple Comparisons and Confidence Intervals in Multiway Analysis of Variance  --
14.7 Power and Sample Size in Multiway Analysis of Variance  --
Exercises  --
15 NESTED (HIERARCHICAL) ANALYSIS OF VARIANCE  --
15.1 Nesting within One Main Factor  --
15.2 Nesting in Factorial Experiments  --
15.3 Multiple Comparisons and Confidence Intervals  --
15.4 Power and Sample Size in Nested Analysis of Variance  --
Exercise  --
16 MULTIVARIATE ANALYSIS OF VARIANCE  --
16.1 The Multivariate Normal Distribution  --
16.2 Multivariate Analysis of Variance Hypothesis Testing  --
17 SIMPLE LINEAR REGRESSION  --
17.1 Regression vs. Correlation  --
17.2 The Simple Linear Regression Equation  --
17.3 Testing the Significance of a Regression  --
17.4 Confidence Intervals in Regression  --
17.5 Inverse Prediction  --
17.6 Interpretations of Regression Functions  --
17.7 Regression with Replication and Testing for Linearity  --
16.3 Further Analysis  --
16.4 Other Experimental Designs  --
Exercises  --
17.8 Power and Sample Size in Regression  --
17.9 Regression through the Origin  --
17.10 Data Transformations in Regression  --
17.11 The Effect of Coding 357 Exercises  --
18 COMPARING SIMPLE LINEAR REGRESSION EQUATIONS  --
18.1 Comparing Two Slopes  --
18.2 Comparing Two Elevations  --
18.3 Comparing Points on Two Regression Lines  --
18.4 Comparing more than Two Slopes  --
18.5 Comparing more than Two Elevations  --
18.6 Multiple Comparisons among Slopes  --
Multiple Comparisons among Elevations  --
Multiple Comparisons of Points among Regression Lines  --
An Overall Test for Coincidental Regressions  --
Exercises  --
19 SIMPLE LINEAR CORRELATION  --
19.1 The Correlation Coefficient  --
19.2 Hypotheses about the Correlation Coefficient  --
19.3 Confidence Intervals for the Population Correlation Coefficient  --
19.4 Power and Sample Size in Correlation  --
19.5 Comparing Two Correlation Coefficients  --
19.6 Power and Sample Size in Comparing Two Correlation Coefficients  --
19.7 Comparing more than Two Correlation Coefficients  --
19.8 Multiple Comparisons among Correlation Coefficients  --
19.9 Rank Correlation  --
19.10 Weighted Rank Correlation  --
19.11 Correlation for Dichotomous Nominal-Scale Data  --
19.12 Intraclass Correlation  --
19.13 Concordance Correlation  --
19.14 The Effect of Coding  --
Exercises  --

20 MULTIPLE REGRESSION AND CORRELATION  --
20.1 Intermediate Computational Steps  --
20.2 The Multiple Regression Equation  --
20.3 Analysis of Variance of Multiple Regression or Correlation  --
20.4 Hypotheses Concerning Partial Regression Coefficients  --
20.5 Standardized Partial Regression Coefficients  --
20.6 Partial Correlation  --
20.7 Round-off Error and Coding Data  --
20.8 Selection of Independent Variables  --
20.9 Predicting Y Values  --
20.10 Testing Difference between Two Partial --
Regression Coefficients  --
20.11 “Dummy” Variables  --
20.12 Interaction of Independent Variables  --
20.13 Comparing Multiple Regression Equations  --
20.14 Multiple Regression through the Origin  --
20.15 Nonlinear Regression  --
20.16 Descriptive vs. Predictive Models  --
20.17 Concordance: Rank Correlation among Several Variables  --
Exercises  --
21 POLYNOMIAL REGRESSION  --
21.1 Polynomial Curve Fitting  --
21.2 Round-off Error and Coding Data  --
Exercises  --
22 TESTING FOR GOODNESS OF FIT  --
22.1 Chi-Square Goodness of Fit  --
22.2 Chi-Square Goodness of Fit for More than Two Categories  --
22.3 Subdividing Chi-Square Analyses  --
22.4 Chi-Square Correction for Continuity  --
22.5 Bias in Chi-Square Calculations  --
22.6 Heterogeneity Chi-Square  --
22.7 The Log-Likelihood Ratio  --
22.8 Kolmogorov-Smirnov Goodness of Fit for Discrete Data  --
22.9 Kolmogorov-Smirnov Goodness of Fit for Continuous Data  --
22.10 Sample Size Required for Kolmogorov-Smirnov Goodness of Fit for Continuous Data  --
Exercises  --
23 CONTINGENCY TABLES  --
23.1 Chi-Square Analysis of Contingency Tablds  --
23.2 Graphing Contingency Table Data  --
23.3 The 2x2 Contingency Table  --
23.4 Heterogeneity Testing of 2 x 2 Tables  --
23.5 Subdividing Contingency Tables  --
23.6 Bias ih Chi-Square Contingency Table Analyses  --
24 MORE ON DICHOTOMOUS VARIABLES  --
24.1 Binomial Probabilities  --
24.2 The Hypergeometric Distribution  --
24.3 Sampling a Binomial Population  --
24.4 Confidence Limits for Population Proportions  --
24.5 Goodness of Fit for the Binomial Distribution  --
24.6 The Binomial Test  --
24.7 The Sign Test  --
24.8 Power of the Binomial and Sign Tests  --
25 TESTING FOR RANDOMNESS  --
25.1 Poisson Probabilities  --
25.2 Confidence Limits for the Poisson Parameter  --
25.3 Goodness of Fit of the Poisson Distribution  --
25.4 The Binomial Test Revisited  --
25.5 Comparing Two Poisson Counts  --
25.6 Serial Randomness of Nominal-Scale Categories  --
23.7 The Log-Likelihood Ratio for Contingency Tables  --
23.8 Three-Dimensional Contingency Tables  --
23.9 Log-Linear Models for Multidimensional Contingency Tables  --
Exercises  --
24.9 Confidence Interval for the Population Median  --
24.10 The Fisher Exact Test  --
24.11 Comparing Two Proportions  --
24.12 Power and Sample Size in Comparing Two Proportions  --
24.13 Comparing more than Two Proportions  --
24.14 Multiple Comparisons for Proportions  --
24.15 Trends among Proportions  --
Exercises  --
25.7 Serial Randomness of Measurements: Parametric Testing  --
25.8 Serial Randomness of Measurements: Nonparametric Testing  --
Exercises  --
26 CIRCULAR DISTRIBUTIONS: DESCRIPTIVE STATISTICS  --
26.1 Data on a Circular Scale  --
26.2 Graphical Presentation of Circular Data  --
26.3 Sines and Cosines of Circular Data  --
26.4 The Mean Angle  --
26.5 Angular Dispersion  --
26.6 The Median and Modal Angles  --
26.7 Confidence Limits for the Population Mean and Median Angles  --
26.8 Diametrically Bimodal Distributions  --
26.9 Second-Order Analysis: The Mean of Mean Angles  --
26.10 Confidence Limits for the Second-Order Mean Angle  --
Exercises  --
27 CIRCULAR DISTRIBUTIONS: HYPOTHESIS TESTING  --
[27.6] --
27.1 Testing Significance of the Mean Angle: Unimodal Distributions  --
27.2 Testing Significance of the Median Angle: Omnibus Test  --
27.3 Testing Significance of the Median Angle: Binomial Test  --
27.4 Testing Symmetry around the Median Angle  --
27.5 Two-Sample and Multisample Testing of Mean Angles  --
Nonparametric Two-Sample and Multisample Testing of Angles  --
27.7 Two-Sample and Multisample Testing of Median Angles  --
27.8 Two-Sample and Multisample Testing of Angular Distances  --
27.9 Two-Sample and Multisample Testing of Angular Dispersion  --
27.10 Parametric One-Sample Second-Order Analysis of Angles  --
27 CIRCULAR DISTRIBUTIONS: HYPOTHESIS TESTING (continued) --
27.11 Nonparametric One-Sample Second-Order Analysis of Angles  --
27.12 Parametric Two-Sample Second-Order Analysis of Angles  --
27.13 Nonparametric Two-Sample Second-Order Analysis of Angles  --
27.14 Parametric Paired-Sample Testing with Angles  --
27.15 Nonparametric Paired-Sample Testing with Angles  --
27.16 Parametric Angular Correlation and Regression  --
27.17 Nonparametric Angular Correlation  --
27.18 Goodness of Fit Testing for Circular Distributions  --
27.19 Serial Randomness of Nominal-Scale Categories on a Circle  --
Exercises  --
APPENDIX A ANALYSIS OF VARIANCE HYPOTHESIS TESTING Appt --
A.1 Determination of Appropriate F’s and Degrees of Freedom Appl --
A.2 Two-Factor Analysis of Variance App5 --
A.3 Three-Factor Analysis of Variance App6 --
A.4 Nested Analysis of Variance App7 --
A.5 Split-Plot and Mixed Within-Subjects Analysis of Variance App8 --
APPENDIX B STATISTICAL TABLES AND GRAPHS App11 --
Table B.l Critical Values of Chi-Square Distribution Appl2 --
Table B.2 Proportions of the Normal Curve (One-Tailed) Appl7 --
Table B.3 Critical Values of the t Distribution Appl9 --
Table B.4 Critical Values of the F Distribution App21 --
Table B.5 Critical Values of the q Distribution App58 --
Table B.6 Critical Values of q' for the One-Tailed Dunnett’s Test App74 --
Table B.7 Critical Values of q' for the Two-Tailed Dunnett’s Test App76 --
Table B.8 Critical Values of dmax for the Kolmogorov-Smirnov Goodness of Fit Test for Discrete or Grouped Data App77 --
Table B.9 Critical Values of D for the Kolmogorov-Smirnov Goodness of Fit Test for Continuous Distributions App83 --
Table B.10 Critical Values of D§ for the 5-Corrected Kolmogorov-Smirnov Goodness of Fit Test for Continuous Distributions App87 --
Table B.ll Critical Values of the Mann-Whitney U Distribution App89 --
Table B.12 Critical Values of the Wilcoxon T Distribution ApplOl --
Table B.13 Critical Values of the Kruskal-Wallis H Distribution Appl04 --
Table B.14 Critical Values of the Friedman Xr Distribution Appl06 --
Table B.15 Critical Values of Q for Nonparametric Multiple Comparison Testing Appl07 --
Table B.16 Critical Values of Q' for Nonparametric Multiple Comparison Testing with a Control Appl08 --
Table B.17 Critical Values of the Correlation Coefficient, r Appl09 --
Table B.18 Fisher’s z Transformation for Correlation Coefficients, r Appl  --
Table B.19 Correlation Coefficients, r, Corresponding to Fisher’s z Transformation Appl  --
Table B.20 Critical Values of the Spearman Rank Correlation Coefficient, rs Appl  --
Table B.21 Critical Values of the Top-Down Correlation Coefficient, rT Appl  --
Table B.22 Critical Values of the Symmetry Measure, g1 Appl  --
Table B.23 Critical Values of the Kurtosis Measure, 82 Appl21 --
Table B.24 The Arcsine Transformation, p' Appl24 --
Table B.25 Proportions, p, Corresponding to Arcsine Transformations, p' Appl27 --
Table B.26a Binomial Coefficients, n Cx Appl29 --
Table B.26b Proportions of the Binomial Distribution for p = q = 0.5 Appl32 --
Table B.27 Critical Values of C for the Sign Test or for the Binomial Test with p = 0.5 Appl33 --
Table B.28 Critical Values for Fisher’s Exact --
Test Appl43 --
Table B.29 Critical Values for Runs Test Appl71 --
Table B.30 Critical Values of C for the Mean Square Successive Difference Test Appl80 --
Table B.31 Critical Values for the Runs Up and Down Test Appl82 --
APPENDIX B STATISTICAL TABLES AND GRAPHS (continued) --
Table B.32 Angular Deviation, s, As a Function of Vector Length, r Appl84 --
Table B.33 Circular Standard Deviation, so, As a Function of Vector Length, r Appl86 --
Table B.34 Critical Values of Rayleigh’s z Appl88 --
Table B.35 Critical Values of u for the V Test of Circular Uniformity Appl90 --
Table B.36 Critical Values of m for the Hodges-Ajne Test Appl91 --
Table B.37 Correction Factor, K, for the Watson and Williams Test Appl93 --
Table B.38 Critical Values of Watson’s I/2 Appl95 --
Table B.39 Critical Values of R' for the Moore Test of Circular Uniformity Appl98 --
Table B.40 Common Logarithms of Factorials Appl99 --
Table B.41 Ten Thousand Random Digits App201 Figure B.l Power and Sample Size in Analysis of Variance App205 --
LITERATURE CITED L1 --
INDEX  --

MR, REVIEW #

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