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Multivariate Analysis Techniques Registration Will BeHe has served on the faculties of the University of Wisconsin, Massachusetts Institute of Technology, Old Dominion University and North Carolina State University. Dr. LaBudde is currently Adjunct Professor of Statistics at Old Dominion University. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course. For Statistics 1 Probability and Study Design, take this assessment test. For Statistics 2 Inference and Association, take this assessment test. These courses are not required as eligibility to enroll in this course, and are presented here for information purposes only. Methods discussed include hierarchical clustering, k-means clustering, two-step clustering, and normal mixture models for continuous variables. Students may cancel, transfer, or withdraw from a course under certain conditions. If youre not satisfied with a course, you may withdraw from the course and receive a tuition refund. Multivariate Analysis Techniques Full Sequence OfTopics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics. During each course week, you participate at times of your own choosing there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor. At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers. Other software may be used, but you should be prepared to use your program and interpret its output (in comparison with that given in the course) on your own. If you are planning to use R in this course and are not already familiar with it, please consider taking one of our courses where R is introduced from the ground up: Introduction to R: Data Handling, Introduction to R: Statistical Analysis, or Introduction to Modeling. R has a learning curve that is steeper than that of most commercial statistical software.
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