>>Welcome to ISE 5013, the Fundamentals of Engineering Statistical Analysis. My name
is Kash Barker, and I’m a faculty member in the school of Industrial and Systems Engineering
at the University of Oklahoma.
I usually teach courses in applied statistics and systems engineering. Naturally, you might
imagine that I enjoy the material that we’re going to cover in this course. And you’d
be right. But beyond enjoying the topics, I think that a statistics course is vital
for engineers, as we often deal with situations that involve uncertainty. We have to collect
data to describe the performance of a process or system, and we need to understand what
“collecting a sample” means to us in describing the process or system in general beyond just
our sample.
Maybe we’re improving the design of a particular system, and we observe its performance to
identify areas for improvement. Processes and systems typically don’t exhibit the
exact same behavior over and over, so there is uncertainty in their performance when we
change different design parameters. A course in statistics will help us describe this uncertainty
and ultimately help us make decisions about the design of engineering processes and systems.
Let’s look briefly at a process for addressing systems engineering problems. There are many
different step-by-step processes for thinking about engineering problems, many of them are
very similar to each other, and this is just one of those. This process is referred to
as SIMILAR, and it’s the process that’s adopted by INCOSE, the International Council
on Systems Engineering. SIMILAR, which was developed and refined by Terry Bahill and
other coauthors, is a 7-step process for bringing systems into being. I’m an industrial engineer,
so I often think about service-oriented systems. As such, let’s use an emergency room design
example to illustrate the SIMILAR process. The process starts with Stating the problem,
or identifying the issue that needs to be address. For our ER example, the problem might
be that customers are waiting too long before they are being treated. The next step deals
with Investigating different system alternatives that may address that initial problem. In
this step, we identify how we might measure the performance of alternative designs to
make a comparison. In our case, patient-waiting time is a good variable to measure, and there
may be several others. Then we Model the performance of those different alternative systems. In
the ER example, we might simulate different ER layout alternatives, changing different
parameters such as the number of beds or staffing levels. After we’ve chosen the components
of our system from several alternatives, we Integrate all the components to make a whole
and ensure that our new system configures with existing systems and software. Finally
we Launch the system and Assess its performance. And with every step, we Re-evaluate the system
to determine if tweaks to the design need to be made.
At several points in the SIMILAR process, or any other systematic process for bringing
a system into being, we need to collect data about the system. When we investigate ER alternatives,
we need to collect data on waiting times and patient throughput. These measures are found
as the result of experimentation. After the new ER design has been launched, we assess
its performance through data collection, and we need a means to turn those data into usable
measures to guide the continued use of the design. This is true whether we’re reconfiguring
an ER layout, building bridges, or designing a new jet engine: we as engineers deal with
data, and we make decisions based on these data.
Broadly, the field of statistics deals with: the collection of, the presentation of, the
analysis of, and the use of DATA to solve problems, understand the performance of processes
and systems, and make decisions.
The word “statistics” is thrown around often to represent a fact that deals with
numbers. You often hear the word statistics during a sport’s broadcast. For example,
“our stats guy just gave us this statistic: the last time the previous year’s Super
Bowl champ won a playoff game prior to 2015 was 2006.”
The use of the term “statistics” in that way is fine, but it’s not how we’re going
to use the term in this class. The formal definition we’ll use is: Statistics is a
group of methods used to collect, analyze, present, and interpret data and to assist
in informed decision making.
From an engineer’s perspective, data collection on its own adds little value. We want to use
data to inform the decision making process, for example: do we redesign a component part
to improve fuel efficiency? Do we choose a different supplier that offers a more reliable
product? Do we use a different material in an electronic subassembly to reduce heat production?
And so on.
We’ll explore foundational tools and techniques of statistical analysis to help us as engineers
understand the performance of uncertain processes and systems through data. And while I use
several engineering contexts, this course is also meant to be a foundational course
in a broader study of analytics, or the discovery of knowledge from data. Competency in analytics
methods is built, in part, on an ability to understand and use the tools and techniques
from this course.
By the end of this course, you will be able to demonstrate an ability to: 1. Understand
data and draw insights through graphical depictions and descriptive measures. 2. Identify random
variables and describe their distribution, while using rules of probability to describe
uncertain events, 3. Estimate population parameters from sample data, 4. Construct statistical
hypotheses and perform hypothesis testing, and 5. Use software tools to assist with all
of the above.