SB1: 1.1-1.3

population

is the entire collection of objects or outcomes about which information is sought

sample

is a subset of a population, containing the objects or outcomes that are actually observed

simple random sample

of a size n is a sample chosen by a method in which each collection of n population items is equally likely to make up the sample, just as in a lottery

sample of convenience

is a sample that is obtained is some conventient way, not drawn by a well-defined random method

sampling variation

a phenomenon where two different samples from the same population differ from each other

conceptual population

the sample comes from a population that consists of all the values that might have been observed

independent

knowing the values of some of the items does not help to predict the values of the others

weighted sampling

some items are given a greater chance of being selected than others (ex. more tickets in a lottery)

stratified random sampling

population is divied up into subpopulatins, and a simple random sample is drawn from each

cluster sampling

items are drawn from the population in groups or clusters

tangible

population consists of physical objects

coceptual

population consits of values from a process under identical experimental conditions

standard deviation

is a quantity that measures the degree of a spread in a sample

outlier

is an data point much larger or smaller that the others in its data set or sample

statistic

is a number that describes a sample

parameter

a numerical summary of a population

mode

most frequent value

range

difference between max and min

mean

sum divied by sample size

median

center of sorted sample

Inferential Statistics

• Draw conclusions about a larger population from a sample

• Statistical inference only works if the sample is representative of the population

Descriptive Statistics

• Organize and summarize data

• to extract information from the sample, we need to summarize the data in a meaningful way

• to describe “shape and pattern” of sample data

Unconditional Probability

probability is based on the entire sample space

Conditional probability

• Probability is based on a subset of the sample space

• It is the probability of an event occurring given that another event has already occurred

• denoted by the symbol P(A|B)=”probability of A given B”