Biomeasurement provides an introduction to the use of statistics in the biosciences, emphasizing why statistical tools are essential tools for bioscientists. It begins by placing data analysis in the context of the wider scientific method and introducing key statistical concepts. It discusses inferring and estimating before showing how to choose the right test and graph and introducing null hypothesis significance testing. The following chapters focus on a variety of test types, including tests of difference and tests of relationship, such as regression and correlation. The book then introduces the generalized linear model, including logistic and loglinear models.
Book
Martin B Reed
Core Maths for the Biosciences consists of two parts. Part 1 looks atconsiders arithmetic, algebra, and functions. Here, chapters cover precision and accuracy, data tables, graphs, molarity and dilutions, variables, functions, equations, and linear functions. They also look at quadratic and polynomial functions, fitting curves, periodic functions, and exponential and logarithmic functions. Part 2 looks atfocusses on calculus and differential equations. It Chapters examines instantaneous rate-of-change, the rules of differentiation, applications of differentiation, techniques of integration, and the definite integral.
Book
Andrew D. Blann
Data Handling starts off with an analysis of information in the biomedical sciences. It then considers handling quantities which encompasses mass, volume, and concentration. It moves on to obtaining and verifying data. Next, it looks at presenting data in graphic form. Another chapter considers quality, audit, and good laboratory practice. The next three chapters are about research, setting the scene, the analysis of modest data sets, and large data sets. Finally, the text ends with an examination of communication methods.
Book
Nick Colegrave and Graeme D. Ruxton
Power Analysis starts by asking: what is statistical power and why is low power undesirable? It then moves on to considering ways in which we can improve the power of an experiment. It asks how we can quantify power by simulation. It also examines simple factorial designs and extensions to other designs. Next, it asks how we can deal with multiple hypotheses. Finally, it looks at how to apply the simulation approach presented in this book beyond null hypothesis testing.
Book
Graham Currell
Scientific Data Analysis consists of two parts. Part I covers how to understand the statistics in scientific data analysis. It looks at statistical concepts, regression analysis, hypothesis testing, and comparing data. Part II looks at analysing experimental data. Within this part, the topics of project data analysis, single response variables, related variables, frequency data, and multiple variables are examined.
