QUMT-
Card Set Information
Author:
mlu.reyes
ID:
13252
Filename:
QUMT-
Updated:
2010-04-06 02:57:56
Tags:
stats
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Description:
EXAM 2
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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