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# statistics week 1

### chapters 1-4

TermDefinition
Voluntary response sample A sample in which the respondents decide whether or not to be included.
parameter numerical measurement describing some characteristic of a population
statistic numerical measurement describing some characteristic of a sample
numerical (quantitative) data consists of number representing counts or measurements (age, height, time)
categorical (qualitative) data consists of names or labels that can be separated into different categories distinguished by some non-numerical characteristic (gender, color, shape)
discrete quantitative data the data may take on any of a finite/countable number of possible values (how many eggs does a hen lay in a day)
continuous data the data may take on any value over a continuous range of infinitely many possible values
nominal level data consists of names, labels, or categories. Data cannot be arranged in an ordering scheme. Ex.: gender, color, yes/no
ordinal level data can be arranged in some order but differences between values either cannot be determined or are meaningless: e.g., grades, ranks, etc
interval level differences between two data values can be determined and are meaningful, but there is no natural zero starting point
ratio level same as interval level, but data has a natural zero starting point, so both differences and ratios are meaningful. e.g., ages, prices, etc
random sample each individual has the same chance of being selected
simple random sample all samples of same size have same chance of being selected
systematic sampling we select some starting point and then select every Kth element in the population
convenience sampling use the results that are very easy to get - "man in the street" polls
stratified sampling subdivide population into at least two different subgroups or strata so that subjects within the same subgroup share the same characteristics (such as age bracket). Then draw sample from each subgroup (stratum)
cluster sampling we first divide the population area into sections (clusters) then randomly select some of those clusters and choose all the members from those selected clusters
Created by: awilson42