Biometry : the principles and practice of statistics in biological research / Robert R. Sokal and F. James Rohlf.
Editor: San Francisco : W. H. Freeman, c1981Edición: 2nd edDescripción: xviii, 859 p. : il. ; 24 cmISBN: 0716712547Tema(s): Biometry | BiometryOtra clasificación: *CODIGO*PREFACE xi NOTES ON THE SECOND EDITION xvi 1. INTRODUCTION [1] 1.1 Some definitions [1] 1.2 The development of biometry [3] 1.3 The statistical frame of mind [5] 2. DATA IN BIOLOGY [8] 2.1 Samples and populations [8] 2.2 Variables in biology [10] 2.3 Accuracy and precision of data [13] 2.4 Derived variables [16] 2.5 Frequency distributions [19] 3. THE HANDLING OF DATA [32] 3.1 Calculators and computers [33] 3.2 Efficiency and economy in data processing [36] 4. DESCRIPTIVE STATISTICS [38] 4.1 The arithmetic mean [39] 4.2 Other means [42] 4.3 The median [43] 4.4 The mode [46] 4.5 Simple statistics of dispersion [48] 4.6 The standard deviation [49] 4.7 Sample statistics and parameters [52] 4.8 Coding of data before computation [54] 4.9 Methods for computing mean and standard deviation [55] 4.10 The coefficient of variation [58] 5. INTRODUCTION TO PROBABILITY DISTRIBUTIONS: BINOMIAL AND POISSON [62] 5.1 Probability, random sampling, and hypothesis testing [64] 5.2 The binomial distribution [70] 5.3 The Poisson distribution [82] 5.4 Some other discrete probability distributions [94] 6. THE NORMAL PROBABILITY DISTRIBUTION [98] 6.1 Frequency distributions of continuous variables [99] 6.2 Properties of the normal distribution [101] 6.3 A model for the normal distribution [106] 6.4 Applications of the normal distribution [109] 6.5 Fitting a normal distribution to observed data [111] 6.6 Skewness and kurtosis [114] 6.7 Graphic methods [117] 6.8 Other continuous distributions [126] 7. ESTIMATION AND HYPOTHESIS TESTING [128] 7.1 Distribution and variance of means [129] 7.2 Distribution and variance of other statistics [137] 7.3 Introduction to confidence limits [140] 7.4 Student's t-distribution [145] 7.5 Confidence limits based on sample statistics [147] 7.6 The chi-square distribution [152] 7.7 Confidence limits for variances [155] 7.8 Introduction to hypothesis testing [157] 7.9 Tests of simple hypotheses employing the normal and t-distributions [170] 7.10 Testing the hypothesis Ho: σ2 = σ20 [175] 8. INTRODUCTION TO ANALYSIS OF VARIANCE [179] 8.1 The variances of samples and their means [180] 8.2 The F-distribution [185] 8.3 The hypothesis Ho: σ2 = σ22 [189] 8.4 Heterogeneity among sample means [191] 8.5 Partitioning the total sum of squares and degrees of freedom [198] 8.6 Model I anova [202] 8.7 Model II anova [205] 9. SINGLE CLASSIFICATION ANALYSIS OF VARIANCE [208] 9.1 Computational formulas [209] 9.2 General case: unequal n [210] 9.3 Special case: equal n [219] 9.4 Special case: two groups [222] 9.5 Special case: a single specimen compared with a sample [229] 9.6 Comparisons among means: planned comparisons [232] 9.7 Comparisons among means: unplanned comparisons [242] 9.8 Finding the sample size n required for a test [262] 10. NESTED ANALYSIS OF VARIANCE [271] 10.1 Nested anova: design [271] 10.2 Nested anova: computation [274] 10.3 Nested anovas with unequal sample sizes [293] 10.4 The optimal allocation of resources [309] 11. TWO-WAY ANALYSIS OF VARIANCE [321] 11.1 Two-way anova: design [321] 11.2 Two-way anova with replication: computation [324] 11.3 Two-way anova: significance testing [332] 11.4 Two-way anova without replication [344] 11.5 Paired comparisons [354] 11.6 Unequal subclass sizes [360] 11.7 Missing values in a randomized blocks design [364] 12. MULTIWAY ANALYSIS OF VARIANCE [372] 12.1 The factorial design [372] 12.2 A three-way factorial anova [374] 12.3 Higher-order factorials [387] 12.4 Other designs [393] 12.5 Anova by computer [395] 13. ASSUMPTIONS OF ANALYSIS OF VARIANCE [400] 13.1 A fundamental assumption [401] 13.2 Independence [401] 13.3 Homogeneity of variances [402] 13.4 Normality [412] 13.5 Additivity [414] 13.6 Transformations [417] 13.7 The logarithmic transformation [419] 13.8 The square root transformation [421] 13.9 The Box-Cox transformation [423] 13.10 The arcsine transformation [427] 13.11 Nonparametric methods in lieu of single classification anova [429] 13.12 Nonparametric methods in lieu of two-way anova [445] 14. LINEAR REGRESSION [454] 14.1 Introduction to regression [455] 14.2 Models in regression [458] 14.3 The linear regression equation [461] 14.4 Tests of significance in regression [469] 14.5 More than one value of Y for each value of X [477] 14.6 The uses of regression [491] 14.7 Estimation of X from Y [496] 14.8 Comparison of regression lines [499] 14.9 Analysis of covariance [509] 14.10 Linear comparisons in anova [530] 14.11 Examination of residuals and transformations in regression [539] 14.12 Nonparametric tests for regression [546] 14.13 Model II regression [547] 15. CORRELATION [561] 15.1 Correlation and regression [562] 15.2 The product-moment correlation coefficient [565] 15.3 The variance of sums and differences [573] 15.4 Computation of the product-moment correlation coefficient [575] 15.5 Significance tests in correlation [583] 15.6 Applications of correlation [591] 15.7 Principal axes and confidence regions [594] 15.8 Nonparametric tests for association [601] 16. MULTIPLE AND CURVILINEAR REGRESSION [617] 16.1 Multiple regression: computations [618] 16.2 Multiple regression: significance tests [631] 16.3 Path analysis [642] 16.4 Partial and multiple correlation [656] 16.5 Choosing predictor variables [661] 16.6 Curvilinear regression [671] 16.7 Advanced topics in regression and correlation [683] 17. ANALYSIS OF FREQUENCIES [691] 17.1 Tests for goodness of fit: introduction [692] 17.2 Single classification goodness of fit tests [704] 17.3 Replicated tests of goodness of fit [721] 17.4 Tests of independence: two-way tables [731] 17.5 The analysis of three-way and multiway tables [747] 17.6 Finding the sample size n required to test the difference between two percentages [765] 17.7 Randomized blocks for frequency data [767] 18. MISCELLANEOUS METHODS [779] 18.1 Combining probabilities from tests of significance [779] 18.2 Tests for randomness: runs tests [782] 18.3 Randomization tests [787] 18.4 The jackknife [795] 18.5 The future of biometry: data analysis [799] APPENDIXES Al Mathematical appendix [806] A2 A package of statistical computer programs [822] BIBLIOGRAPHY [826] AUTHOR INDEX [839] SUBJECT INDEX [843]
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| 62 Si587s Statistical inference, | 62 So683 Introducción a la bioestadística / | 62 So683 Introducción a la bioestadística / | 62 So683-2 Biometry : | 62 St794 Linear statistical models / | 62 St797 Statistical methods of model building / | 62 St797-1 Statistical methods for physical science / |
Incluye índice.
Bibliografía: p. 826-837.
PREFACE xi --
NOTES ON THE SECOND EDITION xvi --
1. INTRODUCTION [1] --
1.1 Some definitions [1] --
1.2 The development of biometry [3] --
1.3 The statistical frame of mind [5] --
2. DATA IN BIOLOGY [8] --
2.1 Samples and populations [8] --
2.2 Variables in biology [10] --
2.3 Accuracy and precision of data [13] --
2.4 Derived variables [16] --
2.5 Frequency distributions [19] --
3. THE HANDLING OF DATA [32] --
3.1 Calculators and computers [33] --
3.2 Efficiency and economy in data processing [36] --
4. DESCRIPTIVE STATISTICS [38] --
4.1 The arithmetic mean [39] --
4.2 Other means [42] --
4.3 The median [43] --
4.4 The mode [46] --
4.5 Simple statistics of dispersion [48] --
4.6 The standard deviation [49] --
4.7 Sample statistics and parameters [52] --
4.8 Coding of data before computation [54] --
4.9 Methods for computing mean and standard deviation [55] --
4.10 The coefficient of variation [58] --
5. INTRODUCTION TO PROBABILITY DISTRIBUTIONS: BINOMIAL AND POISSON [62] --
5.1 Probability, random sampling, and hypothesis testing [64] --
5.2 The binomial distribution [70] --
5.3 The Poisson distribution [82] --
5.4 Some other discrete probability distributions [94] --
6. THE NORMAL PROBABILITY DISTRIBUTION [98] --
6.1 Frequency distributions of continuous variables [99] --
6.2 Properties of the normal distribution [101] --
6.3 A model for the normal distribution [106] --
6.4 Applications of the normal distribution [109] --
6.5 Fitting a normal distribution to observed data [111] --
6.6 Skewness and kurtosis [114] --
6.7 Graphic methods [117] --
6.8 Other continuous distributions [126] --
7. ESTIMATION AND HYPOTHESIS TESTING [128] --
7.1 Distribution and variance of means [129] --
7.2 Distribution and variance of other statistics [137] --
7.3 Introduction to confidence limits [140] --
7.4 Student's t-distribution [145] --
7.5 Confidence limits based on sample statistics [147] --
7.6 The chi-square distribution [152] --
7.7 Confidence limits for variances [155] --
7.8 Introduction to hypothesis testing [157] --
7.9 Tests of simple hypotheses employing the normal and t-distributions [170] --
7.10 Testing the hypothesis Ho: σ2 = σ20 [175] --
8. INTRODUCTION TO ANALYSIS OF VARIANCE [179] --
8.1 The variances of samples and their means [180] --
8.2 The F-distribution [185] --
8.3 The hypothesis Ho: σ2 = σ22 [189] --
8.4 Heterogeneity among sample means [191] --
8.5 Partitioning the total sum of squares and degrees of freedom [198] --
8.6 Model I anova [202] --
8.7 Model II anova [205] --
9. SINGLE CLASSIFICATION ANALYSIS OF VARIANCE [208] --
9.1 Computational formulas [209] --
9.2 General case: unequal n [210] --
9.3 Special case: equal n [219] --
9.4 Special case: two groups [222] --
9.5 Special case: a single specimen compared with a sample [229] --
9.6 Comparisons among means: planned comparisons [232] --
9.7 Comparisons among means: unplanned comparisons [242] --
9.8 Finding the sample size n required for a test [262] --
10. NESTED ANALYSIS OF VARIANCE [271] --
10.1 Nested anova: design [271] --
10.2 Nested anova: computation [274] --
10.3 Nested anovas with unequal sample sizes [293] --
10.4 The optimal allocation of resources [309] --
11. TWO-WAY ANALYSIS OF VARIANCE [321] --
11.1 Two-way anova: design [321] --
11.2 Two-way anova with replication: computation [324] --
11.3 Two-way anova: significance testing [332] --
11.4 Two-way anova without replication [344] --
11.5 Paired comparisons [354] --
11.6 Unequal subclass sizes [360] --
11.7 Missing values in a randomized blocks design [364] --
12. MULTIWAY ANALYSIS OF VARIANCE [372] --
12.1 The factorial design [372] --
12.2 A three-way factorial anova [374] --
12.3 Higher-order factorials [387] --
12.4 Other designs [393] --
12.5 Anova by computer [395] --
13. ASSUMPTIONS OF ANALYSIS OF VARIANCE [400] --
13.1 A fundamental assumption [401] --
13.2 Independence [401] --
13.3 Homogeneity of variances [402] --
13.4 Normality [412] --
13.5 Additivity [414] --
13.6 Transformations [417] --
13.7 The logarithmic transformation [419] --
13.8 The square root transformation [421] --
13.9 The Box-Cox transformation [423] --
13.10 The arcsine transformation [427] --
13.11 Nonparametric methods in lieu of single classification anova [429] --
13.12 Nonparametric methods in lieu of two-way anova [445] --
14. LINEAR REGRESSION [454] --
14.1 Introduction to regression [455] --
14.2 Models in regression [458] --
14.3 The linear regression equation [461] --
14.4 Tests of significance in regression [469] --
14.5 More than one value of Y for each value of X [477] --
14.6 The uses of regression [491] --
14.7 Estimation of X from Y [496] --
14.8 Comparison of regression lines [499] --
14.9 Analysis of covariance [509] --
14.10 Linear comparisons in anova [530] --
14.11 Examination of residuals and transformations in regression [539] --
14.12 Nonparametric tests for regression [546] --
14.13 Model II regression [547] --
15. CORRELATION [561] --
15.1 Correlation and regression [562] --
15.2 The product-moment correlation coefficient [565] --
15.3 The variance of sums and differences [573] --
15.4 Computation of the product-moment correlation coefficient [575] --
15.5 Significance tests in correlation [583] --
15.6 Applications of correlation [591] --
15.7 Principal axes and confidence regions [594] --
15.8 Nonparametric tests for association [601] --
16. MULTIPLE AND CURVILINEAR REGRESSION [617] --
16.1 Multiple regression: computations [618] --
16.2 Multiple regression: significance tests [631] --
16.3 Path analysis [642] --
16.4 Partial and multiple correlation [656] --
16.5 Choosing predictor variables [661] --
16.6 Curvilinear regression [671] --
16.7 Advanced topics in regression and correlation [683] --
17. ANALYSIS OF FREQUENCIES [691] --
17.1 Tests for goodness of fit: introduction [692] --
17.2 Single classification goodness of fit tests [704] --
17.3 Replicated tests of goodness of fit [721] --
17.4 Tests of independence: two-way tables [731] --
17.5 The analysis of three-way and multiway tables [747] --
17.6 Finding the sample size n required to test the difference between two percentages [765] --
17.7 Randomized blocks for frequency data [767] --
18. MISCELLANEOUS METHODS [779] --
18.1 Combining probabilities from tests of significance [779] --
18.2 Tests for randomness: runs tests [782] --
18.3 Randomization tests [787] --
18.4 The jackknife [795] --
18.5 The future of biometry: data analysis [799] --
APPENDIXES --
Al Mathematical appendix [806] --
A2 A package of statistical computer programs [822] --
BIBLIOGRAPHY [826] --
AUTHOR INDEX [839] --
SUBJECT INDEX [843] --
MR, REVIEW #
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