2017 November 22,
Carleton College, Fall 2017, Prof. Joshua R. Davis, , CMC 219, x4095
Probability is a beautiful subject of pure mathematics with many applications throughout the sciences. It is the theoretical basis for statistics. It finds heavy use in quantum theory, thermodynamics, finance, traffic flow, meteorology, etc. And there's gambling.
This is a first course in probability, assuming only multivariable calculus as background. Approximately, the first half of the course is discrete and the second half continuous. The materials are
Our class meets in Willis 211 during period 3A (MonWed 11:10-12:20, Fri 12:00-1:00). If you want to meet with me outside class, then try to make my office hours, which are Monday 5A, Tuesday 11:00-12:00 and 1:00-2:00, and Friday 4A. If you cannot make office hours, then e-mail me, listing several possible times.
Final grades (A, B, C, etc.) are assigned according to an approximate curving process. By this I mean that there are no predetermined percentages (90%, 80%, 70%, etc.) required for specific grades. The advantage of this system is that student grades don't suffer when I write a difficult exam. The disadvantage is that you cannot compute your own grade. Visit me in my office, if you want me to estimate your current grade for you. The following elements contribute.
You are expected to spend about 10 hours per week on this course outside class. Some students need to spend more than 10 hours. If you find yourself spending more than 15 hours, then talk to me.
On homework, you are encouraged to figure out the problems with other students. However, you should always write/type your solutions individually, in your own words. You may not copy someone else's work or allow them to copy yours. Presenting someone else's work as your own is a violation of Carleton's Academic Integrity standards.
Writing is not just for English and history majors. Written and oral communication skills are essential to every academic discipline and are highly prized by employers. In this course, your written work is evaluated both for correctness and for presentation.
Although homework is assigned every day, it is collected only once a week. When handing in a week's homework, staple your pages into a single packet, in the correct order. Multi-sheet packets that are not stapled are unacceptable. I will not accept packets that are not stapled. Is there a stapler in the classroom? Often not, so staple ahead of time. Is a paper clip okay? No.
Depending on time constraints in any given week, perhaps not all of your homework will be graded. In order to ensure full credit, do all of the assigned problems.
During the term, you have one free pass to hand in a week's homework packet late, no questions asked. Simply hand in your late packet when the next packet is due, writing "Late Pass Used" prominently at the top of the late packet.
Once you have used your late pass, no late assignments are accepted, except in extreme circumstances that typically involve interventions by physicians or deans.
If some medical condition affects your participation in class or your taking of exams, let me know in the first week of class. You may need to make official arrangements with the Disability Services.
To help you decode the schedule, here is an example. On Day 1 we discuss sample spaces and probability functions. Sections 1.1-1.3 of the textbook covers that material; read them before or after class, to get another treatment. You have homework called "Day 1", some of which is due on Day 1 and some of which is due on Day 2. If you wish, you can view the file coinsAndDice.R that I used in class.
| Date | Day | Reading | Topics | Assignment | Due | Notes |
|---|---|---|---|---|---|---|
| M 09/11 | 1 | 1.1-1.3 | sample spaces, probability functions | Day 1 | 1, 2 | coinsAndDice.R |
| W 09/13 | 2 | 1.4-1.7 | properties, counting, sampling with(out) replacement | Day 2 | 5 | replacement.R |
| F 09/15 | 3 | 1.8-1.9, 2.1-2.3 | random variables, conditional probability | Day 3 | 5 | |
| M 09/18 | 4 | 2.4-2.5 | conditioning, Bayes' theorem | Day 4 | 8 | |
| W 09/20 | 5 | 3.1-3.2 | independence | Day 5 | 8 | |
| F 09/22 | 6 | 3.3-3.5 | Bernoulli, binomial distributions | Day 6 | 8 | binomial.R |
| M 09/25 | 7 | 3.6-3.7, 5.1 | Poisson, geometric distributions | Day 7 | 11 | |
| W 09/27 | 8 | 4.1 | R lab, random variables, expected value | Day 8 | 11 | basicR.R |
| F 09/29 | 9 | 4.2, 4.10, 4.3 | functions of random variables, joint distributions | Day 9 | 11 | |
| M 10/02 | 10 | first exam | ||||
| W 10/04 | 11 | class cancelled | ||||
| F 10/06 | 12 | 4.4-4.5 | independence, sums of random variables | Day 12 | 14 | |
| M 10/09 | 13 | 4.6-4.7 | variance, covariance, correlation | Day 13 | 16 | |
| W 10/11 | 14 | 4.8, 6.1, 6.3 | conditional distributions | Day 14 | 16 | |
| F 10/13 | 15 | 6.1-6.4 | continuous distributions, expected value, variance | Day 15 | 16 | |
| M 10/16 | midterm break | |||||
| W 10/18 | 16 | 6.5-6.6 | exponential distribution, functions of random variables | Day 16 | 19 | inverseTransform.R |
| F 10/20 | 17 | 6.7 | joint and marginal distributions | Day 17 | 22 | |
| M 10/23 | 18 | 6.8 | independence | |||
| W 10/25 | 19 | second exam | ||||
| F 10/27 | 20 | 6.9 | covariance | Day 20 | 22 | |
| M 10/30 | 21 | 6.10, 8.1, 8.3 | functions of a random variable, conditional distributions, expectation | Day 21 | 25 | |
| W 11/01 | 22 | 8.3, 7.1 | conditional expectation, normal distribution | Day 22 | 25 | |
| F 11/03 | 23 | 7.1, 9.1-9.2 | normal distribution, laws of large numbers | Day 23 | 25 | binomialNormal.R |
| M 11/06 | 24 | 9.4-9.5 | central limit theorem, moment generating functions | Day 24 | 28 | |
| W 11/08 | 25 | 9.4-9.5 | R lab, central limit theorem, moment generating functions | Day 25 | 28 | clt.R |
| F 11/10 | 26 | 9.3, 9.4.1 | Monte Carlo methods | monteCarlo.R | ||
| M 11/13 | 27 | review | Review Problems | |||
| W 11/15 | 28 | review | ||||
| S 11/18 | final exam 3:30-6:00 |
If the course were longer, we might have done: Poisson processes, more on conditional expectation, more on the hypergeometric, negative binomial, gamma, beta distributions.