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Biostatistics for Animal Science

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ISBN-10: 0851998208

ISBN-13: 9780851998206

Edition: 2004

Authors: Miroslav Kaps, William R. Lamberson

List price: $70.00
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Designed to cover techniques for analysis of data in the animal sciences, this book provides a complete source of information for students and researchers with a basic statistical background. The first part of the book provides an overview of the basic principles of statistics. The second half covers more complex applications and detailed procedures for analyzing designs commonly used in research in animal sciences.
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Book details

List price: $70.00
Copyright year: 2004
Publisher: Oxford University Press, Incorporated
Publication date: 9/16/2004
Binding: Paperback
Pages: 460
Size: 6.75" wide x 9.50" long x 1.10" tall
Weight: 2.134
Language: English

Preface
Presenting and Summarizing Data
Data and Variables
Graphical Presentation of Qualitative Data
Graphical Presentation of Quantitative Data
Construction of a Histogram
Numerical Methods for Presenting Data
Symbolic Notation
Measures of Central Tendency
Measures of Variability
Measures of the Shape of a Distribution
Measures of Relative Position
Sas Example
Exercises
Probability
Rules About Probabilities of Simple Events
Counting Rules
Multiplicative Rule
Permutations
Combinations
Partition Rule
Tree Diagram
Compound Events
Bayes Theorem
Exercises
Random Variables and Their Distributions
Expectations and Variances of Random Variables
Probability Distributions for Discrete Random Variables
Expectation and Variance of a Discrete Random Variable
Bernoulli Distribution
Binomial Distribution
Hyper-geometric Distribution
Poisson Distribution
Multinomial Distribution
Probability Distributions for Continuous Random Variables
Uniform Distribution
Normal Distribution
Multivariate Normal Distribution
Chi-square Distribution
Student t Distribution
F Distribution
Exercises
Population and Sample
Functions of Random Variables and Sampling Distributions
Central Limit Theorem
Statistics with Distributions Other than Normal
Degrees of Freedom
Estimation of Parameters
Point Estimation
Maximum Likelihood Estimation
Interval Estimation
Estimation of Parameters of a Normal Population
Maximum Likelihood Estimation
Interval Estimation of the Mean
Interval Estimation of the Variance
Exercises
Hypothesis Testing
Hypothesis Test of a Population Mean
P value
A Hypothesis Test Can Be One- or Two-sided
Hypothesis Test of a Population Mean for a Small Sample
Hypothesis Test of the Difference Between Two Population Means
Large Samples
Small Samples and Equal Variances
Small Samples and Unequal Variances
Dependent Samples
Nonparametric Test
SAS Examples for Hypotheses Tests of Two Population Means
Hypothesis Test of a Population Proportion
Hypothesis Test of the Difference Between Proportions from Two Populations
Chi-Square Test of the Difference Between Observed and Expected Frequencies
SAS Example for Testing the Difference between Observed and Expected Frequencies
Hypothesis Test of Differences Among Proportions from Several Populations
SAS Example for Testing Differences among Proportions from Several Populations
Hypothesis Test of Population Variance
Hypothesis Test of the Difference of Two Population Variances
Hypothesis Tests Using Confidence Intervals
Statistical and Practical Significance
Types of Errors in Inferences and Power of Test
SAS Examples for the Power of Test
Sample Size
SAS Examples for Sample Size
Exercises
Simple Linear Regression
The Simple Regression Model
Estimation of the Regression Parameters - Least Squares Estimation
Maximum Likelihood Estimation
Residuals and Their Properties
Expectations and Variances of the Parameter Estimators
Student T Test in Testing Hypotheses About the Parameters
Confidence Intervals of the Parameters
Mean and Prediction Confidence Intervals of the Response Variable
Partitioning Total Variability
Relationships among Sums of Squares
Theoretical Distribution of Sum of Squares
Test of Hypotheses - F Test
Likelihood Ratio Test
Coefficient of Determination
Shortcut Calculation of Sums of Squares and the Coefficient of Determination
Matrix Approach to Simple Linear Regression
The Simple Regression Model
Estimation of Parameters
Maximum Likelihood Estimation
SAS Example for Simple Linear Regression
Power of Tests
SAS Examples for Calculating the Power of Test
Exercises
Correlation
Estimation of the Coefficient of Correlation and Tests of Hypotheses
Numerical Relationship Between the Sample Coefficient of Correlation and the Coefficient of Determination
SAS Example for Correlation
Rank Correlation
SAS Example for Rank Correlation
Exercises
Multiple Linear Regression
Two Independent Variables
Estimation of Parameters
Student t test in Testing Hypotheses
Partitioning Total Variability and Tests of Hypotheses
Partial and Sequential Sums of Squares
Testing Model Fit Using a Likelihood Ratio Test
SAS Example for Multiple Regression
Power of Multiple Regression
SAS Example for Calculating Power
Problems with Regression
Analysis of Residuals
Extreme Observations
Multicollinearity
SAS Example for Detecting Problems with Regression
Choosing the Best Model
SAS Example for Model Selection
Curvilinear Regression
Polynomial Regression
SAS Example for Quadratic Regression
Nonlinear Regression
SAS Example for Nonlinear Regression
Segmented Regression
SAS Examples for Segmented Regression
SAS Example for Segmented Regression with Two Simple Regressions
SAS Example for Segmented Regression with Plateau
One-Way Analysis of Variance
The Fixed Effects One-Way Model
Partitioning Total Variability
Hypothesis Test - F Test
Estimation of Group Means
Maximum Likelihood Estimation
Likelihood Ratio Test
Multiple Comparisons among Group Means
Least Significance Difference (LSD)
Tukey Test
Contrasts
Orthogonal contrasts
Scheffe Test
Test of Homogeneity of Variance
SAS Example for the Fixed Effects One-way Model
Power of the Fixed Effects One-way Model
SAS Example for Calculating Power
The Random Effects One-Way Model
Hypothesis Test
Prediction of Group Means
Variance Component Estimation
Intraclass Correlation
Maximum Likelihood Estimation
Restricted Maximum Likelihood Estimation
SAS Example for the Random Effects One-way Model
Matrix Approach to the One-Way Analysis of Variance Model
The Fixed Effects Model
Linear Model
Estimating Parameters
Maximum Likelihood Estimation
Regression Model for the One-way Analysis of Variance
The Random Effects Model
Linear Model
Prediction of Random Effects
Maximum Likelihood Estimation
Restricted Maximum Likelihood Estimation
Mixed Models
Prediction of Random Effects
Maximum Likelihood Estimation
Restricted Maximum Likelihood Estimation
Exercises
Concepts of Experimental Design
Experimental Units and Replications
Experimental Error
Precision of Experimental Design
Controlling Experimental Error
Required Number of Replications
SAS Example for the Number of Replications
Blocking
Randomized Complete Block Design
Partitioning Total Variability
Hypotheses Test - F test
SAS Example for Block Design
Randomized Block Design - Two or More Units Per Treatment and Block
Partitioning Total Variability and Test of Hypotheses
SAS Example for Two or More Experimental Unit per Block x Treatment
Power of Test
SAS Example for Calculating Power
Exercises
Change-over Designs
Simple Change-Over Design
Change-Over Designs with the Effects of Periods
SAS Example for Change-over Designs with the Effects of Periods
Latin Square
SAS Example for Latin Square
Change-over Design Set as Several Latin Squares
SAS Example for Several Latin Squares
Exercises
Factorial Experiments
The Two Factor Factorial Experiment
SAS Example for Factorial Experiment
Exercise
Hierarchical or Nested Design
Hierarchical Design with Two Factors
SAS Example for Hierarchical Design
More About Blocking
Blocking with Pens, Corrals and Paddocks
SAS Example for Designs with Pens and Paddocks
Double Blocking
Split-Plot Design
Split-Plot Design - Main Plots in Randomized Blocks
SAS Example: Main Plots in Randomized Blocks
Split-Plot Design - Main Plots in a Completely Randomized Design
SAS Example: Main Plots in a Completely Randomized Design
Exercise
Analysis of Covariance
Completely Randomized Design with a Covariate
SAS Example for a Completely Randomized Design with a Covariate
Testing the Difference Between Regression Slopes
SAS Example for Testing the Difference between Regression Slopes
Repeated Measures
Homogeneous Variances and Covariances Among Repeated Measures
SAS Example for Homogeneous Variances and Covariances
Heterogeneous Variances and Covariances Among Repeated Measures
SAS Examples for Heterogeneous Variances and Covariances
Random Coefficient Regression
SAS Examples for Random Coefficient Regression
Homogeneous Variance-Covariance Parameters across Treatments
Heterogeneous Variance-Covariance Parameters across Treatments
Analysis of Numerical Treatment Levels
Lack of Fit
SAS Example for Lack of Fit
Polynomial Orthogonal Contrasts
SAS Example for Polynomial Contrasts
Discrete Dependent Variables
Logit Models, Logistic Regression
Testing Hypotheses
SAS Examples for Logistic Models
Probit Model
SAS Example for a Probit model
Log-Linear Models
SAS Example for a Log-Linear Model
Solutions of Exercises
Vectors and Matrices
Types and Properties of Matrices
Matrix and Vector Operations
Statistical Tables
Area Under the Standard Normal Curve, Z [greater than sign] Z[subscript alpha]
Critical Values of Student T Distributions, T [greater than sign] T[subscript alpha]
Critical Values of Chi-Square Distributions, x[superscript 2] [greater than sign] x[superscript 2 subscript alpha]
Critical Values of F Distributions, F [greater than sign] F[subscript alpha], [alpha] = 0.05
Critical Value of F Distributions, F [greater than sign] F[subscript alpha], [alpha] = 0.01
Critical Values of the Studentized Range, Q(A,V)
References
Subject Index
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