-
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
|
|
