Calculus Review

Is your calculus rusty?  Refresh it with “Calculus Review,” which is a refresher in calculus. Calculus at this level concerns itself with three primary concepts: the limit, the derivative and the integral. Applications involving statistics supplement the more traditional calculus examples. Participants will learn how to find and interpret derivatives and integrals, both by hand and via software. Learn “Calculus Review” in Dr. John Verzani’s online course at Statistics.com. For more details please visit at http://www.statistics.com/calculus/.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Calculus Review, Limits, The Derivative

• Pre-Calculus Concepts
• Limits
• Differentiation
• Computer Examples

SESSION 2: Applications of the Derivative, The Integral

• Applications
• Area and the Riemann Sum
• Computer Examples

SESSION 3: Integration

• Fundamental Theorem of Calculus
• Antiderivatives: Evaluation of Indefinite Integrals
• Applications
• Computer Examples

The instructor for "Calculus Review," John Verzani is a member of the faculty at the College of Staten Island of the City University of New York, and the author of "Using R for Introductory Statistics" (CRC Press). His research interests and publications are in the area of superprocesses. 

You will be able to ask questions and exchange comments with the Dr. John Verzani 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.

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

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com

Environmental Statistics

How do you know if the pH level in a lake is changing?  How should you design a study to determine whether a site has been damaged by mining?  Learn more about the application of statistical methods to environmental problems in Dr. Bryan Manly's online course, "Environmental Statistics," at Statistics.com. For more details please visit at http://www.statistics.com/enviro/.

"Environmental Statistics" is designed for the environmental analyst or scientist with some previous exposure to statistics.  It reviews general statistical procedures (sampling, regression, analysis of variance, control charts) from an environmental science angle, and also covers topics that are specific to environmental impact and risk assessment.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Background and Sampling

• Examples illustrating the role of statistics in environmental science.
• Optional review of basic statistical concepts for those who need it.
• Environmental sampling strategies and the analysis of the data obtained: random sampling, stratified sampling, systematic sampling, ratio estimation, double sampling, choosing sample sizes, the Data Quality Objectives (DQO) process

SESSION 2: Environmental Data Analysis

• Models for data: standard statistical distributions, the linear regression model, analysis of variance, generalized linear models for non-normal data
• Statistical inference: different types of study (observational and experimental), quasi-experiments, design-based and model-based inference, tests of significance and confidence intervals, computer-intensive methods, avoiding pseudoreplication, multiple testing procedures, meta analysis, Bayesian methods
• Using analysis of variance with environmental monitoring data

SESSION 3: Monitoring and Impact Assessment

• Using control charts and CUSUM charts with monitoring data
• Designs and analyses for impact assessment: BACI designs, impact-control designs, before-after designs, impact gradient designs, possible inferences. Assessing site reclamation: problems with usual tests of significance, testing for bioequivalence
• Introduction to time series analysis: components of time series, serial correlation, test for randomness, detection of change points and trend, more complicated time series models.

SESSION 4: Spatial and Censored Data, and Monte Carlo Risk Assessment

• Spatial data analysis: types of spatial data, analysis of count data, randomness in where points are located, the Mantel randomization test for spatial correlation, the geostatistical methods (variograms and kriging), correlation between variables in space
• Censored data: estimation of the population mean and variance from a single sample, estimation of quantiles, comparing the means of two or more samples, regression
• Principles of Monte Carlo risk assessment
• Using Resampling Stats for Excel for risk assessment.

Dr. Bryan Manly is a consultant with Western EcoSystem Technology, Inc. in Cheyenne, Wyoming. Before that, he was Chair of Statistics and Director of the Center for Applications of Statistics and Mathematics at the University of Otago, New Zealand.  Dr. Manly is the author of "Statistics for Environmental Science and Management," "Randomization, Bootstrap and Monte Carlo Methods in Biology," "The Statistics of Natural Selection," "Multivariate Statistical Methods," "Resource Selection by Animals," "The Design and Analysis of Research Studies," and over 200 articles in refereed journals. Participants can ask questions and exchange comments with Dr. Manly via a private discussion board throughout the period.

You will be able to ask questions and exchange comments with the Dr. Bryan Manly 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.

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

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com

Designing Valid Statistical Studies

Statistical studies gone wrong:  a famous example is the decline in coronary heart disease among women taking hormone replacement therapy (HRT), reported in numerous observational studies.  The effect disappeared in later randomized trials - it was likely due to a third correlated factor (socioeconomic status). 

Learn more about how to design a valid statistical study from the ground up in Dr. David Kleinbaum's and Dr. Mildred Maisonet's online course "Designing Valid Statistical Studies," at Statistics.com. For more details please visit at http://www.statistics.com/validstudies.

"Designing Valid Statistical Studies," by Dr. David Kleinbaum and Dr. Mildred Maisonet, emphasizes the underlying concepts and methods for addressing validity and bias issues in statistical research. Topics covered in the course include: overview of validity and bias, selection bias, information bias, and confounding bias.

Who Should Take This Course:

Researchers who need to design, or review the designs of, statistical studies.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Review Types of Studies, Validity

• Methodologic issues
• Clinical trial (brief overview)
• Observational
• Case-control
• Cross-sectional
• Validity vrs. precision
• Internal vrs. external validity

SESSION 2: Bias and Confounding

• Selection bias (brief overview)
• Information bias (brief overview)
• Confounding

SESSION 3: Controlling Extraneous factors

• At design stage
• At analysis stage
• Randomization
• Matching

SESSION 4: Matching and Stratification

Dr. David Kleinbaum (Emory University) is internationally known for his textbooks in statistical and epidemiologic methods and as an outstanding teacher.  He is the course designer, and the author of "Epidemiologic Research - Principles and Quantitative Methods", "Survival Analysis- A Self Learning Text," and other texts. 

Dr. Mildred Maisonet, who will lead the class discussions in this session of the course, is an Adjunct Assistant Professor at Rollins School of Public Health, Emory University and an Epidemiologist at TKC Integrated Services, LLC.  Participants can ask questions and exchange comments with her via a private discussion board throughout the period.

You will be able to ask questions and exchange comments with the Dr. Mildred Maisonet  and Dr. David Kleinbaum 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.

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

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com

Sentiment Analysis

Mike Dewar, data scientist at bitly, described sentiment analysis recently as "rubbish," but noted that lots of it is being bought and sold.  Sentiment analysis is the relatively new art and science of distilling useful data from this mass of unstructured text.  If you are engaged in buying or using sentiment analysis, shouldn't you learn something about how it works?  Nitin Indurkhya's online course in Sentiment Analysis is coming up soon. For details please visit at http://www.statistics.com/sentiment-analysis/.

"Sentiment Analysis" introduces you to the algorithms, techniques and software used in sentiment analysis. Their use will be illustrated by reference to existing applications, particularly product reviews and opinion mining. The course will try to make clear both the capabilities and the limitations of these applications. For real-world applications, sentiment analysis draws heavily on work in computational linguistics and text-mining. At the completion of the course, a student will have a good idea of the field of sentiment analysis, the current state-of-the-art and the issues and problems that are likely to be the focus of future systems.

Who Should Take This Course:

Analysts, researchers and managers who deal with, or might need to deal with, Sentiment Analysis systems at a variety of levels - needs assessment, design, deployment and operation.

Course Program:

Course outline: The course is structured as follows

SESSION 1: Introduction and Subjectivity Analysis

• Text Categorization concepts
• Computational Linguistics concepts
• Subjectivity Analysis

SESSION 2: Sentiment Extraction

• Topic Extraction
• Product Reviews
• Sentiment analysis of Comparative Sentences
• Applications

SESSION 3: Opinion Retrieval and Spam

• Searching for Opinions
• Opinion Summarization
• Opinion Spam
• Applications

Nitin Indurkhya is co-author of "Text Mining" (Springer), and co-editor of the "Handbook of Natural Language Processing" (CRC). Dr. Indurkhya is the founder and president of Data-Miner Pty Ltd, an Australian company engaged in data-mining consulting and education, and has served as Principal Research Scientist at eBay and Professor at the School of Computer Science and Engineering, University of New South Wales (Australia).  Participants can ask questions and exchange comments directly with Dr. Indurkhya via a private discussion forum throughout each course.

You will be able to ask questions and exchange comments with the Dr. Nitin Indurkhya via a private discussion board throughout the course.   The courses take place online at statistics.com in a series of 3 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.

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

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com

Quasi-Least Squares Regression

"Quasi-Least Squares Regression" extends longitudinal methods for the analysis of correlated data.

Examples of correlated data include, but are not limited to, clustered data, repeated observations, longitudinal data, multiple dependent variables, spatial data or data from population pharmacokinetic/pharmacodynamic studies.  Extra emphasis will be given to engaging in various types of modeling projects, with class and instructor discussion regarding the most appropriate ways to select an appropriate model for a given data situation, methods of constructing a model, interpreting a model, and evaluating a model for its comparative fit.  "Quasi Least Squares Regression" will be taught at The Institute for Statistics Education at Statistics.com.  The instructors are Dr. Joseph Hilbe and Dr. Justine Shults, and the course will be based on their forthcoming book on the subject. For more details please visit at http://www.statistics.com/qls-regression.

Joseph Hilbe, elected ASA Fellow and the author of "Logistic Regression Models," and also "Generalized Linear Models and Extensions" and "Generalized Estimating Equations," was, until recently, the software reviews editor for "The American Statistician."  

Justine Shults, Associate Professor of Biostatistics at the University of Pennsylvania, is a co-investigator on studies in pediatrics, nutritional epidemiology, and psychiatry.

Hilbe and Shults are co-authors of the forthcoming CRC text "Quasi Least Squares Regression."

Who Should Take This Course:

Any analyst who needs to develop, interpret or assess models for complex multivariate data structures - e.g. clustered data, repeated observations, longitudinal data, multiple dependent variables, spatial data or data from population pharmacokinetic/pharmacodynamic studies.

Course Program:

Course outline: The course is structured as follows

SESSION 1: History and Theory of QLS Regression

• Use of QLS in biomedical research and business analytics
• How QLS compares with some popular competing approaches, including mexed-effects models

SESSION 2: Correlation Structures for Clustered and Longitudinal Data

• Analysis of cross-sectional and clustered data (e.g. clinical data that are collected on a family who are measured at one time point; customer satisfaction scores that are measured in different departments of a store during a one-day sale)
• Analysis of equally-spaced longitudinal data (e.g. clinical trials with measurements collected at baseline and at 6 and 12 months; monthly hospital pricing data)
• Analysis of unequally-sapced longitudinal data (e.g. clinical trials with unequally-spaced measurements)

SESSION 3: Analysis of Data with Multiple Sources of Correlation

• Analysis of clustered data measured over time, such as
• Adverse events measured on different body systems of patients at several measurement occasions
• Montly customer satisfaction scores in several departments of a store

SESSION 4: Analysis of Model Fit

• Choice of variables to include in the regression model, e.g. age, gender,  income, severity of disease...
• Adequacy of assumed correlation structure
• Sensitivity of results when compared with fitting other popular approaches such as mixed 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.

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

Email: info@c-elt.com

Call: 020 66009116

Websites:

www.india.statistics.com

www.c-elt.com