## Biostatistical Design and Analysis Using R: A Practical GuideR — the statistical and graphical environment is rapidly emerging as an important set of teaching and research tools for biologists. This book draws upon the popularity and free availability of R to couple the theory and practice of biostatistics into a single treatment, so as to provide a textbook for biologists learning statistics, R, or both. An abridged description of biostatistical principles and analysis sequence keys are combined together with worked examples of the practical use of R into a complete practical guide to designing and analyzing real biological research.
- simple hypothesis testing, graphing
- exploratory data analysis and graphical summaries
- regression (linear, multi and non-linear)
- simple and complex ANOVA and ANCOVA designs (including nested, factorial, blocking, spit-plot and repeated measures)
- frequency analysis and generalized linear models.
Linear mixed effects modeling is also incorporated extensively throughout as an alternative to traditional modeling techniques. The book is accompanied by a companion website |

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Biostatistical Design and Analysis Using R: A Practical Guide Dr Murray Logan No preview available - 2010 |

Biostatistical Design and Analysis Using R: A Practical Guide Dr Murray Logan No preview available - 2010 |

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alternative Analysis of Variance appropriate argument assumptions BIOFILM block factor boxplots calculated codes coefficients Conclusions contrasts correlation covariate data frame dataset defined degrees of freedom density Deviance Df Sum Sq example F-ratio factorial ANOVA fitted model fixed factor frequencies graphics device HABITAT header I T homogeneity of variance hypothesis tests Import section 2.3 interaction Intercept library(biology linear mixed effects linear model linear regression log-linear modelling matrix Mean Sq F mixed effects models model fit multiple multiple comparisons normally distributed null hypothesis object observations odds ratios overall p-value p—value package parameter estimates polynomial predictor variables quadratic Quinn and Keough random factor repeated measures replicates represent response variable scale scatterplot Signif slope specific Sq F value Sq Mean Sq statistical Step 3 Key subset Sum Sq Mean sums of squares t-test transformed treatment trends unbalanced value Pr(>F