Four types of errors There are two kinds of errors discussed in classical statistics, unimaginatively named Type I and Type II. Aside from having completely non-mnemonic names, they represent odd concepts. Technically, a Type I error consists of rejecting the “null hypothesis” (roughly speaking, the assumption of no effect, the hypothesis you typically set out to disprove) in favor of the “alternative hypothesis” when in fact the null hypothesis is true. A Type II error consists of accepting the null hypothesis (technically, failing to reject the null hypothesis) when in fact the null hypothesis is false. Suppose you’re comparing two treatments with probabilities of efficacy θj and θk. I’ve never made a Type 1 error in my life. (Emphasis added.) Instead of Type I and Type II errors, Gelman proposes we concentrate on “Type S” errors (sign errors, concluding θj > θk when in fact θj < θk) and “Type M” errors (magnitude errors, concluding that an effect is larger than it truly is.)
Circos - Tutorials The tutorials serve as a walkthrough through Circos. The course is a more structured set of materials that takes you through creating an image from scratch. The tutorials act as documentation — each lesson presents a specific feature of Circos. Example Image Once you download and install Circos, > tar xvfz circos-x.xx.tgz > cd circos-x.xx try creating the example image that ships with the Circos core distribution. > cd example > . Creating a Tutorial Image You will need to download the tutorials separately. > cd tutorials/2/2 > ../../.. In each tutorial directory (e.g. tutorials/2/2), there will be several configuration files (*.conf). Creating Your Own Image The first thing you will need is the karyotype for your genome. You can download the karyotype from the table browser or directly for human hg19 (Feb 2009)hg18 (Mar 2006), mouse mm9 (Jul 2009)mm8 (Mar 2006), rat rn4 (Nov 2004)rn3 (Jun 2003), or other species.
jtleek/dataanalysis Using R for statistical analyses - Non-parametric statistics Studentized Range - Q The studentized range statistic is commonly used in post-hoc analyses. The distribution function is built-in to R and we may access it in one of two ways. ptukey(q, nmeans, df) qtukey(p, nmeans, df) In the first case we input a confidence level and get the corresponding Q value. With respect to our Kruskal-Wallis post-hoc test the easiest way to proceed would be to calculate the value of Q that results when using the U value calculated by the pairwize U-test (using the formula as shown above). Next enter the values into the ptukey() command using: q= the value of Q you just found. nmeans= the number of samples in the original K-W test (e.g. 6 for our carbs data set) df= Inf The result is the Confidence Interval not a p-value. An alternative method would be to work out the critical value of Q first of all. qtukey(CI, nmeans, df= Inf) Then we would calculate the critical value of U using the equation as shown above. Which U-value? However, there is a potential problem.
Interactive Statistical Calculation Pages Chart of distribution relationships Probability distributions have a surprising number inter-connections. A dashed line in the chart below indicates an approximate (limit) relationship between two distribution families. A solid line indicates an exact relationship: special case, sum, or transformation. Click on a distribution for the parameterization of that distribution. Follow @ProbFact on Twitter to get one probability fact per day, such as the relationships on this diagram. More mathematical diagrams The chart above is adapted from the chart originally published by Lawrence Leemis in 1986 (Relationships Among Common Univariate Distributions, American Statistician 40:143-146.) Parameterizations The precise relationships between distributions depend on parameterization. Let C(n, k) denote the binomial coefficient(n, k) and B(a, b) = Γ(a) Γ(b) / Γ(a + b). Geometric: f(x) = p (1-p)x for non-negative integers x. Discrete uniform: f(x) = 1/n for x = 1, 2, ..., n. Poisson: f(x) = exp(-λ) λx/ x! Uniform: f(x) = 1 for 0 ≤ x ≤ 1. jdc
Papers - 1.9.4 [Intel] Papers 1.9.4 [Intel] | MacOS | 34.60 MB Papers · your personal library of research: Repository of knowledge, Search article repositories and download articles without leaving Papers, All your papers at a glance, Papers lets you view, browse and search your library, iTunes style. Smart groups. Features: * Import: Simply drag and drop your PDFs to import them into Papers, or import from Endnote or a BibTeX file. * Searching: Search through the world's largest article repositories direct from within Papers. * Collections: Organize your library by topic, project or anything else, with help of manual and smart collections Link for more information: DownloadHotfileFileServe
Highland Statistics Ltd Choosing the Correct Statistical Test in SAS, Stata and SPSS The following table shows general guidelines for choosing a statistical analysis. We emphasize that these are general guidelines and should not be construed as hard and fast rules. Usually your data could be analyzed in multiple ways, each of which could yield legitimate answers. The table below covers a number of common analyses and helps you choose among them based on the number of dependent variables (sometimes referred to as outcome variables), the nature of your independent variables (sometimes referred to as predictors). You also want to consider the nature of your dependent variable, namely whether it is an interval variable, ordinal or categorical variable, and whether it is (approximately) normally distributed (see What is the difference between categorical, ordinal and interval variables? This page was adapted from Choosing the Correct Statistic developed by James D.