Statistics with Applications in Biology and Geology
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A01=Jorgen Granfeldt
A01=Preben Blaesild
AA Aa
AA Aa Aa
Author_Jorgen Granfeldt
Author_Preben Blaesild
binomial models
Bivariate Normal Distribution
Category=PBT
Category=PS
Class Variable
Data Set
Deletion Residuals
Distribution Function
eq_bestseller
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
eq_non-fiction
eq_science
Fehmarn Belt
Generalize Linear Model
Genotype AA
Hardy Weinberg Proportions
Hotelling's T2
likelihood estimation
Linear Normal Model
Linear Regression
Logistic Dose Response Model
Model M2
Model Statement
nonparametric hypothesis tests
parametric statistical methods
Poisson regression
Proc CORR
PROC GENMOD
PROC GENMOD DATA
Proc GLM
Proc Sort Data
Proportional Parameters
Root MSE
SAS programming techniques
Source DF Type
statistical modeling for life sciences
Product details
- ISBN 9781138469815
- Weight: 1156g
- Dimensions: 178 x 254mm
- Publication Date: 11 Sep 2017
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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The use of statistics is fundamental to many endeavors in biology and geology. For students and professionals in these fields, there is no better way to build a statistical background than to present the concepts and techniques in a context relevant to their interests. Statistics with Applications in Biology and Geology provides a practical introduction to using fundamental parametric statistical models frequently applied to data analysis in biology and geology.
Based on material developed for an introductory statistics course and classroom tested for nearly 10 years, this treatment establishes a firm basis in models, the likelihood method, and numeracy. The models addressed include one sample, two samples, one- and two-way analysis of variance, and linear regression for normal data and similar models for binomial, multinomial, and Poisson data. Building on the familiarity developed with those models, the generalized linear models are introduced, making it possible for readers to handle fairly complicated models for both continuous and discrete data. Models for directional data are treated as well. The emphasis is on parametric models, but the book also includes a chapter on the most important nonparametric tests.
This presentation incorporates the use of the SAS statistical software package, which authors use to illustrate all of the statistical tools described. However, to reinforce understanding of the basic concepts, calculations for the simplest models are also worked through by hand. SAS programs and the data used in the examples and exercises are available on the Internet.
Preben Blaesild, Jorgen Granfeldt
