Statistics Book
Many scientists and students are asking for advice in statistical analyses during preparation of their publications or theses. This book aims to address these needs by representing a “recipe book” for statistical analysis in biological sciences. More importantly, limited knowledge in statistical thinking affects both the quality of the experimental design and the outcome of research projects. The content of this book will enable everyone to perform high-quality statistical analyses and to produce publication-quality graphics with no additional costs using R. R is at least as good (and often better) as many expensive statistical software solutions for statistical computing. It runs on all important platforms and provides a wide array of specialized modules and utilities for diverse needs.
To use this book the reader does not need to be an expert in statistics. Just copy and paste the syntax of commands and apply them on your own specific data set. The secret in understanding statistics is to perform multiple analyses on diverse data sets to get a better knowledge of the different models.
What’s inside?
You will find a book introducing into scientific researching with practical step by step examples in data analysis and in visualizing data using R.
Content:
Introduction into scientific working and why we need statistics
Using R as tool for statistical analysis
How can we perform a proper statistical analysis of a biological data set in an easy way?
First: What is your scientific question?
Second: Draw a hypothesis that you can test!
Third: Get your data!
Forth: Examine your data!
Fifth: Summarize and plot your data!
Sixth: Build a statistical model!
Student’s T-test (t.test)
Correlation test (cor.test)
ANOVA (aov) and linear regression model (lm)
Generalized Linear Models (glm)
Linear Mixed Effect Models (lme)
Survival analysis (coxph)
Predictions with statistical models