Malaysia : Wind Energy Distribution Recent Development
Both wind speed and wind velocity related to the contribution are discussed. It involves a statistical analysis of its wind regime, the probability distribution of wind speed and velocity together with power analysis. A rigorous selection of the probability distribution leads to an unambiguous power analysis. The four well known distribution are Weibull, Gama, Inverse Gamma and Burr probability distribution. The wind power equation is derived through transformation method and the outcome of wind power analysis demonstrates the feasibility for the efficient extraction of wind energy in Malaysia.
Azami Zaharim worked first 13 years as a lecturer in the Universiti Teknologi MARA (University of MARA Technology - UiTM) before joining the Universiti Kebangsaan Malaysia (National University of Malaysia - UKM) in the year 2003. He obtained his BSc(Statistics and Computing) with Honours from North London University, UK in 1988 and PhD (Statistics) in 1996 from University of Newcastle Upon Tyne, UK. He specialize in statistics, public opinion, engineering education and renewable energy resources. In the year 2009 -2011, he headed the Head of Centre for Engineering Education Research and Built Environment (P3K). At the same time, he is currently active involve in outcome based education (OBE). He is also involved actively in the Renewable Energy Resources Analysis, Policy & Energy Management. He has until now published over 300 research papers in Journals and conferences, conducted more than 20 public opinion consultancies and delivered 16 keynotes/invited speeches at national and international meetings.”
Richard De Veaux
Data Science for all. Sure, but who, where, what, how and why?
In spite of the lack of consensus as to what Data Science actually is, universities across the country and around the world are rushing to create programs in Data Science at all levels. Is data science the mysterious intersection of computer science, statistics and domain expertise, or it is an overarching umbrella containing all of these disciplines? What might an introductory data science course look like and how might it fit in to a data science program? In this talk we’ll lay out the challenges for data science and examine some of the current trends.
Dick De Veaux is the C. Carlise and Margaret Tippit Professor of Statistics at Williams College. He holds degrees in Civil Engineering (B.S.E. Princeton), Mathematics (A.B. Princeton), Dance Education (M.A. Stanford) and Statistics (Ph.D., Stanford), where he studied with Persi Diaconis.
Previously, Dick taught at the Wharton School and the Engineering School at Princeton and has been a visiting researcher De Veaux has won numerous teaching awards from the Engineering Council at Princeton. He has won both the Wilcoxon and Shewell (twice) awards from the American Society for Quality, is a fellow of the American Statistical Association (ASA) and an elected member of the ISI. In 2006-2007 he was the William R. Kenan Jr. Visiting Professor for Distinguished Teaching at Princeton. In 2008 he was named the Statistician of the Year by the Boston Chapter of the ASA. He has served on the Board of Directors of the ASA and is the current Vice President.
Dick has been a consultant for many Fortune 500 companies, holds two U.S. patents, and is the author of more than 40 refereed journal articles. He is the co-author, with Paul Velleman and David Bock, of the critically acclaimed textbooks “Intro Stats”, “Stats: Modeling the World” and “Stats: Data and Models” and with Norean Sharpe and Paul Velleman of “Business Statistics” and “Business Statistics: A First Course”, all published by Pearson.
His hobbies include cycling, swimming, singing — and dancing (he was once a professional dancer and taught Modern Dance during Winter Study at Williams). He also teaches a course called “The history, geography and economics of the wines of France”. He is the father of four: two boys and two girls.
Data Literacy Should Not Be Optional: Implications for Statistics Educators and for IASE
Students of today will be consumers of statistical information whatever their future, confronted with data from many contexts and in many different forms. They will need to make decisions, which should be based on objective, analytical and data driven processes and not on human judgment. They will need to make sense of the world, able to process information and ask the right questions. Whether it is called statistical thinking, numeracy, or data literacy, the ability to reason with quantitative information is increasingly important in today’s world. One question is what core concepts are essential to produce critical consumers of data capable of thinking and reasoning in quantitative situations. Another is how to navigate educational systems that often have statistics as part of the curriculum, yet with a few exceptions, statistical literacy has never became part of the mainstream curriculum for all students. What can we as statistics educators do to confront these challenges and how can IASE help?
An Academic Specialist in the Program for Mathematics Education at Michigan State University, Gail Burrill was a secondary teacher and department chair for over 28 years. She is currently the President of the International Association for Statistical Education, a fellow of the American Statistical Association, and an elected member of the International Statistics Institute. She served as President of the National Council of Teachers of Mathematics (NCTM) and as Director of the Mathematical Sciences Education Board. Burrill received the Presidential Award for Excellence in Teaching Mathematics, the NCTM Life-Time Achievement Award, and the Ross Taylor /Glenn Gilbert National Council of Supervisors of Mathematics service award. She is currently the co-chair of the College Board’s Advanced Placement Calculus Development Committee and is a National Instructor for Teachers Teaching with Technology. Her research interests are statistics education, the use of technology in teaching mathematics and statistics, and the development of understanding of core mathematics and statistics concepts.