terça-feira, 9 de Abril de 2013

Explicações de Análise de Dados ONLINE via SKYPE

1º Visita o site do skype
2º Regista-te e faz o download gratuito
3º Testa com amigos, conhecidos ou familiares (o chat, o audio e a partilha de ecrã/ desktop)

As explicações via skype permitem que poupemos tempo em deslocações e têm valores hora mais acessíveis.

Por valores desde 15 euros terás apoio na parte estatística / análise de dados da tua tese ou trabalho.

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segunda-feira, 11 de Junho de 2012

Equações Estruturais com o pacote SEM do software R / Structural Equation Modeling with the "SEM" Package in R


"R is free, open-source, cooperatively developed software that implements the S sta-tistical programming language and computing environment. The current capabilities of R are extensive, and it is in wide use, especially among statisticians. The sem package provides basic structural equation modeling facilities in R, including the ability to fit structural equations in observed variable models by two-stage least squares, and to fit latent variable models by full information maximum likelihood as-suming multinormality. This article briefly describes R, and then proceeds to illus-trate the use of the tsls and sem functions in the sem package. The article also demonstrates the integration of the sem package with other facilities available in R, for example for computing polychoric correlations and for bootstrapping."

Aceda a mais informações sobre este pacote do software R em: http://personality-project.org/r/r.sem.html e em http://personality-project.org/r/r.guide.html


Equações Estruturais com o software AMOS da IBM SPSS


IBM® SPSS® Amos gives you the power to easily perform structural equation modeling to build models with more accuracy than with standard multivariate statistics techniques.
With SPSS Amos, you can specify, estimate, assess, and present your model in an intuitive interface to show hypothesized relationships among variables. Alternatively, SPSS Amos offers a non-graphical method to specify models. SPSS Amos is the perfect tool for a variety of purposes, including:

LISREL for structural equation models / Aplicação LISREL para modelos de equações estruturais - Structural equation modeling (SEM)


Maximum Likelihood (ML), Robust Maximum Likelihood (RML), Generalized Least Squares (GLS), Un-weighted Least Squares (ULS), Weighted Least Squares (WLS), Diagonally Weighted Least Squares (DWLS) and Full Information Maximum Likelihood (FIML) methods to fit structural equation models to data (...)

In practice, the variables of interest are often latent (unobservable) variables, such as intelligence, job satisfaction, organizational commitment, socio-economic status, ambition, alienation, verbal ability, etc.  These latent variables are modeled by specifying a measurement model and a structural model.  The measurement model specifies the relationships between the observed indicators and the latent variables while the structural model specifies the relationships amongst the latent variables.  However, it is also possible and often desired to include observed variables as part of the structural model. 

"Structural equation modeling (SEM). SEM allows researchers in the social sciences, management sciences, behavioral sciences, biological sciences, educational sciences and other fields to empirically assess their theories. These theories are usually formulated as theoretical models for observed and latent (unobservable) variables. If data are collected for the observed variables of the theoretical model, the LISREL program can be used to fit the model to the data.
Today, however, LISREL for Windows is no longer limited to SEM. The latest LISREL for Windows includes the following statistical applications.
  • LISREL for structural equation modeling.
  • PRELIS for data manipulations and basic statistical analyses.
  • MULTILEV for hierarchical linear and non-linear modeling.
  • SURVEYGLIM for generalized linear modeling.
  • CATFIRM for formative inference-based recursive modeling for categorical response variables.
  • CONFIRM for formative inference-based recursive modeling for continuous response variables.
  • MAPGLIM for generalized linear modeling for multilevel data. "

domingo, 10 de Junho de 2012

Applied Statistics Using SPSS, STATISTICA, MATLAB and R


SPSS and STATISTICA; MATLAB and R, Presenting and Summarising the Data, Estimating Data Parameters, Parametric Tests of Hypotheses, Non-Parametric Tests of Hypotheses , Statistical Classification, Data Regression, Data Structure Analysis, Survival Analysis , Directional Data, MATLAB Functions, R Functions, Tools EXCEL  File, SCSize Program (...)

Stochastic Processes / Processos estocásticos

Markov Chains, Renewal and Regenerative Processes, Poisson Processes, Continuous-Time Markov Chains, Brownian Motion, Probability Spaces and Random Variables, Table of Distributions, Random Elements and Stochastic Processes, Expectations as Integrals, Functions of Stochastic Processes, Independence, Conditional Probabilities and Expectations, Existence of Stochastic Processes, Convergence (...) 

Análise de Dados com o Excel


Excel Tables, Working with PivotTables, Building PivotTable Formulas, Using the Database Functions, Using the Statistics Functions, Descriptive Statistics, Inferential Statistics, Optimization Modeling with Solver, Presenting Table Results and Analyzing Data (...)