Voronoi diagram from smooshing paint between glass

clicar na imagem para seguir o link

Uma abordagem original aos diagramas de Voronoi.

Tags: ,

How To Forecast Time Series Data With Multiple Seasonal Periods

clique na imagem para seguir o link

clique na imagem para seguir o link

Análise de séries complexas com múltiplos períodos sazonais

Time series data is produced in domains such as IT operations, manufacturing, and telecommunications. Examples of time series data include the number of client logins to a website on a daily basis, cell phone traffic collected per minute, and temperature variation in a region by the hour. Forecasting a time series signal ahead of time helps us make decisions such as planning capacity and estimating demand. Previous time series analysis blog posts focused on processing time series data that resides on Greenplum database using SQL functions. In this post, I will examine the modeling steps involved in forecasting a time series sequence with multiple seasonal periods. The various steps involved are outlined below:

  • Multiple seasonality is modelled with the help of fourier series with different periods
  • External regressors in the form of fourier terms are added to an ARIMA model to account for the seasonal behavior
  • Akaike Information Criteria (AIC) is used to find the best fit model

Tags:

A Simple Introduction to Complex Stochastic Processes

clique na imagem para seguir o link

clique na imagem para seguir o link

Stochastic processes have many applications, including in finance and physics. It is an interesting model to represent many phenomena. Unfortunately the theory behind it is very difficult, making it accessible to a few ‘elite’ data scientists, and not popular in business contexts.

1. Construction of Time-Continuous Stochastic Processes: Brownian Motion

2. General Properties

Tags:

Data Analysis Method: Mathematics Optimization to Build Decision Making

clique na imagem para seguir o link

clique na imagem para seguir o link

Uma pequena introdução à utilização de otimização na análise de dados

Optimization is a problem associated with the best decision that is effective and efficient decisions whether it is worth maximum or minimum by way of determining a satisfactory solution.

Optimization is not a new science. It has grown even since Newton in the 17th century discovered how to count roots. Currently the science of optimization is still evolving in terms of techniques and applications. Many cases or problems in everyday life that involve optimization to solve them. Lately much developed especially in the emergence of new techniques to solve the problem of optimization. To mention some, among others, conic programming, semi definite programming, semi infinite programming and some meta heuristic techniques.

Tags: ,

Earliest Known Uses of Words of Mathematics

clique na imagem para seguir o link
clique na imagem para seguir o link

um curioso site onde se registam os significados mais antigos para termos matemáticos

These pages attempt to show the first uses of various words used in mathematics. Research for these pages is ongoing, and a citation should not be assumed to be the earliest use unless it is indicated as such.

Mathematical Words: Origins and Sources by John Aldrich is an excellent article and companion to this web site.

Please see also Earliest Uses of Various Mathematical Symbols, Images of Mathematicians on Postage Stamps, and Ambiguously Defined Mathematical Terms at the High School Level.

These pages are maintained by Jeff Miller, a teacher at Gulf High School in New Port Richey, Florida. The principal contributors are John Aldrich, Julio González Cabillón, Carlos César de Araújo, and James A. Landau. Other contributors are Manoel de Campos Almeida, Antranig Basman, Dave Cohen, John Conway, Martin Davis, Karen Dee Michalowicz, Joanne M. Despres of Merriam-Webster Inc., Bill Dubuque, Mark Dunn, John G. Fauvel, Walter Felscher, Giovanni Ferraro, Tom Foregger, Michael N. Fried, John Harper, Antreas P. Hatzipolakis, Barnabas Hughes, Samuel S. Kutler, Franz Lemmermeyer, Avinoam Mann, Peter M. Neumann, Ken Pledger, Paul Pollack, Jim Propp, Aldo I. Ramirez, Lee Rudolph, Randy K. Schwartz, Max Urchs, Tom Walsh, William C. Waterhouse, and David Wilkins.

“Perhaps I may without immodesty lay claim to the appellation of Mathematical Adam, as I believe that I have given more names (passed into general circulation) of the creatures of mathematical reason than all the other mathematicians of the age combined.” —James Joseph Sylvester, Nature 37 (1888), p. 152.

Tags:

R news and tutorials R bloggers

clique na imagem para seguir o link

clique na imagem para seguir o link

Montes de blogs sobre R.

Here you will find daily news and tutorials about R, contributed by over 573 bloggers.

Top 3 Posts from the past 2 days

Top 9 articles of the week

  1. Installing R packages
  2. In-depth introduction to machine learning in 15 hours of expert videos
  3. New Version of RStudio (v0.99) Available Now
  4. Using apply, sapply, lapply in R
  5. Review of ‘Advanced R’ by Hadley Wickham
  6. Scatterplots
  7. An R Enthusiast Goes Pythonic!
  8. Open data sets you can use with R
  9. Basics of Histograms

Tags:

Rtips. Revival 2014!

Uma animação com todos os lugares referidos numa canção de johnny cash

Uma animação com todos os lugares referidos numa canção de johnny cash

Montes de exemplos de R numa única longa página.

Table of Contents
Section: Original Preface
Section 1: Data Input/Output
Section 2: Working with data frames: Recoding, selecting, aggregating
Section 3: Matrices and vector operations
Section 4: Applying functions, tapply, etc
Section 5: Graphing
Section 6: Common Statistical Chores
Section 7: Model Fitting (Regression-type things)
Section 8: Packages
Section 9: Misc. web resources
Section 10: R workspace
Section 11: Interface with the operating system
Section 12: Stupid R tricks: basics you can’t live without
Section 13: Misc R usages I find interesting

Tags: , , , ,

Statistical Associates E-Book Catalog

clicar na imagem para seguir o link

clicar na imagem para seguir o link

e-books grátis.

TITLE INFO DESCRIPTION EDITION FREE KINDLE
NO PASSWORD REQUIRED FOR TITLES IN THIS SECTION
2013 Annual Report, Statistical Associates Publishers Info Pages: 8. Coverage: General. 2013 Free No Kindle edition
10 Worst Statistical Mistakes and Pitfalls Info Coverage: For selected statistical procedures 2015 Free No Kindle edition
Creating Simulated Datasets Info Pages: 15. Coverage: General, SPSS. 2012 Free No Kindle edition
Game Theory Info Pages: 15. Coverage: General. 2012 Free No Kindle edition
Probability Info Pages: 15. Coverage: General, SPSS, SAS, Stata. 2013 Free No Kindle edition
Testing Statistical Assumptions Info Pages: 51. Coverage: General, SPSS. 2012 Free Coming
E-MONOGRAPHS: ALL $5 AT AMAZON/KINDLE
Association, Measures of Info Pages: 49. Coverage: General, SPSS. 2012 Free Buy at Amazon
Correlation Info Pages: 60. Coverage: General, SPSS, SAS, Stata. 2013 Free Buy at Amazon
Correspondence Analysis Info Pages: 37. Coverage: General, SPSS. 2012 Free Buy at Amazon
Crosstabulation Info Pages: 60. Coverage: General, SPSS, SAS, Stata. 2013 Free Buy at Amazon
Curve Fitting & Nonlinear Regression Info Pages: 53. Coverage: General, SPSS. 2012 Free Buy at Amazon
Discriminant Function Analysis Info Pages: 52. Coverage: General, SPSS. 2012 Free Buy at Amazon
Life Tables & Kaplan-Meier Analysis Info Pages: 32. Coverage: General, SPSS. 2012 Free Buy at Amazon
Literature Review in Research and Dissertation Writing Info Pages: 52. Coverage: General. 2013 Free Buy at Amazon
Multidimensional Scaling Info Pages: 55. Coverage: General, SPSS. 2012 Free Buy at Amazon
Network Analysis Info Pages: 35. Coverage: General, UCINET. 2012 Free Buy at Amazon
Ordinal Regression Info Pages: 93. Coverage: General, SPSS, SAS, Stata. 2014 Free Buy at Amazon
Parametric Survival Analysis (Event History Analysis) Info Pages: 64. Coverage: General, Stata, SAS. 2012 Free Buy at Amazon
Partial Correlation Info Pages: 40. Coverage: General, SPSS, SAS, Stata. 2014 Free Buy at Amazon
Path Analysis Info Pages: 81. Coverage: General, SPSS AMOS. SAS, Stata. 2014 Free Buy at Amazon
Power Analysis Info Pages: 36. Coverage: General, SPSS SamplePower, G*Power. 2012 Free Buy at Amazon
Probit Regression & Response Models Info Pages: 92. Coverage: General, SPSS. 2012 Free Buy at Amazon
Research Design Info Pages: 53. Coverage: General. 2013 Free Buy at Amazon
Scales and Measures Info Pages: 91. Coverage: General, SPSS, SAS, Stata, WINSTEPS, jMetric 2013 Free Buy at Amazon
Survey Research & Sampling Info Pages: 82. Coverage: General. 2013 Free Buy at Amazon
Two-Stage Least Squares Regression Info Pages: 45. Coverage: General, Stata, SPSS, SAS. 2013 Free Buy at Amazon
Variance Components Analysis Info Pages: 37. Coverage: General, SPSS, SAS. 2012 Free Buy at Amazon
WLS: Weighted Least Squares Regression Info Pages: 54. Coverage: General, SPSS, SAS, Stata. 2013 Free

Tags:

Scholarpedia

clique na imagem para seguir a ligação

clique na imagem para seguir a ligação

Uma alternativa à weekepedia com suposta melhor qualidade.

Welcome to Scholarpedia

the peer-reviewed open-access encyclopedia,
where knowledge is curated by communities of experts

Established faculty and researchers

Disseminate: share your expertise with a global audience

Pioneer: write the first persistent online review in your area of specialization

Steward: supervise the development of articles in your field

Students and writers

Collaborate: work with expert scientists and scholars from around the world

Learn: gain experience with scholarly writing and editing

Publish: transform your writing into a peer-reviewed article

erts

Tags:

Markov Chains explained visually

Boa forma de perceber como funcionam as cadeias de Markov

Boa forma de perceber como funcionam as cadeias de Markov

Adding on to their series of graphics to explain statistical concepts, Victor Powell and Lewis Lehe use a set of interactives to describe Markov Chains. Even if you already know what Markov Chains are or use them regularly, you can use the full-screen version to enter your own set of transition probabilities. Then let the simulation run.

Tags: ,