Qmb Chpt 1 Where Why How Of Data Collection

Front 1

Population

Back 1

A set of all items or individuals of interest

Examples:
- All likely voters in the next election
- All parts produced today
- All sales receipts for November

Front 2

Sample

Back 2

A subset of the population

Examples:
- 1000 voters selected at random for interview
- A few parts selected for destructive testing
- Every 100th receipt selected for audit

Front 3

Why Sample?

Back 3

- Less time consuming than a census
- Less costly to administer than a census
- It may not be possible to conduct a census
- It is possible to obtain statistical results of a sufficiently high precision based on samples.

Front 4

Simple Random Samples

Back 4

- Every individual or item from the population has an equal chance of being selected
- Selection may be with replacement or without replacement
- Samples can be obtained from a table of random numbers or computer random number generators

Front 5

Parameter

Back 5

a summary measure computed to describe a characteristic of the population

Front 6

Statistic

Back 6

a summary measure computed to describe a characteristic of the sample

Front 7

Inferential Statistics

Back 7

Making statements about a population by examining sample results

Sample Statistics (known) --> Inference --> Population Parameters (unknown but can be estimated from sample evidence)

Front 8

Inferential Statistics Cont.

Back 8

Drawing conclusions and/or making decisions concerning a population based on sample results

Front 9

Estimation

Back 9

Estimate the population mean by using sample mean

Front 10

Hypothesis Testing

Back 10

Use sample evidence to test the claim that the population mean is something

Front 11

Data Types

Back 11

Data
Qualitative v. Quantitative
(categorical) (numerical)
Ex. Discrete v Continuous
- marital status
- political party
- eye color

Front 12

Discrete v. Continuous

(Quantitative Data Collection)

Back 12

Discrete - Counted Items
Ex.
- # of Children
- Defects per hour

Continuous - Measured Characteristics
Ex.
- Weight
- Voltage

Front 13

Data Types

Time Series v. Cross Sectional

Back 13

Time Series
- A set of ordered data values observed at successive points in time

Cross Sectional
- A set of data values observed at a fixed point in time