# Experimental Data Analysis Lab

## PHYS 391 - Spring 2013

`http://physics.uoregon.edu/~torrence/391/`
Updated Wednesday May 15, 2013

Homework #4 due May 21
Notes on Poisson distribution
Lab #4 due Wednesday May 29

Instructor LabAssistant Prof. Eric Torrence Willamette 418, 346-4618torrence (at) uoregonOffice Hours: F 2-4 Jonathan Mackrory mackrory (at) uoregon TuTh 3:30-4:50 Willamette 318 Lab schedule Introduction to Error Analysis, 2ed, Taylor Getting Started with MATLAB, Pratap

### Overview

This course will introduce the basic concepts of data analysis and practical techniques for implementing them. Half of the course will emphasize the theoretical foundation of data analysis with lectures and homework assignments, while the other half will emphasize the practical application of data analysis in lab assignments. Development of programming techniques for performing data analysis and data visualization will also be stressed using MATLAB. The following topics will be covered:

• Measurment Uncertainty and Error Propogation
• Statistical Inference
• Gaussian Distribution and Confidence Levels
• Least Squares and Linear Regression
• Binomial/Poisson Distributions
• Photon Counting Statistics
• Fourier Transforms

Course grades will be based on five bi-weekly homework assignments from Taylor (50%), and five bi-weekly lab assignments (50%). There will be no examinations (midterm or final) for this course, so it is important for students to turn in all assigned work when it is due. Late assignments will either be significantly penalized or not accepted at the instructors discretion. The final lab assignment will be due during finals week in place of a final examination.
In order to pass the course, you must complete all of the labs!

Grades will be awarded based on the departmental grading policy, and students can assume that 90% will earn an A, 80% will earn a B, 70% will earn a C, 60% will earn a D, and below this will result in a failing grade. Modifiers (+/-) will be applied for scores within a few percent of these boundaries. I may adjust the grade boundaries depending upon the final distribution so that students with similar scores will receive similar grades.

### Syllabus

Week Topic Lab
(due following Tuesday)
Homework
(due following Tuesday)
Week 1
4/1 - 4/5
Measurement Uncertainties Lab Signup HW1 Taylor Ch. 1-3
Pratap Ch. 1-2
Week 2
4/8 - 4/12
Statistical Inference MATLAB Intro Taylor Ch. 4
Pratap Ch. 1-2
Week 3
4/15 - 4/19
Normal Distribution HW2 Taylor Ch. 5
Pratap Ch. 3-4
Week 4
4/22 - 4/26
Weighted Average Speed of Light Taylor Ch. 6-7
Pratap Ch. 3-4
Week 5
4/29 - 5/3
Linear Regression HW3 Taylor Ch. 8
Pratap Ch. 5-6 (as needed)
Week 6
5/6 - 5/10
Binomial Distribution and Random Walks Brownian Motion Taylor Ch. 10
Pratap Ch. 5-6 (as needed)
Week 7
5/13 - 5/17
Poisson Distribution and Counting Statistics HW4 Taylor Ch. 11
Week 8
5/20 - 5/24
Random Processes Photon Counting
due Wednesday
Week 9
5/27 - 5/31
Fourier Transforms
No Labs Monday
HW5
due Thursday
FFT Handout
Week 10
6/3 - 6/7
Oscillators Fourier Transforms
Finals
6/10 - 6/14
Final Lab Due Thursday 6/13 at 5 PM

This syllabus is tentative, and is subject to change as the quarter progresses.

### MATLAB

One of the goals of this course is to give you the skills to properly do non-trivial data analysis of large data samples. There are many different tools available to do this, but we had to pick something, and we are going to use MATLAB. MATLAB is relatively easy to learn, very powerful, and widely used in Science and Engineering disciplines. MATLAB is also a good example of 'procedural programming', and the general techniques learned in this course can easily be transferred to the language or tool of your choice later. MATLAB is widely (and freely) available at the University of Oregon. Some possibilities are listed on the MATLAB Information page. You will need to use MATLAB extensively in this course, so you should invest some time in the first two weeks to make sure you have a working computing environment which you can use and you are happy with.

### Homework

Homework will typically be assigned every other week on Monday and due on the following Monday at the start of class. The homework will mostly be problems from Taylor forcing you to work through a particular concept 'by hand' at least once. Supplimental problems to exercise your MATLAB skills may also be assigned.

### Labs

Lab assignments will be made every two weeks and will be due on Tuesday during weeks when homework is not due. It is expected that you will work on your labs during the two weeks before they are due. Assigned lab times will be available when TAs will be in room 17 to provide support and advice, although you are free to work on the labs whenever you have time available. You will be expected to work with a partner, although each member of the lab group is expected to turn in their own material including the data analysis and any associated code.

Formal write-ups will not be required, although I do expect you to keep a lab notebook which clearly shows the work you have done. I really want to see proof that you did the lab and understood the material. Neatly organized notes taken during the lab itself, answers to the questions posed in the lab writeup, plus a short summary giving the main quantitative results in your notebook is perfectly adequate. If you are very sloppy in your notes, you may also turn in a longer printed write up, but please get into the habit of taking neat legible lab notes. Either way, please turn in your lab notebook (legible or not) on Tuesday in class. For labs with significant computer work, your M-files and supplemental material should be emailed to me directly as well.

For an upper-division course, the university expects students to spend one hour in class and two hours out of class for each credit. While each student will vary, you should expect on average to put this much time into this course. In particular, you should not expect to complete all of the lab work during the scheduled lab times each week, although you certainly should be able to collect all of the necessary data during that time. Make sure you do not try to start your lab assignments at the last minute. Most students who struggle in this course simply don't invenst enough time in completing the labs.