Modern Statistics for the Life Sciences

ISBN-10: 0199252319
ISBN-13: 9780199252312
Edition: 2002
List price: $85.95 Buy it from $58.65
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Description: This textbook teaches statistics in a different way. It is aimed at undergraduate students in the life sciences, and it will also be invaluable for many graduate students. It makes the powerful methods of model formulae and the General Linear Model  More...

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Book details

List price: $85.95
Copyright year: 2002
Publisher: Oxford University Press, Incorporated
Publication date: 5/9/2002
Binding: Paperback
Pages: 368
Size: 6.75" wide x 9.75" long x 1.00" tall
Weight: 1.628
Language: English

This textbook teaches statistics in a different way. It is aimed at undergraduate students in the life sciences, and it will also be invaluable for many graduate students. It makes the powerful methods of model formulae and the General Linear Model accessible to undergraduates for the first time. The computer revolution has finally made it possible to teach life sciences undergraduates how to use the statistics they really need to know - this book provides the course materials needed to fulfil that possibility. This text presents the fundamental statistical concepts without being tied to any one statistical package. Three supplements available on the web site provide all the information you need to conduct the analyses in either Minitab, SAS, or SPSS. All datasets are available on the web site.

Why use this book
How to use this book
How to teach this text
An introduction to analysis of variance
Model formulae and geometrical pictures
General Linear Models
The basic principles of ANOVA
An example of ANOVA
The geometrical approach for an ANOVA
Regression
What kind of data are suitable for regression?
How is the best fit line chosen?
The geometrical view of regression
Regression--an example
Confidence and prediction intervals
Conclusions from a regression analysis
Unusual observations
The role of X and Y--does it matter which is which?
Models, parameters and GLMs
Populations and parameters
Expressing all models as linear equations
Turning the tables and creating datasets
Using more than one explanatory variable
Why use more than one explanatory variable?
Elimination by considering residuals
Two types of sum of squares
Urban Foxes--an example of statistical elimination
Statistical elimination by geometrical analogy
Designing experiments--keeping it simple
Three fundamental principles of experimental design
The geometrical analogy for blocking
The concept of orthogonality
Combining continuous and categorical variables
Reprise of models fitted so far
Combining continuous and categorical variables
Orthogonality in the context of continuous and categorical variables
Treating variables as continuous or categorical
The general nature of General Linear Models
Interactions--getting more complex
The factorial principle
Analysis of factorial experiments
What do we mean by an interaction?
Presenting the results
Extending the concept of interactions to continuous variables
Uses of interactions
Checking the models I: independence
Heterogeneous data
Repeated measures
Nested data
Detecting non-independence
Checking the models II: the other three asumptions
Homogeneity of variance
Normality of error
Linearity/additivity
Model criticism and solutions
Predicting the volume of merchantable wood: an example of model criticism
Selecting a transformation
Model selection I: principles of model choice and designed experiments
The problem of model choice
Three principles of model choice
Four different types of model choice problem
Orthogonal and near orthogonal designed experiments
Looking for trends across levels of a categorical variable
Model selection II: datasets with several explanatory variables
Economy of variables in the context of multiple regression
Multiplicity of p-values in the context of multiple regression
Automated model selection procedures
Whale Watching: using the GLM approach
Random effects
What are random effects?
Four new concepts to deal with random effects
A one-way ANOVA with a random factor
A two-level nested ANOVA
Mixing random and fixed effects
Using mock analyses to plan an experiment
Categorical data
Categorical data: the basics
The Poisson distribution
The chi-squared test in contingency tables
General linear models and categorical data
What lies beyond?
Generalised Linear Models
Multiple y variables, repeated measures and within-subject factors
Conclusion
Answers to exercises
Revision section: The basics
Populations and samples
Three types of variability: of the sample, the population and the estimate
Confidence intervals: a way of precisely representing uncertainty
The null hypothesis--taking the conservative approach
Comparing two means
Conclusion
The meaning of p-values and confidence intervals
What is a p-value?
What is a confidence interval?
Analytical results about variances of sample means
Introducing the basic notation
Using the notation to define the variance of a sample
Using the notation to define the mean of a sample
Defining the variance of the sample mean
To illustrate why the sample variance must be calculated with n - 1 in its denominator (rather than n) to be an unbiased estimate of the population variance
Probability distributions
Some gentle theory
Confirming simulations
Bibliography
Index

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