Statistical Modeling Week 2008

October 20 - 23, 2008, Boston

Bridging the Gap Between Theory and Practice - Our 32nd Year!

Statistical Modeling Week is an annual conference sponsored by Statistical Innovations Inc., featuring applications-oriented seminars focusing on the latest trends in statistical analysis. Led by a group of experts in the field, the seminars teach professionals how to apply and understand today's major breakthroughs in statistical methodology. Statistical Modeling Week 2008 will take place in Boston on October 20 - 23, 2008. Whether you work in marketing, advertising, health, econometrics or evaluation, you will learn to apply these important breakthroughs to your everyday work. And you'll learn more than the techniques - you'll learn the theories behind them. Numerous leading organizations have taken advantage of our prior seminars - here is a sample list. Below are the course descriptions for Statistical Modeling Week 2008. You can also download our conference brochure. This year's conference will feature the following courses:

Courses

October 20, 2008. Introduction to Statistical Modeling Applications, Tony Babinec and Nicole Huyghe October 21 - 22, 2008. Latent Class and Finite Mixture Modeling, Jay Magidson and Tony Babinec October 23, 2008. Latent Class Choice and Hybrid / Data Fusion Models, Tom Eagle and Jay Magidson

Tuition and Enrollment Information

Tuition

October 20, 2008. Introduction to Statistical Modeling Applications	$895
October 21 - 22, 2008. Latent Class and Finite Mixture Modeling	$1,490
October 23, 2008. Latent Class Choice and Hybrid / Data Fusion Models	$895
Note: Discounts are available for multiple course registrations. After enrolling in the first course for an undiscounted tuition, you deduct $50 for each additional course you sign up for (the maximum discount would be $100 if all 3 courses are taken). Discounts are also available for additional people who sign up from your organization. Their tuition is $795 for each 1-day seminar and $1,390 for each 2-day seminar.

Enrollment

Register for the conference. For more information, e-mail or call us at (617) 489-4490.

Course Descriptions

Introduction to Statistical Modeling Applications

Instructors: Tony Babinec & Nicole Huyghe Description This important introductory seminar provides an overview of the major kinds of regression measurements, and segmentation approaches and their applications. It also illustrates the utility of montecarlo studies to calculate power and determine minimum sample sizes. The Program Model Types -- Drivers of satisfaction / loyalty Segmentation Modeling Variables and coding issues Overview of: Discriminant analysis Regression (linear & logistic) MaxDiff Factor analysis CHAID Cluster analysis Power calcs & sample size determination Simulating data from models What you will learn How to choose the appropriate model for different applications About useful LG-EquationsTM specifications Why simulate data from models Use of CHAID as a supplementary technique Techniques appropriate for loyalty studies Use of Gains chart to compare results between tree based and regression approaches Key Driver regression with rating scale data Differences between stated and derived importance and related scale problems How to reduce scale problems with MaxDiff Back to Courses Register

Latent Class and Finite Mixture Modeling

Instructors: Jay Magidson & Tony Babinec Description Interest in Latent Class (LC) models is increasing rapidly. This is due to the growing evidence that they provide better solutions than traditional approaches to cluster, factor and regression analysis when the population is not homogenous. In this 2-day course we introduce LC as a probability model and describe various applications using the newest version of Latent GOLD®. On day 1, we focus on model fitting strategies and the interpretation of output. On day 2, we consider several advanced topics including identification issues, random effects with continuous factors, repeated observations, hidden (latent) Markov models for latent growth and latent transition analysis, and the computation of individual-level coefficients. The Program Day 1 Basic ideas of latent class analysis The concept of local independence The general probability model Handling nominal, ordinal, continuous and count variables LC measurement models Discrete vs. continuous factor analysis LC regression and segmentation models Inclusion of covariates in LC models Model-based clustering / latent discriminant analysis Comparing models and assessing fit Uni-plot, bi-plot, tri-plot and profile plot Case studies and computer demos Model specification using LG-EquationsTM Day 2 Identification problems and boundary solutions Use of Bayes constants to eliminate boundary solutions The problem of local solutions Use of randomized start values Use of the bootstrap p-value Bivariate residuals to diagnose local dependencies Repeated measures / conjoint marketing LC Growth models and latent transition models Use of continuous factors as a random intercept What you will learn How to specify LC cluster, factor and regression/segmentation probability models. Interpreting graphical displays of results. What to look for when interpreting output. Strategies for assessing model fit with sparse and non-sparse data. How to isolate the scale effects in ratings data. How to include nominal, ordinal and continuous variables in the models. Why LC models improve over K-means clustering. Back to Courses Register

Latent Class Choice and Hybrid / Data Fusion Models

Instructors: Thomas Eagle & Jay Magidson Description Latent Class (LC) choice models that identify segments differing in preferences are widely used to forecast market share, design optimal products and services, and more. This course begins by introducing the theory and practical applications of these models in traditional choice, ranking and constant sum experiments in conjunction with the latest version of the Latent GOLD Choice program. We then show how such models can be extended to separate out potentially confounding scale factors as well as incorporate additional data (i.e., data fusion applications). Specifically, we analyze a combination of Choice and Ratings data, as well as MaxDiff and Ratings data. The results provide not only meaningful part-worth utilities but also absolute rating (e.g., likelihood to purchase) levels for each LC segment. The Program Development of Excel-based simulators Stated preference vs. revealed preference data Experimental designs for stated preference Independence of Irrelevant Alternatives (IIA) Accounting for segment differences -- HB) models What you will learn Advantages and limitations of discrete choice models How to develop a .xls simulator Which choice models to use How to include ratings data in LC Choice models How to specify, estimate, and interpret the results from choice models Why real-world data violate IIA and how LC modeling resolves this problem Back to Courses Register

Other Presentations

Check out information on other presentations and seminars conducted by Jay Magidson.