Engineering Statistics and Quality Control

36-220, Fall 2005

Statistics Department, Carnegie Mellon University

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.

Homework Assignments

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.

  1. Assignment #1, due September 7th; solutions
  2. Assignment #2, due September 14th; solutions
  3. Assignment #3, due September 21st; solutions
  4. Assignment #4, due September 28th; solutions
  5. Assignment #5, due October 5th; solutions
  6. Assignment #6, due October 12th; solutions
  7. Assignment #7, due November 2nd; solutions
  8. Assignment #8, due November 9th; solutions
  9. Assignment #9, due November 16th; solutions
  10. Assignment #10, due November 23rd; solutions
  11. Assignment #11, due December 7th; solutions

Lab Assignments

  1. Lab #1, for the week of September 5th
  2. Lab #2, for the week of September 12th
  3. Lab #3, for the week of September 19th
  4. Lab #4, for the week of September 26th
  5. Lab #5, for the week of October 3rd
  6. Lab #6, for the week of October 10th
  7. Lab #7, for the week of October 24th
  8. Lab #8, for the week of October 31st
  9. Lab #9, for the week of November 7th
  10. Lab #10, for the week of November 14th
  11. Lab #11, for the week of November 28th

Lecture Notes

Reading these notes is not a substitute for coming to lecture.

  1. Lecture 4: Conditional probability, total probability, Bayes's rule
  2. Lecture 5: Independence, discrete random variables
  3. Lecture 6: More on discrete random variables
  4. Lecture 7: Continuous random variables
  5. Lecture 8: More continuous random variables
  6. Lecture 9: Multiple random variables, joint distributions, independent random variables
  7. Lecture 10: Sampling distributions, law of large numbers, central limit theorem
  8. Lecture 11: Propagation of error; limit theorems applied to real data
  9. 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 exam.
ACTUAL midterm; solutions
This is an actual final exam from a previous semester; the same warnings apply to this as to the sample midterm.

Data Sets

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 9)