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Discrete Random Variable
the set of all possible values is at most a finite or a countably infinite number of possible values

Countinuous Random Variable
takes on values at every point over a given interval

Required for a discrete probability function
probabilities are between 0 and 1: total of all probabilities equals 1

Binomial Distribution
exactly two possible outcomes: success and failure

POisson Distribution
describes a process that extends over time space, on any well defined unit of inspection

uniform distribution
 uniformly distributed
 anything outside the box = 0

Normal distribution
 exhibits the following characteristics: it is continuous distribution, symmetric about the mean, asymptotic
 to the horizontal axis, unimodal, is a family of curves, area under the curve is 1, it is bell shaped.
sample size greater than 30

Central Limit theorem
 assumption of normality
 sample distribution will be normal regardless of true population distribution

Sampling Reasons
highly precise, less costly, and less time consuming

sampling error
 sample mean  population mean
 the greater the sample size the less probability for error

Point estimate
the single value of a statistic calculated from a sample which is used to estimate a population parameter

interval estimate
a range of values calculated from a sample statistic(s) and standardized statistics, such as the Z

Sampling techniques
nonstatistical sampling: convenience, judgement
statistical sampling: simple random, systematic, stratified, cluster

statistical sampling
 items of the sample are chosen based on known or calculated probabilities
 simple random
 stratified
 Systematic
 Cluster

simple random sampling
equal chance of being selected

stratified random sampling
divide population into subgroups (strata) according to some common characteristics

cluster sampling
divide population into several clusters, simple random sample of clusters

