Business Statistics Ch 7

Front 1

Define Element

Back 1

The entry on which data are collected.

Front 2

Define Population

Back 2

The collection of all the elements of interest.

Front 3

Define Sample

Back 3

A subset of the population.

Front 4

What is the "sampled population?"

Back 4

The population from which the sample is drawn.

i.e. all registered voters in Texas

Front 5

What is a sample frame?

Back 5

A list of the elements that the sample will be selected from.

i.e. a list of all registered voters in Texas.

Front 6

Define Parameters

Back 6

Numerical characteristics of a population.

i.e. population mean annual salary, population standard deviation of annual salary, etc.

Front 7

Define Simple Random Sample
(Finite Population)

Back 7

A simple random sample of size n from a finite population of size N is a sample selected such that each possible sample of size n has the same probability of being selected.

Front 8

Give an example of constructing a frame

Back 8

Assigning each element on a list of 2500 a number of 1 to 2500

Front 9

Define Sampling Without Replacement

Back 9

Once an element has been included in the sample, it is removed from the population and cannot be selected a second time.

Front 10

Define Sampling With Replacement

Back 10

Once an element has been included in the sample, it is returned to the population. A previously selected element can be selected again and therefore may appear in the sample more than once.

Front 11

Define Sample Statistic

Back 11

Front 12

Define Point Estimator

Back 12

Front 13

Define Point Estimate

Back 13

The value of a point estimator used in a particular instance as an estimate of a population parameter.

Front 14

Define Target Population

Back 14

The population for which statistical inference such as point estimates are made. It is important for the target population to correspond as closely as possible to the sampled population.

Front 15

Define Sampling Distribution

Back 15

A probability distribution consisting of all possible values of a sample statistic.

Front 16

Define Unbiased

Back 16

A property of a point estimator that is present when the expected value of the point estimator is equal to the population parameter it estimates.

Front 17

Define Standard Error

Back 17

The standard deviation of a point estimator.

Front 18

Define Relative Efficiency

Back 18

Given two unbiased point estimators of the same population parameter, the point estimator with the samller standard error is more efficient.

Front 19

Define Consistency

Back 19

A property of a point estimator that is present whenever larger sample sizes tend to proved point estimates closer to the population parameter.

Front 20

Define Stratified Random Sampling

Back 20

A probability sampling method in which the population is first divided into strata and a simple random sample is then taken from each stratum.

Front 21

Define Cluster Sampling

Back 21

A probability sampling method in which the population is first divided into clusters and then a simple random sample of the clusters is taken.

Front 22

Define Systematic Sampling

Back 22

A probability sampling method in which we randomly select on of the first k elements and then select every kth element thereafter.

Front 23

Define Convenience Sampling

Back 23

A nonprobability method of sampling whereby elements are selected for the sample on the basis of convencience.

Front 24

Define Judgment Sampling

Back 24

A nonprobability method of sampling whereby elements are selected for the sample based on the judgement of the person doing the study.

Front 25

What is the finite population correction factor?

Back 25

Front 26

Define The Central Limit Theorem

Back 26

Front 27

Back 27

Front 28

Back 28

Front 29

Back 29

Front 30

Back 30

Front 31

Back 31

Front 32

Back 32

Front 33

What is the formula to determine the numbers of different simple random samples of size n that can be selected from a finite population of size N?

Back 33

Front 34

Define a Random Sample for an Infinite Population

Back 34

A random sample of size n from an infinite population is a sample selected such that the following conditions are satisfied:
1) Each element selected comes from the same population
2) Each element is selected independently

Front 35

Compare population parameter and point estimator

Back 35

Population parameter is the actual value (if a census was taken) but a point estimator is the value created from the sample.

Front 36

When a population is finite, what percentage of a population can the sample be in order to use the standard deviation formula for an infinite population?

Back 36

5% or less

Front 37

What are the three properties of good point estimators?

Back 37

Unbiased, efficiency, and consistency