Unit 6 Flashcards
Takes numerical values that describe the outcomes of a random process
Random Variable
Gives the possible values and probabilities of a random variable
Probability Distribution
Takes a fixed set of possible values with gaps between them
Discrete Random Variable
The average value of a variable over many trials of the same random process
Mean (Expected Value) of a Discrete Random Variable
Measures how much the values of the variable typically vary from the mean in many trials of the same random process
Standard Deviation of a Discrete Random Variable
Can take any value in an interval on the number line
Continuous Random Variable
Knowing the value of one variable does not change the probability distribution of the other variable
Independent Random Variables
Arises when we perform n independent trials of the same random process and count the number of times that a particular outcome (called a “success”) occurs
Binomial Setting
The count of successes in a binomial setting
Binomial Random Variable
The probability distribution of the successes
Binomial Distribution
The number of ways to arrange x successes among n trials
Binomial Coefficient
When taking a random sample of size n from a population of size N, we can treat individual observations as independent when performing calculations as long as n < 0.10N
10% Condition
The probability distribution of successes is approximately normal if np >= 10 and n(1-p) <= 10
Large Count Condition
Arises when we perform independent trials of the same random process and record the number of trials it takes to get one success
Geometric Setting
The number of trials it takes to get a success in a geometric setting
Geometric Random Variable
