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A search of MERLOT materialsCopyright 1997-2014 MERLOT. All rights reserved.Sun, 26 Oct 2014 00:08:04 PDTSun, 26 Oct 2014 00:08:04 PDTMERLOT Search - category=2827&materialType=Online%20Coursehttp://www.merlot.org:80/merlot/images/merlot.gif
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4434Dr. Stat: an Online Statistics Course for Social Scientists
http://www.merlot.org/merlot/viewMaterial.htm?id=413277
Dr. Stat is an online statistics course for higher education students. Originally designed for students of Psychology, it can be used for any student of Social Sciences and gives a first introduction into statistics, either as a self study tool or as an exercise tool besides another statistics course. It also provides a great resource when working on a resource project.There are 25 lessons about basic subjects such as measurement levels, measures of central tendency, correlation, probability and distributions, and more advanced subjects as hypothesis testing, measures of association and variance analysis. You can follow each lesson separately. All lessons contain a great number of exercises.Students report great enthusiasm for the course. Especially those Social Science students who have trouble with the subject and would like extra practice appreciate the clear straightforward explanations and the exercises.NOTE: The first lesson can be used for free, without registration. All others require an account that costs €7.50. Payment can be done through Paypal.11.220 Quantitative Reasoning & Statistical Methods for Planners I
http://www.merlot.org/merlot/viewMaterial.htm?id=555072
This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice.9.07 Statistical Methods in Brain and Cognitive Science
http://www.merlot.org/merlot/viewMaterial.htm?id=555068
This course emphasizes statistics as a powerful tool for studying complex issues in behavioral and biological sciences, and explores the limitations of statistics as a method of inquiry. The course covers descriptive statistics, probability and random variables, inferential statistics, and basic issues in experimental design. Techniques introduced include confidence intervals, t-tests, F-tests, regression, and analysis of variance. Assignments include a project in data analysis.9.520 Statistical Learning Theory and Applications
http://www.merlot.org/merlot/viewMaterial.htm?id=554609
This course is for upper-level graduate students who are planning careers in computational neuroscience. This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification, and Bioinformatics. The final projects, hands-on applications, and exercises are designed to illustrate the rapidly increasing practical uses of the techniques described throughout the course.9.520 Statistical Learning Theory and Applications
http://www.merlot.org/merlot/viewMaterial.htm?id=554693
Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification. Derives generalization bounds using both stability and VC theory. Discusses topics such as boosting and feature selection. Examines applications in several areas: computer vision, computer graphics, text classification and bioinformatics. Final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.9.63 Laboratory in Cognitive Science
http://www.merlot.org/merlot/viewMaterial.htm?id=555036
Teaches principles of experimental methods in human perception and cognition, including design and statistical analysis. Combines lectures and hands-on experimental exercises; requires an independent experimental project. Some experience in programming desirable. To foster improved writing and presentation skills in conducting and critiquing research in cognitive science, students are required to provide reports and give oral presentations of three team experiments; a fourth individually conducted experiment includes a proposal with revision, and concluding written and oral reports.AP3021 Advanced Statistical Mechanics
http://www.merlot.org/merlot/viewMaterial.htm?id=555006
Ensemble theory; noninteracting classical and quantum systems; cluster expansion for interacting systems, many body quantum mechanics, phase transitions, scaling, renormalisation; nonequilibrium thermodynamics; Boltzmann transport equation. Study Goals: The student who passes this course should have a working knowledge of statistical mechanics on the intermediate level. The course topics are: ensemble theory, non-interacting particles (quantum and classical), interacting particles (quantum and classical), phase transitions and nonequilibrium phenomena (transport). At the end of the course, the student has an broad view of the theory and he or she is able to solve problems pertaining to the topics covered in this course. Students should also be able to present their solutions to their fellow students in a clear way. Assessment: In this course, there is strong emphasis on problem solving skills. Every week, the students will study at least one problem in some detail. The participation during the exercise classes counts to the final mark. There will however also be a final written examination which should be doable for everybody who has participated properly in the course. The exercises will then be used to round off the mark if necessary. Students who want to pass the course without problem solving can do a separate exam (at the same time) which will be more elaborate than the standard exam. It is not recommended to try passing this course via this elaborate exam :-).Bioinformatics and Computational Biology Solutions Using R and Bioconductor
http://www.merlot.org/merlot/viewMaterial.htm?id=554657
Covers the basics of R software and the key capabilities of the Bioconductor project, including importation and preprocessing of high-throughput data from microarrays and other platforms.Biostatistics for Medical Product Regulation
http://www.merlot.org/merlot/viewMaterial.htm?id=554770
Provides a broad understanding of the application of biostatistics in a regulatory context.Biostatistics Lecture Series
http://www.merlot.org/merlot/viewMaterial.htm?id=554607
Addresses topics that commonly arise from the day-to-day collaboration between researchers in public health and Biostatistics at the School.