Welcome to our class website. I will use this site to supply material for the class. We will use I-learn 3.0 as well. This is my second year of teaching at BYU-I and my first time teaching this specific course at BYU-I. I enjoy the learning process associated with introductory statistics classes and anticipate a fun semester with you this semester.

Statistics and Engineering

Over the last decade, I have spent much of my time working with engineers and economists to tackle complex problems. I have found the experience to be rewarding and invigorating. I learned it was vital to learn how others used language to convey topics. I found that we were all speaking English, but that we had learned very specific definitions in our domains that were different than the general language definition. At times, each domain’s specific definition were very different from each other.

I hope that this class will introduce you to the language of statisticians and empower you to understand the implications of variability in all aspects of our lives.

In 1986 Arno Penzias, the VP of Research at Bell Labs, said, “The competitive position of industry in the United States demands that we greatly increase the knowledge of statistics among our engineer graduates.” This fact has dramatically increased in the last three decades.

Course Objectives

We will meet the MATH 330 course objectives stated by the Mathematics department in conjuction with the engineering department. I have included an even more generic set of course goals and written the specific measures/implementations of the BYU-I MATH 330 course objectives. The text below is what will guide our class this semester.

General Course Goal

Help non-statistician quantitative focused degree students (Engineers, Applied Math, CS)

  • use statistics as a bridge between imprecise measurements of, or knowledge about, a system and the truths of the system (ALT Writing: understand the principles of data analysis and decision making when uncertainty exists in engineering based applications),
  • understand how to communicate/interact with statisticians and statistical software when working in teams.

Measures of Course Goal

  1. Demonstrate an understanding of the fundamentals of data driven inference. These include the difference between a population and a sample, between parameters and statistics, and the use of descriptive statistics and graphical summaries.
  2. Use statistics for inferential decision making with confidence intervals and hypothesis tests under different statistical methods.
  3. Be able to recognize data/situations that necessitate varied discrete and continuous probability functions (e.g Poisson, Normal, Binomial, Hypergeometric, Log-Normal, Gamma).
  4. Given bivariate data, perform linear regression and interpret the results.
  5. Learn the basics of statistical software (R for our class) for data manipulation, visualization, and analysis. Understand how to read output from statistical software.
  6. Apply statistical techniques for Quality Control (QC) and Design of Experiments (DOE) with engineering based data and applications.

Course Materials

The two primary pages that will be updated weekly are the homework page and the class meeting page

Please read the syllabus and review the material on R Analysis Languange.