Probability And Statistics Chapter 1

Front 1

The objective of statistics

Back 1

Make inferences about a population based on information in a sample.

Front 2

Population

Back 2

Complete collection of elements to be studied; every individual of interest

Front 3

Sample

Back 3

Sub collection/ subset of the elements drawn from the population; a part of the population that is examined to gain info

Front 4

Data is produced by

Back 4

Sampling, experiments, census, simulations by computers or calculator

Front 5

Random sampling

Back 5

Members of the population are selected in such a way that each member has an EQUAL CHANCE of being selected

Front 6

Variable

Back 6

Any characteristic of an object /individual that can be expressed as a number. (Ex: height, income)

Front 7

Value

Back 7

The actual number (ex: 6 feet, $20,000 a year)

Front 8

Parameter

Back 8

A numerical measurement describing some characteristic of a POPULATION (P)

Front 9

Statistic

Back 9

A numerical measurement describing some characteristic of a SAMPLE (S)

Front 10

Two types of statistics

Back 10

Descriptive statistics and inferential statistics

Front 11

Descriptive statistics

Back 11

Uses numerical and graphical methods to describe the data collected (summary without inferences)

Front 12

Inferential statistics

Back 12

Uses the sample to make an inference or deduction about the population from which the sample came. (Conclusions)

Front 13

Data can be broken into

Back 13

Categorical/qualitative:data that can be separated into different categories OR quantitative: data that consists of numbers

Front 14

Types of categorical/qualitative data

Back 14

Nominal: measurements that simply classify the data of the sample or population into categories with no ordering scheme. (Ex: eye color, serial number on cans)

Ordinal: measurements that enable the data to be ordered with respect to the variable of interest, like best to worst. (Ranks of colleges in US)

Front 15

Types of quantitative data

Back 15

Interval: measurements that provide meaningful amounts of differences between data (ex: body temps in C or F, years of elections or Olympics) there is no inherent starting point at 0.

Ratio: provide meaningful information; include an inherent 0 starting point. (Heights, lengths, distances, volumes) there are no negative numbers.

Front 16

Discrete data

Back 16

Results from a finite number of possible values (you can count it like the number of fish in a pond)

Front 17

Continuous data

Back 17

Results from infinitely many possible values (has decimals and is precise detail)

Front 18

Types of sampling

Back 18

Random, stratified, systematic, cluster, convenience, voluntary response, multistage

Front 19

Random sampling

Back 19

Unbiased, each member of the population has an equal chance of being selected

Front 20

Stratified sampling

Back 20

Subdivide the population into at least 2 different subpopulations and draw a sample from each subpopulation (Democrats and Republicans)

Front 21

Systematic sampling

Back 21

Select every kth element in the population (ex: every 20th person who walks into a store)

Front 22

Cluster sampling

Back 22

Divide the population into sections and randomly select a few of those sections and include all members in them (picking certain classes in the college and using every student in there)

Front 23

Convenience sampling

Back 23

Use results that are readily available and willing to participate (may have bias) ex: asking someone

Front 24

Voluntary response sampling

Back 24

Respondents choose themselves (ex: survey in an email)

Front 25

Multistage sampling

Back 25

Continue to select successively smaller regions within the population in stages. At each stage pick a random sample or other type of sample