Roberto Altschul
Affiliate Associate Professor, Department of Statistics
Affiliate Assistant Professor, STEM, UW Bothell
email: altscr at uw dot edu


Course Information 
Class Time and Location. Tuesdays & Thursdays, 1:15 pm–3:15 pm UW2 031
Office Hours:  Truly House, Tuesdays and Thursdays 12:00-1:00
Midterms: see Schedule (find under the Files link on the left column)
Final: TBD

Required textbook 

  • Altschul, Roberto, Course Pack for STMATH 390, version November 16, 2017

Required  Software:

R, a free software environment for statistical computing and graphics.
•    Information on R can be found in the Comprehensive R Archive Network (CRAN) webpage: http://www.r-project.org/ 
•    To download a copy of R see the "Introduction/Review of R" by Professor Fritz Scholz.  A copy of this file is found by clicking on the Other Technical Files link on the left hand side of this web page.  
•    On the main left hand side of the CRAN webpage the links Manuals and Contributed will lead you to many documents on R (Introductions, tutorials, reference cards, etc).

Readings and Homework

A detailed weekly Schedule, showing reading and homework assignments, is provided on the Files link to the left of this web page.   You are expected to read ahead of the class meetings the sections in the Class Notes as suggested in the Weekly Schedule.  The best way to learn Probability and Statistics is to review the concepts and to study many examples and to solve many problems.  Before or as you are solving the homework problems, you may want to review similar examples from either the class notes or from other textbooks.   
We will have weekly HW assignments. You are urged to work individually. If you run into difficulties with a HW problem, before discussing it with a fellow student or with the instructor, find a similar one and discuss that one.   Do not stop with the problems in the HW assignment. You are urged to solve, or at least to sketch solutions to many of the problems as possible.


The course will start with of an introduction to R, probability spaces, random variables, expectations, distributions, etc. We will highlight the use of these concepts to model phenomena where uncertainty is present, with an emphasis on engineering and communications problems.  As an introduction to statistical analysis we discuss the laws of large numbers and the central limit theorem.  Next we introduce some statistical tools, e.g., exploratory, estimation, to analyze data and so confirm or review a model which might be under consideration.  Depending on time we may end with a short introduction to stochastic processes.

After successful completion of this course the student:
• Will have an understanding of essential topics in Probability and Statistics.
• Will be able to build probabilistic models for representative systems found in engineering and science.
• Understand how statistical methods are used to analyze data and evaluate hypothesized models.
• Can evaluate data from an engineering process, build a probabilistic model, and gain further knowledge about the process.
• Learn to use probability methods to compute solutions for a complex deterministic and/or random engineering process.

Calculus and Multivariate Calculus (limits, infinite series, partial derivatives, and multiple integrals). Familiarity with basic Linear Algebra and with Concepts of Elementary Probability would help but are not required.

Under the Math Diagnostic Test link on the left hand side of this webpage you will find the Math Diagnostic Test. You are not required to hand in your solutions; if you do I will review your answers and evaluate your strengths and weaknesses. This in turn will help you determine which areas to review in order to be successful in this course.


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Send questions about this workspace to Roberto Altschul.