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The aim of this course is to provide formal training for undergraduates in Physics on the foundations of statistical analysis of data. By the end of this course students should be able to appreciate measurement uncertainty, reproducibility, systematic errors, covariance matrices, the Gaussian, Binomial and Poisson distributions for measuring events, and to understand the concept of a test statistic for minimization, and understand various data mining algorithms. In addition to this, advanced understanding of errors will be tested through calculation of confidence intervals and upper limits.
The techniques covered in this course underpin the scientific process that has ultimately led to today's understanding of human knowledge. In addition to being fundamental to science, and in particular Physics, many of these methods are also useful in the financial world. Note that this course places it's emphasis on scientific applications of statistical data analysis.
The lecture timetables for this course can be found on the departments Information web page under the heading of Times and Dates. The Student Handbook also contains a summary of the course under the heading of Programme and Module information.
Course Assessment: 20% of the marks for this course will come from homework assignments, the remaining 80% will come from an exam. You are encouraged to work through past papers in order to test your understanding of course material. Please note that any instance of plagiarism will be dealt with in accordance with college regulations, and you will find more information on this in the student handbook.
The course organiser is Dr. A. Bevan. Please contact me if you have any questions with regard to this course.