BiometrySome Definitions. The development of biometry. The statistical frame of mind. Data in biology. Samples and populations. Variables in biology. Accuracy and precision of data. Derived variables. Frequency distribuitions. The handling of data. Computers. Software. Efficiency and economy in data processing. Descriptive statistics. The arithmetic mean. Other means. The median. The mode. The range. The standard deviation.Sample statistics and parametrs. Coding data before computation. Computing means and standard deviations. The coefficient of variation. Introduction to probability distribution: Binomial and poison. probability, random sampling, and hypothesis testing. the binomial distribuition. The poisson distribuition. Other discrete probability distributions. The normal probability distribuition. Estimation and hypothesis testing. introduction to the analysis of variance. Single-classification analysis of variance. Nested analysis of variance. Two-way analysis of variance. Multiway analysis of variance. Assumptions of analysis of variance. A fundamental assumption. Independence. Homogeneity of variances. Normality. Additivity. Transformations. The logarithmic, the square-root, the box-cox and the arcsine transformation. Nonparametric methods in lieu of single-classification anovas and two-way anova. Linear regression. Correlation. Multiple and curvilinear regression. Analysis of frequencies. Miscellaneous methods. Mathematical proofs. |
From inside the book
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Page ix
... Anova : Design 272 10.2 Nested Anova : Computation 275 10.3 Nested Anovas With Unequal Sample Sizes 292 10.4 The Optimal Allocation of Resources 309 II TWO - WAY ANALYSIS OF VARIANCE 321 11.1 Two - Way Anova : Design . 321 11.2 Two - Way ...
... Anova : Design 272 10.2 Nested Anova : Computation 275 10.3 Nested Anovas With Unequal Sample Sizes 292 10.4 The Optimal Allocation of Resources 309 II TWO - WAY ANALYSIS OF VARIANCE 321 11.1 Two - Way Anova : Design . 321 11.2 Two - Way ...
Page x
... Two - Way Anova 440 14 LINEAR REGRESSION 451 14.1 Introduction to Regression 452 14.2 Models in Regression ... 455 14.3 The Linear Regression Equation 457 14.4 Tests of Significance in Regression 466 14.5 More Than One Value of Y for ...
... Two - Way Anova 440 14 LINEAR REGRESSION 451 14.1 Introduction to Regression 452 14.2 Models in Regression ... 455 14.3 The Linear Regression Equation 457 14.4 Tests of Significance in Regression 466 14.5 More Than One Value of Y for ...
Page 196
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Contents
INTRODUCTION | 1 |
DATA IN BIOLOGY | 8 |
THE HANDLING OF DATA | 33 |
34 | 50 |
39 | 56 |
3 | 98 |
5 | 111 |
ESTIMATION AND HYPOTHESIS TESTING | 128 |
LINEAR REGRESSION | 451 |
INTRODUCTION TO THE ANALYSIS | 452 |
1 | 535 |
2 | 554 |
CORRELATION | 555 |
MULTIPLE AND CURVILINEAR REGRESSION | 609 |
ANALYSIS OF FREQUENCIES | 685 |
MISCELLANEOUS METHODS | 794 |
9 | 169 |
SINGLECLASSIFICATION ANALYSIS | 207 |
NESTED ANALYSIS OF VARIANCE | 272 |
TWOWAY ANALYSIS OF VARIANCE | 321 |
MULTIWAY ANALYSIS OF VARIANCE | 369 |
ASSUMPTIONS OF ANALYSIS OF VARIANCE | 392 |
3 | 822 |
MATHEMATICAL PROOFS | 833 |
BIBLIOGRAPHY | 850 |
865 | |
871 | |
Common terms and phrases
alternative hypothesis analysis of variance approximate average binomial calculate column comparisons computed confidence limits correlation coefficient critical value degrees of freedom density equal sample sizes equation estimate example expected frequencies expected mean squares experiment experimentwise error rate Expression factor females Figure formula frequency distribution genetic given groups housefly independent variables individual interaction linear regression magnitude measure method Model I anova nested anova normal distribution null hypothesis obtain parametric pea sections population probability procedure proportion quantity random ranks ratio rats regression line replicates represent sample mean shown in Box significance test single-classification Source of variation species standard deviation standard error Statistical Table strains subgr subgroups sugars sum of squares t-test tion transformation treatment effects two-way anova type I error variation df SS wing length X₁ Y₁ Y₂ yield zero Σ Σ σ²