FactoMineR: Exploratory Multivariate Data Analysis with R. Factoextra R Package: Easy Multivariate Data Analyses and Elegant Visualization. Factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including: There are a number of R packages implementing principal component methods.

These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition. However, the result is presented differently according to the used packages. Extract and Visualize the Results of Multivariate Data Analyses. Factoextra is an R package making easy to extract and visualize the output of exploratory multivariate data analyses, including: There are a number of R packages implementing principal component methods.

These packages include: FactoMineR, ade4, stats, ca, MASS and ExPosition. However, the result is presented differently according to the used packages. Statistical forecasting: notes on regression and time series analysis. Robert Nau Fuqua School of Business Duke University.

Universidad de Granada. Este proyecto está subvencionado por el Programa de Innovación docente y buenas prácticas docentes, de la Universidad de Granada.

Documento sin título. Estad stica: "Introducci n a la Probabilidad, por Concepci n Alonso y Ana Isabel Zamora, profesoras de la Universidad de Alcal de Henares (Espa a) StatWiki. Online Statistics Course Materials. Statistical Calculators - GIGACalculator.com. Interactive Statistical Calculation Pages. HyperStat Online: An Introductory Statistics Textbook and Discussion of whether most published research is false. Recommend HyperStat to your friends on Facebook Click here for more cartoons by Ben Shabad.

Other Sources. Statistical Inference (and what is wrong with classical statistics) Home Scope This page concerns statistical inference as described by the most prominent and mainstream school of thought, which is variously described as ‘classical statistics’, ‘conventional statistics’, ‘frequentist statistics’, ‘orthodox statistics’ or ‘sampling theory’.

Oddly, statistical inference—to draw conclusions from the data—is never defined within the paradigm. The practice of statistical inference as described here includes estimation (point estimation and interval estimation (using confidence intervals)) and significance tests (testing a null hypothesis and calculating p-values).

The important point is that all of these methods involve pretending that our sample came from an imaginary experiment that involved considering all possible samples of the same size from the population. A random mathematical blog. R Sites. Forums. Structural Equation Modeling. David A.

Kenny January 12, 2014 Structural Equation Modeling Find out about an interesting conference about SEM: Modern Modeling Methods 2015. This page serves as a gateway to a tutorial on structural equation modeling or SEM. If you have suggestions for changes, please email me. Go to SEM links. Tutorials Ed Rigdon's pages G. Books Kline book on SEM. Homepage of David A. Kenny. Freeware and Documentation. Social Research Methods and Program Evaluation. Social Research Methods and Program Evaluation Resources Basic guides These have very plain and clear descriptions of what evaluation is.

Useful to introduce people to evaluation. Interactive Statistical Calculation Pages. Spatial Statistical Sites. Gordan Žitković - lecture notes. Forecasting: Principles and Practice. Statistical Distributions. Every statistics book provides a listing of statistical distributions, with their properties, but browsing through these choices can be frustrating to anyone without a statistical background, for two reasons.

First, the choices seem endless, with dozens of distributions competing for your attention, with little or no intuitive basis for differentiating between them. Second, the descriptions tend to be abstract and emphasize statistical properties such as the moments, characteristic functions and cumulative distributions. In this appendix, we will focus on the aspects of distributions that are most useful when analyzing raw data and trying to fit the right distribution to that data. Fitting the Distribution When confronted with data that needs to be characterized by a distribution, it is best to start with the raw data and answer four basic questions about the data that can help in the characterization. Is the data discrete or continuous? A. Figure 6A.1: Binomial Distribution b. Good Web Sites. These are some of the bookmarks that I think are particularly useful.

If you have any that you think that I might add, please let me know. Bookmarks seem to become unavailable on an unpredictable schedule, so there may be some here that don't work. General Statistical Stuff Karl Wuensch's StatHelp page This is a great page and I highly recommend it. PsychStat at Missouri State University. K-means clustering. The problem is computationally difficult (NP-hard); however, there are efficient heuristic algorithms that are commonly employed and converge quickly to a local optimum. These are usually similar to the expectation-maximization algorithm for mixtures of Gaussian distributions via an iterative refinement approach employed by both k-means and Gaussian mixture modeling. Additionally, they both use cluster centers to model the data; however, k-means clustering tends to find clusters of comparable spatial extent, while the expectation-maximization mechanism allows clusters to have different shapes.

The algorithm has a loose relationship to the k-nearest neighbor classifier, a popular machine learning technique for classification that is often confused with k-means due to the k in the name. One can apply the 1-nearest neighbor classifier on the cluster centers obtained by k-means to classify new data into the existing clusters. Description[edit] where μi is the mean of points in Si. . Blog — Stats Make Me Cry Consulting. Four years is a reasonable amount of time between blog posts, right?

Help me decide what to blog about. What topics do you want to hear about? Read More Statistical Soup: ANOVA, ANCOVA, MANOVA, & MANCOVA The distinctions between ANOVA, ANCOVA, MANOVA, and MANCOVA can be difficult to keep straight. Elementary statistics books. The Little Handbook of Statistical Practice. The Little Handbook of Statistical Practice Gerard E. Dallal, Ph.D Chief, Biostatistics Unit Jean Mayer USDA Human Nutrition Research Center on Aging at Tufts University 711 Washington Street Boston, MA 02111Gerard.Dallal@tufts.edu Logistic Regression Poisson Regression Degrees of Freedom A good case can be made that the best set of articles about statistical practice written for the practitioner is the series of Statistics Notes appearing in the British Medical Journal.

Free Statistics Book. R Psychologist. Psychology - University of Amsterdam. The UvA website uses cookies and similar technologies to ensure the basic functionality of the site and for statistical and optimisation purposes. It also uses cookies to display content such as YouTube videos and for marketing purposes. This last category consists of tracking cookies: these make it possible for your online behaviour to be tracked.

You consent to this by clicking on Accept or continuing to use this website. Necessary Cookies that are essential for the basic functioning of the website. Optimalisation & Necessary Cookies that collect information about visitor behaviour anonymously to help make the website work more effectively. Marketing, Optimalisation & Necessary Cookies that make it possible to track visitors and show them personalised adverts.

Methodology & Statistics - Faculty of Social and Behavioural Sciences. Understanding Statistical Power and Significance Testing — an Interactive Visualization. Type I and Type II errors, β, α, p-values, power and effect sizes – the ritual of null hypothesis significance testing contains many strange concepts. Much has been said about significance testing – most of it negative. Methodologists constantly point out that researchers misinterpret p-values. Some say that it is at best a meaningless exercise and at worst an impediment to scientific discoveries. Consequently, I believe it is extremely important that students and researchers correctly interpret statistical tests.

Linear Regression. Homepage [The metafor Package] Effect Size Calculator. Recommended Readings on PLS-SEM - pls-sems Webseite! Rigdon, E. E., Sarstedt, M., Ringle, C. M.On Comparing Results from CB-SEM and PLS-SEM: Five Perspectives and Five Recommendations. Marketing ZfP - Journal of Research and Management, Volue 39 (2017), Issue 3, pp. 4-16. Hair, J. F., Hult, G. Transforming Matrix Algebra Education for the STEM Undergraduate. CIS - Current Index to Statistics. How2stats: SPSS. The R Stats Package. Documentation for package ‘stats’ version 3.6.0.

Model Fitting · Data Science with R. SAS/ETS 14.3 User's Guide Documentation Examples. Online Statistics Education: A Free Resource for Introductory Statistics. Rice Virtual Lab in Statistics (RVLS) Partial support for this work was provided by the National Science Foundation's Division of Undergraduate Education through grant DUE 9751307. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. StatWiki. History of Statistics. Link to Departmental Home Page Portraits of Statisticians. Economagic: Economic Time Series Page. Introduction to Statistical Learning. Course: Estadística Descriptiva. La estadística es una disciplina que apoya el proceso de toma de decisiones en diversas áreas del conocimiento, además, de entregar pautas para la presentación adecuada de información.

Este curso que se empezó en el año 2007, para estudiantes de pregrado en el àrea de la salud, se ha aprovechado para insertar temas y opiniones de personas tanto de nivel de especialización como de maestrias, lo que ha permitido generar un mayor conocimiento para todos los participantes, dado que los foros se han diseñado para posibilitar la participación de todos. The World-Wide Web Virtual Library: Statistics. External Resources. Electronic Journal of Probability. Statistics.com. External Resources. Virtual Laboratories in Probability and Statistics.

Susan Holmes- Stanford University. Probabilty by Surprise. The Cross-Entropy Method. Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook. MORE PROBABILTIY AND STATISTICS LINKS. HyperStat Online: An Introductory Statistics Textbook and Discussion of whether most published research is false.

Probability Web. Calculadora del incremento del indice. Estadística para todos. Estadística para todos. The pLab project - Home. Statistical Modeling, Causal Inference, and Social Science - Statistical Modeling, Causal Inference, and Social Science. STAT 505 - Applied Multivariate Statistical Analysis. SEMATECH e-Handbook of Statistical Methods. Center For Statistics And The Social Sciences. Random Numbers Info. All your Bayes are belong to us! Robert kohn. Bradley P. Carlin. Marc A. Suchard. International financial statistics. Label:large_scale_data_analysis. HyperStat Online: An Introductory Statistics Textbook and Online Tutorial for Help in Statistics Courses. Big Data, Data Mining, Predictive Analytics, Statistics, StatSoft Electronic Textbook. Dr. Arsham's Web Page.