This course provides an introduction to the theory and practice of panel-data analysis. After introducing the fixed-effects and random-effects approaches to unobserved individual-level heterogeneity, the course covers linear models with exogenous covariates, linear models with endogenous variables, dynamic linear models, and some nonlinear models.
In diesem Kurs werden wir grundlegende Aspekte der Studienplanung und statistischer Datenauswertungen besprechen. Was sollten Sie bei der Studienplanung und der Datenauswertung beachten? Warum gelangen zwei Studien zur gleichen Fragestellung zu unterschiedlichen Ergebnissen? Welche Studienergebnisse sind „bedeutsamer“ als andere und welche Aspekte sollten Sie beim Lesen und Interpretieren statistischer Ergebnisse berücksichtigen? Diese Fragen stehen im Mittelpunkt und werden von uns anhand von medizinischen sowie wirtschafts- und sozialwissenschaftlichen Studien näher erläutert.
Via a series of case studies, this webinar demonstrates multifactor testing tools for aerospace R&D. See how Design-Expert empowers experimenters to quickly converge on the “sweet” spot—factor settings that meet all specifications. Engineers and scientists working in the aircraft, space and defense industries will do well by attending this briefing on design of experiments for screening and characterization, response surface methods for optimization (e.g., wing design), and mixture design for optimal formulation (e.g., composites).
Step up your design of experiments (DOE) know-how via this essential briefing on this multifactor-testing tool. A quick demo lays out what makes statistical DOE so effective for accelerating R&D. Discover how:
• Traditional one-factor-at-a-time scientific methods fall flat
• DOE will find your vital few factors and reveal breakthrough interactions
• To strategically choose the right design at just the right time
• Graphical tools point out the right directions and map out your sweet spot
The fuel provided in this 1-hour webinar will kick-start your first designed experiment. Get up to speed, enroll now!
Learn how and when to use Stata’s treatment-effects estimators to analyze treatment effects in observational data. Use regression adjustment, inverse probability weights, doubly robust methods, propensity-score matching, and covariate matching to estimate average treatment effects (ATEs) and ATEs on the treated. We will cover the conceptual and theoretical underpinnings of treatment effects as well as many examples using Stata.
Learn how to use all of Stata's tools and become a sophisticated Stata user. You will understand the Stata environment, how to import and export data from different formats, how Stata's intuitive syntax works, data management in Stata, matching and merging, how to analyze subgroups of data, how to reproduce your work and document it for publication and review, how to interact with the Stata community online, and more.
Become an expert in organizing your work in Stata. Make the most of Stata's scripting language to improve your workflow and create concretely reproducible analyses. Learn how branching, looping, flow of control, and accessing stored estimation results can speed up your work and lead to more complete analyses. Learn about bootstrapping and Monte Carlo simulations, too.
Learn how to communicate your data with Stata's powerful graphics features. This course will introduce different kinds of graphs and demonstrate how to use them for exploratory data analysis. Topics include how to use graphs to check model assumptions; how to format, save, and export your graphs for publication using the Graph Editor.
Course registration is binding. Upon cancellation more than 8 working days before the course start date, we invoice 50% of the course fee. Upon cancellation less than 8 working days before the course start date, we invoice the full course fee. Click here to read the full Terms and Conditions!