## 1. Number between 0 and 1

### 1.1. The possible outcome of a random circumstance

### 1.2. Meets 1st condition for valid probablity

## 2. Uses percentages based on proportions of individuals

## 3. Definitions

### 3.1. Sample Space

3.1.1. With Replacement

3.1.2. Without Replacement

### 3.2. Simple Event

3.2.1. Sum of all Simple Events = 1

3.2.2. If equally likely then probability = 1/k

### 3.3. Event

## 4. Rules for Finding Probabilities

### 4.1. Rule 1 (Not the event)

4.1.1. Probability an event does not occur

4.1.1.1. P(A)+P(A^c)=1

### 4.2. Rule 2 (Addition)

4.2.1. Probability that either of two events happens

4.2.1.1. Rule 2a (general)

4.2.1.1.1. P(A or B)=P(A)+P(B)-P(A and B)

4.2.1.2. Rule 2b (mutually exclusive events)

4.2.1.2.1. P(A or B)=P(A)+P(B)

### 4.3. Rule 3 (Multiplication)

4.3.1. Probability that two or more events occur together

4.3.1.1. Rule 3a (general)

4.3.1.1.1. P(A and B)=P(A)PB|A)=P(B)PA|B)

4.3.1.2. Rule 3b (independent events)

4.3.1.2.1. PA and B)=P(A)P(B)

4.3.1.3. Rule 3b (extension)

4.3.1.3.1. P(A1 and A2...and An)=P(A1)P(A2)...(PAn)

### 4.4. Rule 4 (Conditional Probability)

4.4.1. Determining a conditional probability

4.4.1.1. P(B|A)=P(A andB)/P(A)

## 5. Random Circumstance

### 5.1. outcome not determined until we see it

### 5.2. Outcome already determined but our knowledge is uncertain

## 6. Relative Frequency Interpretation

### 6.1. Make assumption about Physical world

### 6.2. Make direct observation of how often something happens over many, many repetitions of the situation.

## 7. Personal Probability (subjective)

### 7.1. Degree an individual believes it will happen

### 7.2. Coherent (no contradiction)

## 8. Events

### 8.1. Complementary ("Opposite")

### 8.2. mutually exclusive (disjoint)

### 8.3. Independent

### 8.4. Dependent

### 8.5. Conditional Probablity

8.5.1. P(B|A) = probability of B given A

## 9. Philosophical Issue

### 9.1. Outcome has been determined but is still unknown

### 9.2. Confidence vs Probability

## 10. Strategies

### 10.1. Bayes Rule

10.1.1. P(A|B)=P(A and B)/PB|A)P(A)+P(B|A^c)P(A^c)