Exam 2 (Chapter 4 - 8)
Union
A & B is event that occur if A or B occurs or both
Probability curve
A curve giving the probability of where an object might be detected.
Discrete
All possible values
Sampling Distribution of Sample Proportion
Approximately has normal distribution if sample size is large enough.
Relative Frequency Method
Cannot assume all outcomes, run sample many times & perform estimated based
Three methods of Sample Space
Classical Method Relative Frequency Method Subjective Method
Controlled Experiment
Determine probability through data collection
Conditional Probability
Event or outcome occurred based on occurrence of previous event or outcome
Probability Rules
Formulas & methods used to calculate probabilities
Subjective Method
Guess or estimate made intuitive judgment, not by running quantitative analysis on numeric data
Mutally Exclusive Events
Have no sample space outcomes in common (Cannot occur at same time)
Venn Diagram
Illustrates the logical relation between sets
normal probability distribution
Its probability density function is bell-shaped and determined by its mean and standard deviation .
Continuous
List numeric & can be decimal place of any number digits
Probability Distribution
Listing all possible values & the probabilities each value results
Probability Model
Mathematical representation of a random phenomenon
Independent
Probabilities of two event are not influenced in any way by each other
Experiment
Process of observation has uncertain outcome
Sample Space
Set of all possible outcomes for experiment
Event
Set of one or more sample space outcomes
Addition Rule
Two events are mutually exclusive, the probability their union is sum of probabilities
Intersection
Two events that occur both A & B
Probability
Used to deal with uncertainty (Chance or likelihood that event will occur)
Classical Method
Used when all outcomes are equally like (Probability of 1/N each outcome within "N")
Density function
a function that returns the probability a given outcome occurs for a particular statistical distribution
uniform distribution
all possible outcomes have same probability
continuous random variable
any numerical value in one or more intervals on real number line
normal distribution
any sample size "N" the population of all possible sample mean
continour random variables
assume any numerical value in one or more intervals on real number line
discrete probability distribution
discrete random variable
probability distribution
how probabilities are distributed over values of random variable
central limit theorem
if sample size "N" large, then sampling distribution of "X" (sample mean) is normal, even if S.P. not normal distribution
Binomial Distribution
observing certain outcome when performing series of test only two possible outcomes
Mean
population of all possible sample mean = population mean
standard deviation
population of all possible sample mean is less then the population standard deviation
sample distribution of sample mean
population of all possible sample means that be obtained from all possible samples of same size (looks like normal curve)
continuous probability distribution
random variable "X" take any value (Assigned to intervals values)
discrete random variable
random variable can be counted or listed
Random variable
value is numerical & determine by the outcome of experiment
discrete random variable
weighted avg. of possible values that random variable can take
