The overall objective of this course is to familiarize students with the basic concepts and ideas of statistics and probability. Also, to provide training in the use of statistical methods and graphics for the analysis and presentation of data encountered in the sciences and engineering.
The free software R, used by over 90% of statistics graduate students for their research programming needs, will be used as an integral part of the course.
The particular topics covered are:
- Basic concepts of probability and statistics (sample vs population, simple random sampling, sample and population mean, variance and percentiles, graphical statistics, comparative studies and experimental design).
- Probability and conditional probability.
- Univariate and multivariate distributions, correlation and regression.
- The Central Limit Theorem.
- Basic concepts of estimation, conﬁdence intervals and hypothesis testing for one sample and regression.
- Comparison of two means and two proportions (independent and paired data), including rank tests.
- Comparison of more than two means and proportions. Bonferroni and Tukey simultaneousconﬁdence intervals and multiple comparisons, and rank methods.
Dr. Michael Akritas is the primary author of the materials for this course and has taught this course in residence for many years.
Students must have immediate access to a printer/scanner in order to scan hand written assignments into .pdf documents and upload them into Canvas.
Akritas, M., (2015). Probability & Statistics with R for Engineers and Scientists, 1st edition, Pearson, ISBN-13: 978-0321852991.
The official prerequisites are 3 credites of calculus (e.g., MATH 111 or MATH 141) If it has been some time since you’ve studied calculus, you might want to get yourself a good reference and do some quick reviewing. The calculus techniques most frequently used in the course include: differentiation, integration, series, and limits, (See Review).