MATH 3342 (Trindade) -- Mathematical Statistics for Engineers & Scientists -- fall 2021

Basic Information

Course Meets: TR 09:30-10:50 in Animal & Food Science 102.

Text Books

Lecture Notes

These slides cover all the material in the course, but about 50% of them contain blank spaces to be filled in class. These incomplete slides are marked with a red asterisk [*].

Course Objectives and Syllabus

This course covers mathematical theory and methods of statistical inference at a basic undergraduate level, corresponding to Chapters 1-4, and 6-9 of the book. Calculus III (MATH 2450) is a prerequisite. After introducing probability and distribution theory, these concepts are used to develop the main tools of statistical inference: estimation, confidence intervals, and hypothesis tests. A specific syllabus with approximate coverage timeline is as follows (based on MWF schedule):

Expected Student Learning Outcomes

Students will apply their calculus knowledge to learn the meanings of, and computational procedures relating to, basic statistical concepts used for making decisions in the sciences and engineering. In particular, students will:

Methods of Assessing the Expected Learning Outcomes

Continuous formative assessment of the progress of the course will occur via ongoing communication between the instructor and the students. to this end all students are encouraged to ask questions during class and to seek the instructor's help outside class. The expected learning outcomes for the course will be assessed through: semester tests and a final exam, homework assignments, and class discussion. The course grade will be determined from homework sets (15%), three (3) semester tests (20% each), and a comprehensive final exam (30%). The traditional grading scale will be used: The grade weighting scheme allows for a maximum of 5% extra credit to be counted toward the overall grade. Firm test dates are as follows: Test grades will be posted on WebAssign.

Homework Problem Sets

There will be chapter Assignments administered through the online grading system WebAssign (which will show the exact due date/time). The due dates for each Assignment will be approximately one week after coverage of the relevant material in class. All the problem sets are already available, and can be worked on at any time. You will no longer be able to work on sets past the due date (visible in WebAssign). Keep the following in mind:

Instructions on Accessing WebAssign

Go to WebAssign and follow the instructions for self-enrolling with a CLASS KEY as follows: Read the Student Quick Start Guide which also explains different payment options for obtaining access. WebAssign also offers extensive online support.


Statistical Computing

In this course we will not use, but only mention statistical computing software packages, such as SAS, Minitab, SPSS, and R. Those wishing to learn and explore more on this can access some of my statistical computing resources here, particularly R.
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