- Contents
- Sheskin D.J. Handbook of Parametric and Nonparametric Statistical Procedures
- Nonparametric Statistics for Non‐Statisticians | Wiley Online Books
- A Comparative Study of Different Variants of Genetic Algorithms for Constrained Optimization
- Meaning of "nonparametric statistics" in the English dictionary

Nonparametric statistics for non-statisticians: a step-by-step approach / . explanation of how to perform the crucial step of checking your data for normality. Nonparametric Statistics for Non‐Statisticians: A Step‐by‐Step A practical and understandable approach to nonparametric statistics for. PDF | On Jan 1, , Alice Richardson and others published Nonparametric Statistics for Non‐Statisticians: A Step‐by‐Step Approach by.

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riastanufulthep.gq: Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach (): Gregory W. Corder, Dale I. Foreman: Books. " a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in. Get instant access to our step-by-step Nonparametric Statistics For Non- Statisticians A Step-by-Step Approach solutions manual. Our solution manuals are.

Any other family of distributions can be tested but the test for each family is implemented by using a different modification of the basic test statistic and this is referred to critical values specific to that family of distributions. The modifications of the statistic and tables of critical values are given by Stephens [2] for the exponential, extreme-value, Weibull, gamma, logistic, Cauchy, and von Mises distributions. Tests for the two-parameter log-normal distribution can be implemented by transforming the data using a logarithm and using the above test for normality. Details for the logistic distribution are given by Stephens A test for the two parameter Weibull distribution can be obtained by making use of the fact that the logarithm of a Weibull variate has a Gumbel distribution. Non-parametric k-sample tests[ edit ] Fritz Scholz and Michael A. Stephens discuss a test, based on the Anderson—Darling measure of agreement between distributions, for whether a number of random samples with possibly different sample sizes may have arisen from the same distribution, where this distribution is unspecified.

You can check your reasoning as you tackle a problem using our interactive solutions viewer. Plus, we regularly update and improve textbook solutions based on student ratings and feedback, so you can be sure you're getting the latest information available. Our interactive player makes it easy to find solutions to Nonparametric Statistics for Non-Statisticians A Step-by-Step Approach problems you're working on - just go to the chapter for your book.

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Bookmark it to easily review again before an exam. The best part? The tests are "exact", in the Monte-Carlo sense -- they can be made as accurate as desired by specifying enough random shuffles.

PCP Pattern Classification Program -- a machine-learning program for supervised classification of patterns vectors of measurements. Supports interactive keyboard-driven menus and batch processing. An augmented Windows version Aug. EXE - For comparisons of two independent groups or samples. The current version number is 3. EXE - For use in descriptive epidemiology including the appraisal of separate samples in comparative studies.

EXE - Miscellaneous randomization, random sampling, adjustment of multiple-test p-values, appraisal of synergism, assessment of a scale, correlation-coefficient tools, large contingency tables, three-way tables, median polish and mean polish, appraisal of effect of unmeasured confounders.

EXE - Multiple logistic regression. The current version number is 1. EXE - For appraisal of differences and agreement between matched samples or observations. EXE - Multiple Poisson regression. EXE - An expression evaluator with storage of constants, interim results, and formulae and calculator for p values and their inverse , confidence intervals, and time spans.

The current version number is 4. Provides sophisticated methods in a friendly interface. TETRAD is limited to models The TETRAD programs describe causal models in three distinct parts or stages: a picture, representing a directed graph specifying hypothetical causal relations among the variables; a specification of the family of probability distributions and kinds of parameters associated with the graphical model; and a specification of the numerical values of those parameters.

EasySample -- a tool for statistical sampling.

Supports several types of attribute and variable sampling and includes a random number generator and standard deviation calculator. Has a consistent, easy-to-use interface. EpiData -- a comprehensive yet simple tool for documented data entry. Overall frequency tables codebook and listing of data included, but no statistical analysis tools.

Calculate sample size required for a given confidence interval, or confidence interval for a given sample size. Can handle finite populations.

Online calculator also available. Grocer -- a free econometrics toolbox that runs under Scilab. It contains: most standard econometric capabilities: ordinary least squares, autocorelated models, instrumental variables, non linear least squares, limited dependent variables, robust methods, specification tests multicolinearity, autocorelation, heteroskedasticity, normality, predictive failure, It also contains some rare -and useful- features: a pc-gets device that performs automatic general to specific estimations, and a contributions device, that provides contributions of exogenous variables to an endogenous one for any dynamic equation.

Has a -rough- interface with Excel and unlike Gauss or Matlab, it deals with true timeseries objects. Deals with: preparing ecogeographical maps for use as input for ENFA e. Based on a new estimation method called Bound and Collapse. Developed within the Bayesian Knowledge Discovery project. See also the commercial product, called Bayesware Discoverer , available free for non-commercial use.

RoC: The Robust Bayesian Classi fier -- a computer program able to perform supervised Bayesian classification from incomplete databases, with no assumption about the pattern of missing data. Based on a new estimation method called Robust Bayesian Estimator. The program allows the user to repeatedly combine probabilities in series or in parallel, and at any time will show a trail of the calculations which led to the current probability value.

Other program capabilities are the calculation of probabilities from input data, Gaussian approximation, and the generation of a mean time between failure MTBF table for various levels of confidence. It is assumed that the user is familiar with the theory behind binomial probability distribution.

Graphical displays include an automatic collection of elementary graphics corresponding to groups of rows or to columns in the data table, automatic k-table graphics and geographical mapping options, searching, zooming, selection of points, and display of data values on factor maps.

Simple and homogeneous user interface. Weibull Trend Toolkit -- Fits a Weibull distribution function like a normal distribution, but more flexible to a set of data points by matching the skewness of the data.

Command-line interface versions available for major computer platform; a Windows version, WinBUGS, supports a graphical user interface, on-line monitoring and convergence diagnostics. Includes complete help files and sample networks. Bayesian Networks are encoded in an XML file format.

AMELIA -- A program for substituting reasonable values for missing data called "imputation" A collection of MS-DOS program from the Downloads section of the QuantitativeSkills web site: Hypergeometric -- calculates the hypergeometric probability distribution to evaluate hypothesis in relation to sampling without replacing in small populations Binomial -- calculates probabilities for sampling with replacing in small populations or without replacing in very large populations.

Can be used to approximate the hypergeometric distribution.

The binomial is probably the best known discrete distribution. Poisson -- calculates probabilities for samples which are very large in an even larger population. Is used to approximate the binomial distribution, try to compare it with the binomial! The distribution is more often used in a completely different way, for the analysis of how rare events, such as accidents, cumulate for a single individual.

For example, you can use it to estimate your chances of getting one, two, three or more accidents in any one year considering that on average people get 'U' accidents per year.

Negative binomial -- Also used to study accidents, is a more general case than the Poison, it considers that the probability of getting accidents if accidents clusters differently in subgroups of the population. However, the theoretical properties of this distribution and the possible relationship to real events are not well known.

Negative binomial -- Another version of the negative binomial, this one is used to do the marginal distribution of binomials try it! Often used to predict the termination of real time events. An example is the probability of terminating listening to a non-answering phone after n-rings. Multinomial -- Same as the multinomial above, this one for DOS computers.

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The sum of small p-values is the most used method, but there does not seem to be a good rationale for that. Use the fisher exact instead of the Chi-square when you have a small value in one cell or a very uneven marginal distribution.

SPRT -- This method of analysis is not often used, which is a pity because it is actually quite good. It is based on the case of phenomena being observed, tested, or data collected, sequentially in time. The testing or data collection is stopped as soon as some upper or lower limit is crossed of the proportion positive or negative events or outcomes relative to the total number observed.

Was originally developed to keep the costs of 'destructive' testing low. Is sometimes used in medical trials to monitor the amount of negative side effects and to decide if the trial should be stopped because the number of side effect is considered unacceptably high.

Chi-square -- Calculates the Chi-square and some other measures for two dimensional tables CASRO -- Calculates response rates according to different procedures. Data Preparator -- handles the "pre-processing" chores of getting a data file ready for analysis The free demo has all features enabled, and will handle up to cases. StatCalc day free trial download -- a handy desk-top tool and instructional aid that transforms from a standard calculator to a collection of modules that calculate statistics, graph distributions, and provide statistical help with definitions, formulas, and interpretation.

Windows WinSPC day free trial -- statistical process control software to: collect quality data from devices, shop-floor machines, data sources, other software systems, or via keyboard; monitor plant-wide operations from a single screen, and initiate corrective actions for out-of-control processes trigger alarm, send email, page an operator, or shut down an out-of-control machine ; perform statistical analysis to solve problems, optimize processes, and create quality reports.

The Unscrambler -- multivariate data analysis software for exploratory statistics, regression analysis, classification, prediction, principal components analysis PCA , partial least squares regression PLSR analysis and three-way PLS regression and experimental design.

Free day evaluation copy available. Handles traditional single fixed sample designs, survival analyses, proportions, means, non-inferiority, flexible adaptive designs, group-sequential designs,? Free day limited-function trial version available for download. Statistics Problem Solver -- tutoring software that not only solves statistical problems, but also generates step-by-step solutions in order to help students understand how to solve statistical problems.

Graphs can be customized in color, scale, resolution, etc. Also calculates slope, area under the curve, tracing and matrix transformation.

Calculus Problem Solver -- differentiates any arbitrary equation and outputs the result, providing detailed step-by-step solutions in a tutorial-like format. Can also initiate an interactive quiz in which you can solve differentiation while the computer corrects your solutions.

Includes equivalence- and non-inferiority testing for most tests, Monte Carlo simulation for small samples; group sequential interim analyses. Design-Ease and Design-Expert -- two programs from Stat-Ease that specialize in the design of experiments. Full-function day evaluation copies of both programs are available for download. AGREE -- to measure agreement of nominal data, where two or more judges classify objects into nominal scale categories.

Bayesware Discoverer -- a computer program able to learn Bayesian Belief Networks from possibly incomplete databases. This is a commercial product, available free for educational and other non-commercial use. ZeroRejects -- Implements the "Six Sigma" statistical process control methodology developed by Motorola.

The alpha and beta version are freely downloadable. Prognosis -- for analysis of time-series data. Uuses artificial intelligence and powerful statistical methodology to achieve high forecasting accuracy. Easy to use; does not require any background in statistics or time series analysis.

Free evaluation copy available for download. Incredibly powerful and multi-featured program for data manipulation and analysis.

Designed for econometrics, but useful in many other disciplines as well. Compumine Rule Discovery System -- easy to use data mining software for developing high-quality rule based prediction models, such as classification and regression trees, rule sets and ensemble models. This program is licensed under the P3 license model wich means that it is free to use forever for developing rule-based predictive models, and can be freely downloaded here.

Creates output modelss as LaTeX files, in tabular or equation format. Has an integrated scripting language: enter commands either via the gui or via script, command loop structure for Monte Carlo simulations and iterative estimation procedures, GUI controller for fine-tuning Gnuplot graphs, Link to GNU R for further data analysis. Includes a sample US macro database.

See also the gretl data page. Lets you create mathematical models, design and simulate experiments, and analyze data. Models can contain differential equations, which will be numerically integrated and fit to data.

Graphic and tabular output is provided. Includes normal fitting, Bayesian estimation, or simulation-only, with integrated or differential equation models.

Allows selection of weighting schemes and methods for numerical integration. Free downloads for Macintosh and Windows; online manual, tutorial, sample data sets. JoinPoint Regression Program from the National Cancer Institute -- for the analysis of trends using joinpoint models where several different lines are connected together at the "joinpoints.

Takes trend data e. Models may incorporate estimated variation for each point e. In addition, the models may also be linear on the log of the response e. The software also allows viewing one graph for each joinpoint model, from the model with the minimum number of joinpoints to the model with maximum number of joinpoints. CurveExpert -- comprehensive curve fitting system for Windows.

Handles linear regression models, nonlinear regression models, interpolation, or splines. Over 30 models built-in; custom user-defined regression models. Full-featured graphing capability. Supports an automated process that compares your data to each model to choose the best curve.

DTREG generates classification and regression decision trees. It uses V-fold cross-valication with pruning to generate the optimal size tree, and it uses surrogate splitters to handle missing data. A free demonstration copy is available for download. NLREG performs general nonlinear regression. NLREG will fit a general function, whose form you specify, to a set of data values.

The p-value can be thought of as the probability of observing the two data samples given the base assumption null hypothesis that the two samples were drawn from a population with the same distribution. The p-value can be interpreted in the context of a chosen significance level called alpha. If the p-value is below the significance level, then the test says there is enough evidence to reject the null hypothesis and that the samples were likely drawn from populations with differing distributions.

We will generate two samples drawn from different distributions. We will draw the samples from Gaussian distributions for simplicity, although, as noted, the tests we review in this tutorial are for data samples where we do not know or assume any specific distribution.

We will use the randn NumPy function to generate a sample of Gaussian random numbers in each sample with a mean of 0 and a standard deviation of 1. Observations in the first sample are scaled to have a mean of 50 and a standard deviation of 5.

Observations in the second sample are scaled to have a mean of 51 and a standard deviation of 5.