2013-2014 Teaching Log for Diane L. Evans, Ph.D.

Summer Quarter 2013: MA 381: Probability with Statistical Applications Online, 2 Sections

Level: Sophomore/Junior; Enrollment: 20 Students; Class Meeting Hours: 40

Two summers ago, I decided to add a new asset to my teaching toolbox; I began teaching an advanced level mathematics

course in Probability online. The need came from students involved in summer internships (over 90% at Rose-Hulman) who

wanted the opportunity to take the class while working. There was a definite need, and I was excited to try a new style of

teaching. The class “met” for 10 weeks, and I provided online material for 40 class sessions. I posted my lectures and solutions

online daily, along with 83 screen capture and live lecture and problem solving videos. The YouTube site of videos is:

https://www.youtube.com/playlist?list=PLBK7yyieyrAbEH1jqkgCaGNn2Q9vlwn1s. Since posting them, most videos have

one to two hundred views, while others have thousands. I have enjoyed hearing from people all over the world who are

learning probability by watching these videos!

The general topics of the course include Axioms of Probability, Combinatorial Methods, Conditional Probability,

Independence, Distribution Functions, Discrete and Continuous Random Variables, Bivariate Distributions, Expected Values

and Variances of Sums of Random Variables, Conditioning on Random Variables, Moment Generating Functions, the Central

Limit Theorem, and Statistical Applications.

Unlike many online courses, there was a lot of communication between my students and me. By the end of the summer, I had

answered 387 emails about course material that were shared with the entire class. While some emails only took a few minutes

to answer, the majority were detailed discussions about problems. It is difficult in probability to “see” some problems without

additional pictures or explanations. I relied on students to inform me when they needed further explanations or guidance. My

responses to their emails (with sender removed) would be in red so that they knew when I was “talking.” I stayed well-

connected with students who were spread out all over the country, including 3 students who took trips to China and one who

was finishing an internship in Germany. I was asked to help develop our online teaching program during this past academic

year.

Fall Quarter 2013: MA 381: Probability with Statistical Applications, 2 Sections

Level: Sophomore/Junior; Enrollment: 47 Students; Class Meeting Hours: 40

This is the same course that I taught in the summer, but I would like to expand on some of the details. My course management

site contains up-to-date copies of lecture notes, worksheets, data sets, homework, and simulations. I make myself available

to students via email to answer questions at late hours (e.g., 10 p.m. – 1 a.m.), especially at times when I know they will be

doing homework. This communication has worked well, and students appreciate receiving quick responses to their queries. I

have also started using Piazza, which is an online communication site in which students can post and answer each other’s

questions anonymously. It has been a splendid addition! The site is a nice way for students to communicate with each other

about problems and to get advice from their peers. The forums have lightened my load in responding to individual questions

at late hours; though I still try to enter discussions when appropriate.

I have always provided lecture notes for my classes because I feel they are helpful to student learning. I want their attention

on the material while they are in class, not on writing definitions and theorems from the board. I do leave space in the notes

for them to work out examples, so they do need to actively participate to fill in the gaps. I work hard to write my own lecture

note problems, as well as homework, exam, and bonus problems. The problems often contain situations involving my current

students and their activities, such as softball, visiting Germany, working at the homework hotline, or building a human

powered vehicle. Students are delighted to see their names in print associated with real-world problems involving probability.

I also frequently give bonus problems on assignments to stretch their thinking beyond the basics of standard problems. Here

is an example of one such fun bonus problem:

BONUS. Cinderella (in her spare time from cleaning before becoming a princess) is a card shark. Suppose she heartily

shuffles a deck of playing cards and then lays out all 52 cards (in order) on the wet, cold basement floor. Is it likely that she

is the first person in history to achieve this particular ordering of the cards? Using mathematics, explain why you believe she

is or is not the first person to do so.

Fall Quarter 2013: MA 387: Statistical Methods in Six Sigma, 1 Section

Level: Junior/Senior; Enrollment: 28 Students; Class Meeting Hours: 40

In 2012, I began teaching a course in Six Sigma to give our engineering students entering the workforce a solid background

in the statistical practices that they would see in industry. I knew Six Sigma was a class that would benefit them in their job

searches and careers, and I set out to get my own Black Belt certificate in Six Sigma in Summer 2011 (Purdue University).

The enrollments in both the Quality Methods and Six Sigma courses have increased dramatically over the years. Currently,

I teach between 60 and 80 students a year in each course. (In the early 2000’s, the enrollment was lucky to cap at 12.) Students

take this course as a math elective, and all of them have Engineering Statistics I as a prerequisite. Approximately a dozen or

more of these students are now going on to pursue careers as Quality Engineers upon graduation. For the students not pursuing

careers in quality, the courses are giving them a step up in their job searches because of their knowledge of Six Sigma and

their experiences working on real-world projects with statistical applications.

The general topics of the course include the Define, Measure, Analyze, Improve, Control (DMAIC) Methodology, Voice of

Customer, Affinity Diagram, Critical to Quality Diagram, Computation of Sigma Levels, Spaghetti Diagram, House of

Quality, Process Map, Cause and Effect Diagram, Attribute Agreement Analysis, Variable Gage Repeatability and

Reproducibility, Control Charts, Capability Analysis, Hypothesis Tests, Confidence Intervals, Failure Modes and Effects

Analysis, Regression, and Design of Experiments. I have written my own notes that serve as a textbook for this class since

many Six Sigma textbooks do not contain a broad and deep coverage of many of the statistical topics. I supplement my notes

with daily handouts of current articles, typically ones that I have read in the past week. Six Sigma is truly one of the “hottest”

topics around that intersects with engineers’ interests.

As a part of the course, teams of 3-4 students are required to do an improvement project using the DMAIC methodology. The

statistical methods that they learn in each DMAIC phase are applied to their projects. In the past year, some of the projects

that have been done include reducing cafeteria food waste, removing invasive plants from campus, reducing the wait time

and increasing the number of customers at our on-campus coffee shop, reducing electricity usage in the dorms, reducing the

cost of housekeeping in the dorms, and increasing the number of clients seen by our counseling service office.

A student from this class presented the food waste project with me at the Statistics in Practice Conference in New Orleans in

February 2013. The work was again presented at the Joint Statistical Meetings (JSM) in August 2013, but this time with a

focus on the special Minitab statistical package that we used called Quality Companion (QC). Minitab wanted to display how

Rose-Hulman was using QC in the classroom and sponsored my talk at JSM. I was a guest presenter at their educational

booth, and they stuffed over 6000 conference bags advertising my talk. The student who was the co-presenter for the talk is

now a Quality Engineer at Tesla Motors! I have also learned a great deal of what to include as topics in this course from the

Six Sigma alumni currently in the work force.

An article in August 2013 in Quality Digest featured our Six Sigma study: “How Lean Six Sigma Students at Rose-Hulman

Reduced Food Waste”: http://www.qualitydigest.com/inside/quality-insider-column/how-lean-six-sigma-students-rose-

hulman-reduced-food-waste.html

Fall Quarter 2013: MA 495-1: Design of Experiments in Six Sigma, Independent Study

Level: Senior; Enrollment: 1 Student

A student from the previous year’s Six Sigma class wanted to do a Six Sigma project, including a Design of Experiments, for

his father’s business in India. The business, Harish Nickel Screens, is a manufacturer of rotary nickel screens used in the

textile printing industry. Located in Gujarat, India, the company is one of the largest producers of these screens in the region.

The main focus of the project was to reduce the rejection rate of the nickel screens, which would ultimately reduce the cost

of printing fabric.

Fall Quarter 2013: MA 495-2: Discrete Event Simulation in Six Sigma, Independent Study

Level: Senior; Enrollment: 1 Student

A student from the previous year’s Six Sigma class wanted to do a Six Sigma project to determine if additional baristas could

increase the number of customers served during Rose-Hulman’s busy passing periods between classes at the campus coffee

shop. Since Rose-Hulman does not offer a course in Discrete Event Simulation, I agreed to work with him on the project as

an independent study. In order to model the flow of traffic at the coffee shop, he set up a video camera at the coffee shop and

recorded many hours of service times. He reviewed the videos to determine time between customer arrivals, customer register

transaction times, coffee service times, etc. Using the simulation software ProModel, he determined a second register or card

reader for student IDs would increase the number of customers per passing period.

Winter Quarter 2013: MA 223: Engineering Statistics, 1 Section

Level: Sophomore; Enrollment: 54 Students; Class Meeting Hours: 40

Although I started at Rose-Hulman with degrees in Operations Research and Probability, the math department needed a

statistician. I was not this person, but I taught myself night after night and year after year until I am now confident that I am

providing my students with an Engineering Statistics course that would rival one taught by a true statistician by training. The

calculus-based class is an introductory course in statistical data analysis. Topics covered include Descriptive Statistics,

Probability Concepts, Random Variables, the Central Limit Theorem, Hypothesis Testing and Confidence Intervals for One

and Two Means, ANOVA, and Simple Linear Regression.

At least once a week I prepare a lab for the class that involves doing an experiment to collect data and applying the class

material to that data. Some experiments include doing puzzles with and without gloves (to simulate a manufacturing

environment), constructing and flying straw rockets, measuring reaction times, and placing limbs in water to determine

displacement amounts. The water displacement exercise actually became a paper and presentation for a student from that

class. I am a firm believer that students learn concepts more easily when they can apply them to real-world situations. I tend

to ask questions in quizzes and exams that are based on current issues or problems that they may encounter in life. In my

exams, I write problems to determine if students understand concepts, rather than plugging numbers into formulas.

Winter Quarter 2013: MA 385: Quality Methods, 1 Section

Level: Junior/Senior; Enrollment: 24 Students; Class Meeting Hours: 40

I began teaching the Quality Methods course in 2002. My teaching methods for the course have evolved throughout the years

as I have immersed myself in the subject. In order to “walk the walk,” I took my first sabbatical at a circuit board company

as a Quality Analyst for an entire year (2008). When I returned, I had more knowledge about quality processes than I could

have ever picked up from textbooks or seminars. I have found that students greatly benefit from seeing first-hand how quality

control is applied in industry. I have taken my classes on quality tours at local manufacturing companies (e.g., Eli Lilly,

ThyssenKrupp) and have had guest speakers from industry. As a member of the American Society of Quality (ASQ), I take

my students to the monthly ASQ meetings to meet, greet, and hear presentations from practitioners.

As part of the course, students do a quarter-long project in which they track a daily activity of their choosing and collect data

from it. Some activities include amount of sleep time, number of texts sent per day, score at some online or real game, number

of times their dog barks to go outside, time spent online or at some other task, etc. The project allows students to apply the

lessons in class to a real-world process that they control. Along the way, they need to construct graphics and control charts

showcasing their data, run statistical analyses on their process, determine factors affecting their data, and determine the

capability for their processes.

Topics in the class include a History of Quality Control (including Management Principles and Deming’s 14 points), Type I

& Type II Errors and Power Calculations, Operating Characteristic Curves, Acceptance Sampling, Control Charts (�̅�, R, I,

MR, p, np, c, u, MA, EWMA, CUSUM), Capability Analysis (Cp, Cpk, Pp, and Ppk), Transformations, Measurement System

Analysis, Distribution Fitting, and Sampling.

With respect to sampling, one of the most important aspects of constructing a control chart for a process is determine how to

sample parts for testing. As part of an in-class lab, I wrote a sample exercise in which students are operators of four machines

that produce Easter peeps. The operators need to determine an acceptable sampling plan for each hour of operation. They can

subgroup between machines, within a given machine, or from a single stream. In order to visibly “see” the process, I enlisted

the help of a Rose-Hulman Junior Computer Science Design team to construct an online simulation of the process. It is

available online at http://www.rose-hulman.edu/~evans/peeps/php/new.php (works best in Internet Explorer). Not only can

students select peeps for testing by clicking on them, they can choose different distributions (e.g., exponential, uniform) for

the peeps to experiment with their effects on control charts. Minitab noticed that we were using their software to do the

statistical analysis in the activity, and a Minitab writer (Carly Berry) created a Minitab blog for the activity that can be viewed

worldwide. It is at the following website:

http://blog.minitab.com/blog/real-world-quality-improvement/control-charts-rational-subgrouping-and-marshmallow-peeps

Spring Quarter 2014: MA 385: Quality Methods, 1 Section: See MA 385: Quality Methods ABOVE

Level: Junior/Senior; Enrollment: 33 Students; Class Meeting Hours: 40

Spring Quarter 2014: MA 387: Statistical Methods in Six Sigma, 2 Sections (teaching an overload)

Level: Junior/Senior; Enrollment: 51 Students; Class Meeting Hours: 40

This quarter I had a waitlist of 25+ students trying to get in my Six Sigma course. I agreed to open another section, and I

thought I better have them all do the same Six Sigma project since it would be too hard to track 50 different projects. We are

doing a major recycling project in the academic buildings at Rose-Hulman. There are 15 teams (Trash Talk, The Oscars,

Wasted Management, etc.) and each team is responsible for collecting trash at the end of afternoon classes in a pre-assigned

area. During the first 3 weeks of this quarter, the teams have collected baseline data for determining the amount of recyclables

that are being thrown in the regular trash. We have asked for help from Facilities and Custodial Services in providing latex

gloves, bags, and trash grabbers. Teams not only keep a count of each type of recyclable in their areas (e.g., plastic, paper) ,

but they weigh their trash and recyclables on a common scale. We currently have two weeks of baseline data suggesting that

35% of the trash in academic buildings is recyclable. We are currently in the Measure and Analyze Phases of the DMAIC

process, and next teams will attempt to reduce this percent in the Improve Phase by various means, such as creating

educational posters, running informative slide shows, or constructing talking trash cans. Here is a video of our BIG campus

recycling count day: https://www.youtube.com/watch?v=cxTROuJOdPU.