Tuesday 27 March 2012

Programming in R



Most statistical software has a graphical interface available. Why learn programming?  Hadley Wickham, a leading member of the R development community, lists the advantages:  "reproducibility, automation and communication" (From an interview in "Decision Stats").  Dr. Wickham will offer his online course, "Programming in R".  For more details please visit at http://www.statistics.com/Rprogramming.

In "Programming in R", you will hone your skills to work with a variety of data types and data sources in R. You'll also learn some techniques for programming "in-the-large", when you are trying to provide a suite of functions to flexibly solve a large class of problems. In particular, you'll learn more about functions, environments and closures, and the basics of object oriented programming with S3.

Dr. Hadley Wickham is the author of "ggplot2: Elegant Graphics for Data Analysis (Use R)" and a contributor to Cook & Swayne's "Interactive and Dynamic Graphics for Data Analysis: Using R and GGobi" (2007). His research interests include interactive and dynamic graphics, developing practical tools for data analysis, and in gaining better understanding of complex statistical models through visualization. An Assistant Professor at Rice University, Dr. Wickham has developed 15 R projects, and written numerous articles, chapters, and other papers and in 2006 he won the John Chambers Award for Statistical Computing for his work on the ggplot and reshape R packages. Participants can ask questions and exchange comments with Dr. Wickham via a private discussion board throughout the period.

Who Should Take This Course?
Statistical analysts who want to use R as a serious statistical computing tool.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Functions and Controlling Evaluation
In this session you'll learn how to go beyond the basics to make closures, functions that are written by other functions. To understand how closures work, we'll go over R's scoping rules and discuss how environments and frames work. To show why closures are worth learning about, we'll give some examples using them for maximum likelihood estimation, and for creating functions that remember what happened last time they were called.

SESSION 2: Strings and Dates
Learn the basics of string manipulation (with regular expressions) and working with dates. Base R provides some functions to deal with these common data types, but they're not very friendly. We'll briefly cover base R functions, then move on to two new packages, stringr and lubridate, that have been designed from the ground up to be consistent and easy to use.

SESSION 3: XML and HTML
For xml, you'll learn the basics of xpath expressions, which make it easy to pull out the pieces that you're interseted in, and how to avoid some of the pitfalls of the xml package.

SESSION 4: Working with Databases
In this session you will learn the basics of working with external databases and xml files. We'll cover some of the range of options for accessing external databases, and some convenient tools for converting large local datasets into databases to make it faster to retrieve subsets of interest.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 66009116

Websites:

Tuesday 20 March 2012

Generalized Linear Models (GLM)

Technique-driven statistical analysis can be like the blind men sent by the king to examine an elephant.  The man who feels the leg says an elephant is like a pillar, the one who feels the tails says it is like a rope, the one who feels the tusk says it is like a pipe.  It is left to the king to integrate the accounts, which he does masterfully (this tale is told by the king).  Dr. Joe Hilbe and Dr. James Hardin help to ensure that your own statistical focus does not narrow too quickly with the help of online course "Generalized Linear Models (GLM)".  For more details please visit at http://www.statistics.com/glm.

"Generalized Linear Models GLM" (Hilbe and Hardin) extends ordinary least squares (OLS) regression to incorporate responses other than normal.  This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.  GLM allows the modeling of responses, or dependent variables, that take the form of counts, proportions, dichotomies (1/0), positive continuous values, as well as values that follow the normal Gaussian distribution. Continuous response variables, the log normal, gamma, log-gamma (survival analysis), and inverse Gaussian cases are covered. Binomial (logit, probit, and others) as well as count models (poisson, negative binomial, geometric) are also touched.

Joe Hilbe and James Hardin are the co-authors of "Generalized Linear Models and Extensions" (Stata Press) as well as "Generalized Estimating Equations" (CRC Press).  They have lectured widely in these areas, and have been instrumental in developing computer routines for these methods - routines that have been incorporated into popular statistical software programs.

Dr. Joseph Hilbe is Emeritus Professor at the University of Hawaii and Solar System Ambassador with NASA's Jet Propulsion Laboratory at California Institute of Technology, and Adjunct Professor of Statistics at Arizona State University. Dr. Hilbe has authored over one hundred journal articles and is author of the COUNT package in R, located on the CRAN website. He was also the first editor of theStata Technical Bulletin (now Stata Journal) and from 1997-2009 was Software Reviews Editor forThe American Statistician. Dr. Hilbe is Editor-in-Chief of the Springer Series in Astrostatistics.

Dr. James Hardin is a Research Associate Professor at the University of South Carolina. Co-author (with Joseph Hilbe) of Generalized Estimating Equations, Dr. Hardin is on the editorial board of The Stata Journal and is the developer of the Stata GEE command, and with Dr Hilbe is developer of the GLM command.

Who Should Take This Course?
Analysts in any field who need to move beyond standard multiple linear regression models for modeling their data.

Course Program:

Course outline: The course is structured as follows

 

SESSION 1: General Overview of GLM

  • Derivation of GLM functions
  • GLM algorithms: OIM, EIM
  • Fit and residual statistics


SESSION 2: Continuous Response Models

  • Gaussian
  • Log-normal
  • Gamma
  • Log-gamma models for survival analysis
  • Inverse Gaussian

SESSION 3: Discrete Response Models

  • Binomial models: logit, probit, cloglog, loglog, others
  • Count models: Poisson, negative binomial, geometric

SESSION 4: Problems with Overdispersion

  • Overview of ordered and unordered logit and probit regression
  • Overview of panel models

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.

Call: 020 66009116

Websites:

Thursday 15 March 2012

Forecasting Time Series


"I have seen the future, and it is just like the present, only longer."  This quote from "The Profit" (a parody of "The Prophet") actually contains subtle insight into statistical forecasting.  It's not a magic wand - it is simply a collection of tools for projecting into the future what we think we know about how the present works.  Statistical forecasting is both an art and a science - the science part is covered in Galit Shmueli's "Forecasting Time Series," offered online at statistics.com. For more details please visit at http://www.statistics.com/forecasting.

Dr. Shmueli is Professor of Data Analytics at the Indian School of Business (SRITNE) in Hyderabad, and Associate Professor of Statistics in the department of Decision, Operations & Information Technologies at the Smith School of Business, University of Maryland. Dr. Shmueli's research has been published in the statistics, information systems, and marketing literature; she is a co-author of "Data Mining for Business Intelligence," "Modeling Online Auctions," and
"Statistical Methods in e-Commerce Research."

Aim of Course:
This course will teach you how to choose an appropriate time series forecasting method, fit the model, evaluate its performance, and use it for forecasting. The course will focus on the most popular business forecasting methods: Regression models, smoothing methods including Moving Average (MA) and Exponential Smoothing, and Autoregressive (AR) models. It will also discuss enhancements such as second-layer models and ensembles, and various issues encountered in practice.

Who Should Take This Course:
Business analysts, sales forecasters, economists, financial analysts, anyone who needs to produce, interpret or assess forecasts will find this course useful. Participants should be familiar with basic statistics, including linear regression.

Course Program:

Course outline: The course is structured as follows


SESSION 1: Characterizing Time Series and the Forecasting Goal; Evaluating Predictive Accuracy and Data Partitioning

  • Visualizing time series
  • Time series components
  • Forecasting vs. explanation
  • Evaluating predictive accuracy


SESSION 2: Regression-Based Models

  • Capturing trends with linear regression
  • Capturing seasonality with linear regression
  • Measuring and interpreting autocorrelation
  • Evaluating predictability and the Random Walk
  • Second-layer models using Autoregressive (AR) models


SESSION 3: Smoothing-Based Methods

  • Model-driven vs. data-driven methods
  • Centered and training Moving Average (MA)
  • Exponential Smoothing (simple, double, triple)
  • Differencing
  • ARMA and ARIMA models
  • Estimation of models

 

SESSION 4: Forecasting in Practice

  • Improving forecasts via ensembles
  • Multiple seasonal patterns
  • Automated forecasting (series partitioning, changing behavior, missing values)
  • Handling managerial forecast corrections

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.


Call: 020 66009116

Websites:


Tuesday 13 March 2012

Centre for eLearning and Training (C-eLT)


We, at Centre for eLearning and Training (C-eLT), a division of TechKnit IT Enabled Services Pvt. Ltd. Pune, offer a suite of eLearning services to undergraduate students, executives, researchers and professionals in USA. We offer these services through our partner based in Washington DC in Mathematics, Statistics, All Sciences and English writing.

Specifically in Statistics domain, we have partnered with Statistics.com, based in Arlington, VA USA, who offers over 100 online courses in Applied Statistics. We work with Statistics.com for course management, provide Teaching Assistance (TA) service and coordinate with course instructors all over the world. We also offer online consultation for professionals and academics in designing or Analyzing research studies in statistics.

We offer special service to Indian participants by offering courses at concessional price as compared to US and also provide facilities to pay in INR through our payment gateway. Please browse 
www.india.statistics.com to know more about these online courses which are useful to individuals, who are involved in Decision Making, Business Analysis, Policy design, Research and those who handles huge amount of data etc.

Our online courses are in the field of Data Mining, Using R, Life Science, Social Science, Business Statistics, Engineering, Bayesian, Environment Statistics etc

All our courses are in the online format thus you can study at your convenience without hampering your day-to-day job responsibility.

To know more about the range of Online Courses, please visit 
http://www.statistics.com/course-catalog  


For More Details please contact at 
Email: info@c-elt.com
Call: 020 66009116

Monday 5 March 2012

Introduction to Statistics 1: Sampling and Inference



“Introduction to Statistics 1: Sampling and Inference” provides an introduction to statistical inference for a single variable. Once you have completed this course you will be able to apply statistically valid designs to basic studies, and test hypotheses regarding proportions and means. Dr. Michelle Everson will present his online course, “Introduction to Statistics 1: Sampling and Inference” at statistics.com. For more details please visit at http://www.statistics.com/introstats1.

The instructor, Dr. Michelle Everson is currently a Senior Lecturer in the Department of Educational Psychology at the University of Minnesota.  She completed a Bachelor’s degree in Industrial Psychology from California State University, Hayward and a Master’s degree in Experimental Psychology from San Jose State University before coming to the University of Minnesota to pursue a Ph.D. in Educational Psychology.  Since obtaining her Ph.D. in 2002, Michelle has been teaching introductory and intermediate statistics courses, and she also teaches a course called "Becoming a Teacher of Statistics." Michelle is particularly interested in distance education and in ways to actively engage students in online learning environments.  Michelle is the recipient of the University of Minnesota College of Education and Human Development’s 2009 Distinguished Teaching Award and the 2011 recipient of the American Statistical Association (ASA) Waller Education Award.

Aim of Course:
To provide an introduction to statistical inference for a single variable. Once you have completed this course you will be able to apply statistically valid designs to basic studies, and test hypotheses regarding proportions and means.

Note: This is the second of a four-course sequence in Introductory Statistics.

Who Should Take This Course:
Anyone who encounters statistics in their work, and anyone who needs introductory statistics for further study. 

Course Program:

Course outline: The course is structured as follows

 

SESSION 1:  Sampling and Sampling Distributions

  • Sample surveys
  • Point estimates
  • Sampling distributions for means and proportions
  • Resampling
  • Formula equivalents to resampling

 

SESSION 2: Confidence Intervals for Proportions and Means

  • Confidence intervals for means
  • Confidence intervals for proportions
  • Confidence intervals for differences

 

SESSION 3: Hypothesis Tests

  • Confidence intervals vs. hypothesis tests
  • Paired comparisons
  • Departure from expectation
    • Chi-squared test
  • Null and alternative hypotheses
  • One-tail, two-tail tests

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.

If you have any query please feel free to call me or write to me. 

For More details contact at
Call: 020 66009116

Websites:

Programming in R


Legend (or at least Wikipedia) has it that "R," the statistical programming language, was named after Robert and Ross (Gentleman and Ihaka), R's originators.  R's capabilities (and its use) are growing rapidly, and if you are considering adopting it, or extending its use, statistics.com's online courses in “R” are a good place to learn more. Dr. Hadley Wickham will present his online course, “Programming in R” at statistics.com. For more details please visit at http://www.statistics.com/rprogramming.

Dr. Wickham is the author of "ggplot2: Elegant Graphics for Data Analysis (Use R)" and a contributor to Cook & Swayne's "Interactive and Dynamic Graphics for Data Analysis: Using R and GGobi" (2007). An Assistant Professor at Rice University, Dr. Wickham has developed 15 R projects, and written numerous articles, chapters, and other papers and in 2006 he won the John Chambers Award for Statistical Computing for his work on the ggplot and reshape R packages.

Aim of the course:
In "Programming in R" with Hadley Wickham, you will hone your skills to work with a variety of data types and data sources in R. You'll also learn some techniques for programming "in-the-large", when you are trying to provide a suite of functions to flexibly solve a large class of problems.  In particular, you'll learn more about functions, environments and closures, and the basics of object oriented programming with S3. 

Who Should Take This Course:
Statistical analysts who want to use R as a serious statistical computing tool.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Functions and Controlling Evaluation
In this session you'll learn how to go beyond the basics to make closures, functions that are written by other functions. To understand how closures work, we'll go over R's scoping rules and discuss how environments and frames work. To show why closures are worth learning about, we'll give some examples using them for maximum likelihood estimation, and for creating functions that remember what happened last time they were called.

SESSION 2: Strings and Dates
Learn the basics of string manipulation (with regular expressions) and working with dates. Base R provides some functions to deal with these common data types, but they're not very friendly. We'll briefly cover base R functions, then move on to two new packages, stringr and lubridate, that have been designed from the ground up to be consistent and easy to use.

SESSION 3: XML and HTML
For xml, you'll learn the basics of xpath expressions, which make it easy to pull out the pieces that you're interseted in, and how to avoid some of the pitfalls of the xml package.

SESSION 4: Working with Databases
In this session you will learn the basics of working with external databases and xml files. We'll cover some of the range of options for accessing external databases, and some convenient tools for converting large local datasets into databases to make it faster to retrieve subsets of interest.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.

If you have any query please feel free to call me or write to me. 

For More details contact at
Call: 020 66009116

Websites:

Friday 2 March 2012

Survival Analysis by Dr. David G. Kleinbaum and Prof. Matthew Strickland


Do telephone poles need to be treated with creosote every 2 years?  Or will every 5 years do? Do heart patients do better with plain stents, or stents coated with medication?  We typically measure the outcomes of treatments like this in terms of "survival times" (in medicine) or "time to event" (more broadly).  Our online course in the subject is called "Survival Analysis," by David Kleinbaum and Matthew Strickland, and runs from March 23 - April 20. For more details please visit at http://www.statistics.com/survival.

"Survival Analysis" describes the various methods used for modeling and evaluating survival data, also called time-to-event data.  Survival models are used in a variety of health and social sciences, including biostatistics, epidemiology, anthropology, sociology, psychology and economics.  In engineering applications, the topic is called "time-to-failure" analysis.  General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates.

Dr. Kleinbaum is internationally known for his textbooks in statistical and epidemiologic methods and as an outstanding teacher.  His popular text "Survival Analysis - A Self Learning Text" is the text for this course. He has also taught over 150 short courses over the past 30 years throughout the world.

Prof. Mathew Strickland is Assistant Professor in the Department of Environmental and Occupational Health at Emory University, and will be the primary discussion leader for this session of the course.  He has taught a variety of in-person and distance education courses on Epidemiologic Modeling, Fundamentals of Epidemiology, and Maternal/Child Health Epidemiology. Participants can ask questions and exchange comments with the instructors via a private discussion board throughout the period.

Aim of the course –
This course describes the various methods used for modeling and evaluating survival data, also called time-to-event data. Survival models are used in biostatistical, epidemiological, and a variety of health related fields. They are also used for research in the social sciences as well as the physical and biological sciences, including, economic, sociological, psychological, political, and anthropological data. Survival analysis also has been applied to the field of engineering, where it typically referred to as reliability analysis.

General statistical concepts and methods discussed in this course include survival and hazard functions, Kaplan-Meier graphs, log-rank and related tests, Cox proportional hazards model, and the extended Cox model for time-varying covariates. The course will also require participants to use a convenient statistical package (e.g., SAS, JMP, STATA, SPSS, R, or S+) to analyze survival analysis data.

Who should take this course –
Investigators designing, conducting or analysing medical studies or clinical trials. Researchers in any field (including engineering) working with data on how long things last.

Course Program:
Course outline: The course is structured as follows
SESSION 1:
  • An overview of survival analysis methods
  • Censoring
  • Key terms: survival and hazard functions
  • Goals of a survival analysis
  • Data layout for the computer
  • Data layout for theory
  • Descriptive statistics for survival analysis- the hazard ratio
  • Graphing survival data- Kaplan Meier
  • The Log Rank and related tests.

SESSION 2:
  • Introduction to the Cox Proportional Hazards (PH) model- computer example
  • Model definition and features
  • Maximum likelihood estimation for the Cox PH model
  • Computing the hazard ratio in the Cox PH model
  • The PH assumption
  • Adjusted survival curves
  • Checking the proportional hazard assumption
  • The likelihood function for the Cox PH model

SESSION 3:
  • Introduction to the Stratified Cox procedure
  • The no-interaction Stratified Cox model
  • The Stratified Cox model that allows for interaction

SESSION 4:
  • Definition and examples of time-dependent variables
  • Definition and features of the extended Cox model
  • Stanford Heart Transplant Study Example
  • Addicts Dataset Example
  • The likelihood functions for the extended Cox model.

The core strength of these online courses is the instructors. Statistics.com has over 60 instructors, who are recruited based in their expertise various areas in statistics. Most of these instructors are internationally-known experts in their field. These instructors are the subject experts with industry / consulting experience.

You will be able to ask questions and exchange comments with the instructors via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 4 weekly lessons and assignments, and require about 15 hours/week.  Participate at your own convenience; there are no set times when you must be online. You have the flexibility to work a bit every day, if that is your preference, or concentrate your work in just a couple of days.

For Indian participants statistics.com accepts registration for its courses at special prices in Indian Rupees through its partner, the Center for eLearning and Training (C-eLT), Pune (www.c-elt.com).

For India Registration and pricing, please visit us at www.india.statistics.com.

If you have any query please feel free to call me or write to me. 

For More details contact at
Call: 020 66009116

Websites: