Chapter four - created from Mind Map

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Random Variables

Continuous random variable

**Discrete random variables**

Methods for discribing

The mean

Variance of Discrete Random Variables

µ = ∑xP(x)

SD^2 = ∑(x - µ)^2 • P(x)

Definitions

SSO

Experiment

Variable

Trials

Probability Distribution

event of interest

Coin Example

Formulas

Binomial Formula

calling it that

(p)

(q)

(x)

(n)

(X)

Attached Study Aids

Mind Map - Discrete Probability Distributions

Probability Distribution Table

Setting up

**Binomial random variables**

Characteristics

Binomial probability mass function

1) Identical Trials

2)Trials results (S) (F)

3) probability (p) is constant from trial to trial

4)The trials are independent

Success

Failure

P(x) = nCx • px • (1-p)^(n-x)

MU= N x P

SD^2 = np(1 - p)

A random variable can be thought of as representing an uncertain numerical outcome of an experiment

variables can take any numerical value on the real number line

temperature of a cup of coffee served at mcdonald's restaurant.

time in seconds that a customer in a store must wait to recieve a cred card autorization

any value is possible in a certain range

The trials are the exact same.

1-P

Sample Space Outcomes

Sucess

Failure

A Random non-determined experiment.

1)A description of a person/place/idea

2) This value can vary from one entity to another

Total number of successes in (n) trials

How many times you did the experiment

Number of trials

list of all possible outcomes of a RANDOM VARIABLE

flip a coin 3 times... Let A= the even of observing 1 heads in three flips.

What is the P(A)?

The event of interest

is a list of all possible outcomes of a random variable and their corresponding probabilities

1) define the random variable x= event of interest

2) create a table of outcomes with the two headings outcomes and value of X

3) list all of the possible outcomes and the values of X that correspond to each

4) create probability distribution table with three headings x, frequency, m, P(x)

Remember to put totals at the end of the graph

Counting numbers like rolling dice or flipping a coin.

1) histograms, X and it's corresponding values in a probability table and a function.

The Average value of the random variable if we were to observe it for an indefinite period of time

n trials where the probability for a success for each trial is p, the probability of observing x successes is?

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Chapter four

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