R u training on line?

Онлайновы курсы и линьки на полезные Rесурсы 

meaning forecast

robjhyndman.com пишет про новый R пакет прогнозирования как бе, но пишет следующим образом:
The accuracy func­tion now works with both time series fore­casts and cross-​​sectional forecasts. то-есть, по-руски прогнозирование -- это предвидение по времени, а попе н достансге -- совсем не обязательно и вводит нас (если на демографическом образовании строицо) в мир indirect techniques

Multivariate regression analysis using categorical explanatory variables

LinkedIN начал новую нитку с этой назвой

Which package is useful to handle categorical variables in a multivariate regression analyisis and where may I find the tutorials? Every information that I find is about quantitative data.
I am currently using a lm function to find the best predictor for a quantitative response variable but I am afraid, it is not the best approach. I also do not want to use dummies.

research vs grants

Rob J Hyn­d­man :
I don’t think telling peo­ple what research areas are impor­tant is very help­ful. The best research comes about when peo­ple are curi­ous and pas­sion­ate, and take a sur­pris­ingly dif­fer­ent per­spec­tive from those who have gone before.
from here
действительно, как раз сейчас пытаюсь ответить на вопросы какбе грантодателей, 1 из х:
Существует ли в стране политика, программа и/или стратегия, направленная на решение вопросов сексуального и репродуктивного здоровья и репродуктивных прав, которая(ые) находится в процессе разработки или исполнения?
ответить надо да/нет

Tutorials from Universities Around the World

Online tutorials for R programming, Statistics and Graphic

Here is a list of FREE R tutorials hosted in official website of universities around the world. The tutorials are listed  in no particular order, actually based on when I have discovered it. They will be categorised soon. Please kindly suggest me other university-hosted online R tutorials by email to me@pairach.com.

1. University of California at Davis,
   Getting Started with the R Data Analysis Package
   by Professor Norm Matloff
2. Clarkson University,
   R Tutorial
   by Kelly Black
3. York University,
   Getting started with R
4. University of Waterloo
   R Tutorial For A WINDOWS Environment (Windows, Unix)
5. University of California at Los Angles, UCLA,
   Resources to help you learn and use R
6. University of California at Riverside.
   Programming in R
7. University of Illinois,
   A Brief Introduction to R
8. University of Texas at Austin,
   R Tutorial Videos
   by Brandon K. Vaughn
9. University of California at Berkeley,
   An Introduction to R (PDF)
   by Phil Spector
10. University of California at Santa Babara,
    R Programming Resource Centre
    by National Center for Ecological Analysis and Synthesis
11. Chiang Mai University,
    Econometrics with R (in Thai)
    by Pairach Piboonrungroj.
12. University of Carnegie Mellon,
    A Tutorial: Some Fundamentals of R.
    by Bruce E. Trumbo
13. University of Illinois State,
    R Tutorial.
    by Dong-Yun Kim
14. University of MacMaster,
    Introduction to the R Statistical Computing Environment.
    by John Fox.
15. University of Princeton,
    Introducing R.
    by Germán Rodríguez
16. University of Amsterdam,
    How to draw graphs with R,  [Graphics]
    by A.M. (Angelos-Miltiadis) Krypotos
17. University of North Texas,
    Do it yourself – Introduction to R [Intro]
18. University of Warwick,
    R programming page [Biosciences: Molecular Organisation and Assembly in Cells]
    by Peter Cock.
19. University of Illinois at Urbana-Champaign
    R tutorial for Applied Econometrics
    by Prof. Roger Koenker
    
20. Coastal Carolina University
    R tutorials [General]
    by  William B. King
21. University of Colorado Denver
    R Tutorial [General]
    by  Stephanie Santorico and Mark Shin
22. Stanford School of Medicine (Biomedical Informatics)
    R Tutorial [VDO on Introduction + Translational Bioinformatics]
23. Harding University
    Producing Simple Graphs with R [basic graphic e.g., line, bar, hist, line]
    by Frank McCown
24. University of Kentucky, Department of Statistics
    Use Software R to do Survival Analysis and Simulation [pdf]
    by Mai Zhou

Forecasting time series using R by Prof Rob J Hyndman at Melbourne R Users

comparing R, Matlab, SAS, STATA, SPSS

The following table compares the standard procedures of the five packages in detail. By "standard" I mean built-in or readily available from the official or widely known and reliable public web-sites.

TYPE OF STATISTICAL ANALYSIS	R	MATLAB	SAS	STATA	SPSS
Nonparametric Tests	Yes	Yes	Yes	Yes	Yes
T-test	Yes	Yes	Yes	Yes	Yes
ANOVA & MANOVA	Yes	Yes	Yes	Yes	Yes
ANCOVA & MANCOVA	Yes	Yes	Yes	Yes	Yes
Linear Regression	Yes	Yes	Yes	Yes	Yes
Generalized Least Squares	Yes	Yes	Yes	Yes	Yes
Ridge Regression	Yes	Yes	Yes
Lasso	Yes	Yes	Experimental
Generalized Linear Models	Yes	Yes	Yes	Yes	Yes
Mixed Effects Models	Yes	Yes	Yes	Yes	Yes
Logistic Regression	Yes	Yes	Yes	Yes	Yes
Nonlinear Regression	Yes	Yes	Yes
Discriminant Analysis	Yes	Yes	Yes	Yes	Yes
Nearest Neighbor	Yes	Yes	Yes		Yes
Factor & Principal Components Analysis	Yes	Yes	Yes	Yes	Yes
Copula Models	Yes	Yes	Experimental
Cross-Validation	Yes	Yes	Yes
Bayesian Statistics	Yes	Yes	Limited
Monte Carlo, Classic Methods	Yes	Yes	Yes	Yes	Limited
Markov Chain Monte Carlo	Yes	Yes	Yes
Bootstrap & Jackknife	Yes	Yes	Yes	Yes
EM Algorithm	Yes	Yes	Yes
Missing Data Imputation	Yes	Yes	Yes	Yes	Yes
Outlier Diagnostics	Yes	Yes	Yes	Yes	Yes
Robust Estimation	Yes	Yes	Yes	Yes
Longitudinal (Panel) Data	Yes	Yes	Yes	Yes
Survival Analysis	Yes	Yes	Yes	Yes	Yes
Path Analysis	Yes	Yes	Yes
Propensity Score Matching	Yes	Yes	Limited
Stratified Samples (Survey Data)	Yes	Yes	Yes	Yes	Yes
Experimental Design	Yes	Yes
Quality Control	Yes	Yes		Yes	Yes
Reliability Theory	Yes	Yes	Yes	Yes	Yes
Univariate Time Series	Yes	Yes	Yes	Yes	Limited
Multivariate Time Series	Yes	Yes	Yes	Yes
Markov Chains	Yes	Yes
Hidden Markov Models	Yes	Yes
Stochastic Volatility Models	Yes	Yes	Limited	Limited	Limited
Diffusions	Yes	Yes
Counting Processes	Yes	Yes	Yes
Filtering	Yes	Yes	Limited	Limited
Instrumental Variables	Yes	Yes	Yes	Yes
Simultaneous Equations	Yes	Yes	Yes	Yes
Splines	Yes	Yes	Yes
Nonparametric Smoothing Methods	Yes	Yes	Yes	Yes
Extreme Value Theory	Yes	Yes
Variance Stabilization	Yes	Yes
Cluster Analysis	Yes	Yes	Yes	Yes	Yes
Neural Networks	Yes	Yes	Yes		Limited
Classification & Regression Trees	Yes	Yes	Yes		Limited
Boosting Classification & Regression Trees	Yes	Yes
Random Forests	Yes	Yes
Support Vector Machines	Yes	Yes
Signal Processing	Yes	Yes
Wavelet Analysis	Yes	Yes	Yes
ROC Curves	Yes	Yes	Yes	Yes	Yes
Optimization	Yes	Yes	Yes

Please read the detailed description of the services offered in the areas of statistical consulting and financial consulting: home page, types of service, experience, case studies and payment options. You may also find the following pages useful: statistics resources and finance resources.

Source: http://stanfordphd.com/Statistical_Software.html