Download A Concise Guide to Statistics by Hans-Michael Kaltenbach PDF

By Hans-Michael Kaltenbach

The textual content offers a concise advent into primary techniques in data. bankruptcy 1: brief exposition of chance conception, utilizing wide-spread examples. bankruptcy 2: Estimation in idea and perform, utilizing biologically influenced examples. Maximum-likelihood estimation in lined, together with Fisher details and tool computations. tools for calculating self belief durations and powerful possible choices to straightforward estimators are given. bankruptcy three: speculation trying out with emphasis on options, really type-I , type-II error, and studying attempt effects. a number of examples are supplied. T-tests are used all through, vital different exams and robust/nonparametric possible choices. a number of trying out is mentioned in additional intensity, and mixture of autonomous exams is defined. bankruptcy four: Linear regression, with computations completely in keeping with R. a number of workforce comparisons with ANOVA are lined including linear contrasts, back utilizing R for computations.

Show description

Read Online or Download A Concise Guide to Statistics PDF

Similar biostatistics books

High-Yield Biostatistics (2nd Edition) (High-Yield Series)

A part of the winning High-Yield™ sequence, High-Yield™ Biostatistics, moment variation explains options, offers examples, and covers the full variety of biostatistics fabric that may be anticipated to seem at the USMLE Step 1. New to this version are references to evidence-based drugs, and data up-to-date to mirror alterations within the present USMLE examinations

Neurological disorders. Public health challenges

There's plentiful facts that pinpoints neurological issues as one of many maximum threats to public wellbeing and fitness. There are a number of gaps in figuring out the numerous matters concerning neurological issues, yet we already be aware of adequate approximately their nature and therapy which will form potent coverage responses to a few of the main widespread between them.

Knowledge Discovery in Bioinformatics: Techniques, Methods, and Applications

The aim of this edited e-book is to bring together the principles and findings of information mining researchers and bioinformaticians through discussing cutting-edge research topics such as, gene expressions, protein/RNA constitution prediction, phylogenetics, series and structural motifs, genomics and proteomics, gene findings, drug layout, RNAi and microRNA research, textual content mining in bioinformatics, modelling of biochemical pathways, biomedical ontologies, procedure biology and pathways, and organic database administration.

Extra resources for A Concise Guide to Statistics

Sample text

8 Visualizing Distributions 4 6 8 10 14 ● 4 Value ● 6 ● ● ● ● ● ● ● ● 8 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 ● ● ● ● ● ● ● ● ● 12 ● ● ● ● 14 ● 16 Value Fig. 9 Empirical (solid) and theoretical (dashed) density functions (left) and cumulative distribution functions (right) of 50 Norm(10, 6) sample points Hn (x) , Fˆn (x) = n where Hn (x) is the number of sample points smaller than x. This leads to a step function, which in the example quite closely follows the theoretical function.

Our goal is to infer one or more parameters θ of the distribution of the X i . For this, we construct an estimator θˆn by finding a function g, such that θˆn = g(X 1 , . . , X n ) is a “good guess” of the true value θ. Since θˆn depends on the data, it is a random variable. Finding its distribution allows us to compute confidence intervals that quantify how likely it is that the true value θ is close to the estimate θˆn . Example 10 Let us revisit the problem of sequence matching from Example 8 (p.

For k = 2 and θ = 2, the distribution has expectation μ = kθ = 4 and variance σ 2 = kθ 2 = 8; its density is shown in Fig. 8 (solid line). For comparison, a normal distribution with the same expectation and variance is plotted by a dashed line. As we can see, the density functions look very different, although both have the same mean and variance. For additionally capturing their different shapes, higher moments are needed (see Sect. 5). Example 8 Let us consider the following model of a random DNA sequence as introduced earlier: we assume independence among the nucleotides and in each position, the probabilities of having a particular nucleotide are p A , pC , pG , pT , respectively.

Download PDF sample

Rated 4.31 of 5 – based on 20 votes