Math 2: General Course Outline
Catalog Description
2. Finite Mathematics. (4)Lecture, three hours; discussion, one hour. Preparation: three years of high school mathematics. Finite mathematics consisting of matrices, Gauss/Jordan method, combinatorics, probability, Bayes theorem, and Markov chains. P/NP or letter grading.
Course Information:
The following schedule, with textbook sections and topics, is based on 23 lectures. The remaining classroom meetings are for leeway, reviews, and midterm exams. These are scheduled by the individual instructor. Often there are reviews and two midterm exams about the beginning of the fourth and eighth weeks of instruction, plus reviews for the final exam.
Math 2 may be used to satisfy the quantitative reasoning requirement of the College of Letters and Science.
The main topic of Math 2 is the theory of probability. This subject is important for many of the applications of mathematics to other areas. Many facets of everyday life involve probabilities, as TV program ratings, insurance rates, freethrow shooting percentages, birthrates, inherited traits, and the California lottery.
Probability furnishes the mathematical foundations for statistics. Consequently, Math 2 complements the courses in statistics taken by many of the students majoring in the social and biological sciences. Math 2 may be taken either before or after an introductory course in statistics.
Textbook
R. Brown, and B. Brown, Essentials of Finite Mathematics: Matrices, Linear Programming, Probability, Markov Chains, Ardsley House.
Outline update: P. Greene, 11/12
Schedule of Lectures
| Lecture | Section | Topics |
|---|---|---|
|
1 |
3.1 |
Introduction: Probability and Odds |
|
2 |
3.2 |
Counting |
|
3 |
3.3 |
Permutations and Factorials |
|
4 |
3.4 |
Combinations |
|
5 |
3.5 |
Computing Probability by Counting |
|
6 |
3.6-3.7 |
Union of Events, Disjoint Events |
|
7 |
3.8 |
Conditional Probability |
|
8 |
3.9 |
Intersection of Events |
|
9 |
4.1 |
Partitions |
|
10 |
4.2 |
Bayes? Theorem |
|
11 |
4.3 |
Random Variables and Probability Distributions |
|
12 |
4.4 |
Expected Value and Variance |
|
13 |
4.5 |
Binomial Experiments |
|
14 |
4.6 |
The Normal Distribution |
|
15 |
4.7 |
Normal Approximations for the Binomial Distribution |
|
16 |
More Practice with Binomial Distributions |
|
|
17 |
1.1 |
Matrices |
|
18 |
1.2 |
Matrix Multiplication |
|
19-20 |
1.4 |
Solving Linear Systems using Gauss Jordon Method |
|
21 |
5.1 |
Matrices and Probability |
|
22 |
5.2 |
Markov Chain Processes |
|
23-24 |
5.3 |
Equilibrium requiring Gauss?Jordon |