# 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