Engineering Statistics and Quality Control
36-220, Fall 2005
Professor: Cosma Shalizi
Statistics is the branch of science which deals with accidents, uncertainty,
variability, irreproducible results and mistakes. The first goal of this
course is to introduce you to some of the mathematical tools statisticians have
devised to handle randomness, uncertainty and error. These tools are based on
probability theory, and the mathematical fact that large collections of
individually random events display predictable patterns. The other goal of the
course is to help you see how these tools can help you do better engineering.
Because many of the calculations involved are tedious, you will also learn how
to use Minitab, an entry-level
statistical computing package. The emphasis is not on the mechanics of the
calculations, but on the basic concepts, the core mathematical ideas, and
knowing which kind of calculation to do when.
Essential Course Information
Lectures are Monday and Wednesday, 11:30-12:20, in room B131 Hamerschlag
Hall. The course time table lists a Friday lecture, but it does not exist.
Instead, lab sections A and C meet Fridays at 11:30; there is also a section B,
which meets Wednesdays at 12:30. Labs are mandatory, and begin the week of
September 5th. There will be homework every week, due on Wednesdays at 11:30.
The textbook is Devore's Probability and Statistics for Engineering and
the Sciences, 6th edition. As a supplement, I strongly recommend Larry
Gonick and Wollcoot Smith's The
Cartoon Guide to Statistics.
Do not miss labs; there are no make-ups. Do not forget to turn in your
homework; late work will not be accepted.
Office hours are as follows:
|Monday 4:30-5:30 ||219 Old Student Center (4902 Forbes)|
|Tuesday 11:00-12:00 ||229C Baker Hall|
|Tuesday 1:30--2:30 ||219 OSC|
|Tuesday 6:00-7:00 ||219 OSC|
The syllabus (link above) gives the schedule of lectures and labs, details
on grading, etc.
Remember, assignments are due one week after they are posted.
Please show your work: partial credit will be based on work shown, and
no credit will be given for unsupported answers.
- Assignment #1, due September 7th; solutions
- Assignment #2, due September 14th; solutions
- Assignment #3, due September 21st; solutions
- Assignment #4, due September
- Assignment #5, due October 5th; solutions
- Assignment #6, due October 12th; solutions
- Assignment #7, due November 2nd; solutions
- Assignment #8, due November 9th; solutions
- Assignment #9, due November 16th; solutions
- Assignment #10, due November 23rd;
- Assignment #11, due December 7th; solutions
- Lab #1, for the week of September 5th
- Lab #2, for the week of September 12th
- Lab #3, for the week of September 19th
- Lab #4, for the week of September 26th
- Lab #5, for the week of October 3rd
- Lab #6, for the week of October 10th
- Lab #7, for the week of October 24th
- Lab #8, for the week of October 31st
- Lab #9, for the week of November 7th
- Lab #10, for the week of November 14th
- Lab #11, for the week of November 28th
Reading these notes is not a substitute for coming to lecture.
- Lecture 4: Conditional probability,
total probability, Bayes's rule
- Lecture 5: Independence, discrete random variables
- Lecture 6: More on discrete random variables
- Lecture 7: Continuous random variables
- Lecture 8: More continuous random variables
- Lecture 9: Multiple random variables, joint distributions, independent random variables
- Lecture 10: Sampling distributions, law of large numbers, central limit theorem
- Lecture 11: Propagation of error; limit theorems applied to real data
- Lecture 12: Quality control
- SAMPLE midterm; solutions
- This is an actual midterm from a previous semester. It's representative of
the level of difficulty of this semester's midterm, or maybe a bit harder.
Do not assume that the midterm will only cover material on the sample
- ACTUAL midterm; solutions
- PRACTICE final
- This is an actual final exam from a previous semester; the same
warnings apply to this as to the sample midterm.
- Data on car crashes (used with lab 1)
- Data on hair-conditioner bottle caps (used with lab 6)
- Data on cloud-seeding (used with lab 8)
- Data on gas mileage (used with lab 11)
- Data on nuclear power-plant construction (used with lab 10)
- Data on screw defects (used with lab 6)
- Data on the speed of light (used with lab 8)
- Data on width of metal wires produced during chip making (used with lab