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

By Hans-Michael Kaltenbach

The textual content supplies a concise advent into basic recommendations in information. bankruptcy 1: brief exposition of likelihood idea, utilizing frequent examples. bankruptcy 2: Estimation in idea and perform, utilizing biologically influenced examples. Maximum-likelihood estimation in coated, together with Fisher info and gear computations. equipment for calculating self assurance durations and powerful choices to plain estimators are given. bankruptcy three: speculation trying out with emphasis on techniques, fairly type-I , type-II blunders, and studying attempt effects. numerous examples are supplied. T-tests are used all through, vital different assessments and robust/nonparametric choices. a number of checking out is mentioned in additional intensity, and mixture of self sustaining exams is defined. bankruptcy four: Linear regression, with computations completely in keeping with R. a number of team comparisons with ANOVA are lined including linear contrasts, back utilizing R for computations.

Show description

Read or Download A Concise Guide to Statistics PDF

Best biostatistics books

Controversial Statistical Issues in Clinical Trials (Chapman & Hall CRC Biostatistics Series)

In medical trial perform, arguable statistical concerns unavoidably take place whatever the compliance with stable statistical perform and solid medical perform. yet by means of settling on the motives of the problems and correcting them, the learn pursuits of medical trials will be higher accomplished. arguable Statistical concerns in scientific Trials covers regularly encountered debatable statistical concerns in scientific trials and, every time attainable, makes ideas to unravel those difficulties.

Integrative Problem-Solving in a Time of Decadence

This ebook provides a different learn of Integrative Problem-Solving (IPS) in the course of the present section of 'Decadence' that characterizes each societal point (science, schooling, politics, economics, and culture). it's distinct in that it integrates subtle arithmetic and clinical process with philosophical pondering and sociological research.

Medical Statistics from Scratch: An Introduction for Health Professionals

Effectively realizing and utilizing scientific data is a key ability for all clinical scholars and well-being pros. In a casual and pleasant sort, scientific facts from Scratch presents a pragmatic origin for everybody whose first curiosity may not be scientific information. preserving the extent of arithmetic to a minimal, it basically illustrates statistical recommendations and perform with various genuine global examples and circumstances drawn from present clinical literature.

Practical Statistics for Field Biology

Presents a very good introductory textual content for college students at the ideas and strategies of statistical research within the lifestyles sciences, aiding them select and examine statistical exams for his or her personal difficulties and current their findings. An realizing of statistical ideas and strategies is key for any scientist yet is very very important for these within the existence sciences.

Additional resources for A Concise Guide to Statistics

Example text

The name “bootstrap” refers to the seemingly impossible task to lift ourselves out of the unknown variance problem by using the straps of our own boots, namely the data we have. The algorithm. We can write the general bootstrap procedure for estimating the variance in a more algorithmic form as • • • • Draw X 1 , . . , X n uniformly with replacement from {x1 , . . , xn }. Compute θˆn,i = g(X 1 , . . , X n ) from this bootstrap sample. Repeat the two steps b times to get the estimates θˆn,1 , .

1 The total number of matches in two random sequences of length n is given by M := M1 + · · · + Mn and follows a binomial distribution: M ∼ Binom(n, p). Applying the linearity of the expectation and some algebra, we compute the expected number of matches: 1 As a word of caution for the biological audience: this argument does not hold for aligned sequences, as the alignment maximizes the number of matches, and this maximum has a different distribution. 20 1 Basics of Probability Theory ∞ E(M) = kP(M = k) k=−∞ n = k n k np (n − 1)!

He cried impatiently. 1 Introduction We assume that n independent and identically distributed random samples X 1 , . . , X n are drawn, whose realizations form an observation x1 , . . , xn . 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 .

Download PDF sample

Rated 4.01 of 5 – based on 24 votes