Statistical Inference/Probability Theory: Difference between revisions

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= Set Theory =
= Set Theory =


== Sample Space [Definition 1.1.1] ==
== Sample Space ==


The set, ''S'', of all possible outcomes of a particular experiment is called the ''sample space'' for the experiment.
The set, ''S'', of all possible outcomes of a particular experiment is called the ''sample space'' for the experiment. [Definition 1.1.1]


== Event [Definition 1.1.2] ==
== Event [Definition 1.1.2] ==

Revision as of 13:01, 1 August 2018

Statistical Inference
Probability Theory
Transformations and Expectations
Common Families of Distributions
Multiple Random Variables
Properties of a Random Sample
Principles of Data Reductions
Point Estimation
Hypothesis Testing
Interval Estimation
Asymptotic Evaluations
Analysis of Variance and Regression
Regression Models

Set Theory

Sample Space

The set, S, of all possible outcomes of a particular experiment is called the sample space for the experiment. [Definition 1.1.1]

Event [Definition 1.1.2]

An event is any collection of possible outcomes of an experiment, that is, any subset of S (including S itself).

Let A be an event, a subset of S. We say the event A occurs if the outcome of the experiment is in the set A. When speaking of probabilities, we generally speak of the probability of an event, rather than a set. But we may use the terms interchangeably.

Base relationships

We first need to define formally the following two relationships, which allow us to order and equate sets:

Containment:

<amsmath>A \subset B \Leftrightarrow x \in A \Rightarrow x \in B</amsmath> 

Equality:

<amsmath>A = B \Leftrightarrow A \subset B \land B \subset A</amsmath>

Elementary operations

Given any two events (or sets) A and B , we have the following elementary set operations:

Union: The union of A and B, written <amsmath>A \cup B</amsmath>, is the set of elements that belong to either A or B or both:

<amsmath>A \cup B = \{ x : x \in A \lor x \in B \}</amsmath>

Intersection: The intersection of A and B, written <amsmath>A \cap B</amsmath>, is the set of elements that belong to both A and B:

<amsmath>A \cap B = \{ x : x \in A \land x \in B \}</amsmath>

Complementation: The complement of A, written <amsmath>\overline{A}</amsmath>, is the set of all elements that are not in A:

<amsmath>\overline{A} = \{ x : x \notin A \}</amsmath>

Event Operations

The elementary set operations can be combined: for any three events, A, B, and C, defined on a sample space S, the following relationships hold [Theorem 1.1.4].

Commutativity

  • <amsmath>A \cup B = B \cup A</amsmath>
  • <amsmath>A \cap B = B \cap A</amsmath>

Associativity

  • <amsmath>A \cup (B \cup C) = (A \cup B) \cup C</amsmath>
  • <amsmath>A \cap (B \cap C) = (A \cap B) \cap C</amsmath>

Distributive Laws

  • <amsmath>A \cap (B \cup C) = (A \cap B) \cup (A \cap C)</amsmath>
  • <amsmath>A \cup (B \cap C) = (A \cup B) \cap (A \cup C)</amsmath>

DeMorgan Laws

  • <amsmath>\overline{A \cup B} = \overline{A} \cap \overline{B}</amsmath>
  • <amsmath>\overline{A \cap B} = \overline{A} \cup \overline{B}</amsmath>

Basics of Probability Theory

Conditional Probability and Independence

Random Variables

Distribution Functions

Density and Mass Functions