# QUMT-

 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 distributedanything outside the box = 0 Normal distribution exhibits the following characteristics: it is continuous distribution, symmetric about the mean, asymptoticto 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 normalitysample distribution will be normal regardless of true population distribution Sampling Reasons highly precise, less costly, and less time consuming sampling error sample mean - population meanthe 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 probabilitiessimple randomstratifiedSystematicCluster 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 Authormlu.reyes ID13252 Card SetQUMT- DescriptionEXAM 2 Updated2010-04-06T06:57:56Z Show Answers