General Definitions

Question 1

DCOVA Framework

Answer 1

Define, Collect, Organize. Visualize, Analyze data.

Question 2

What are data?

Answer 2

In statistics, data are “the values associated with a trait or property that help distinguishing the occurrences of something”.

Question 3

Variable

Answer 3

A characteristic of an item or individual.

Question 4

Descriptive Statistics

Answer 4

Refer to methods that primarily help summarize and present data.

Question 5

Inferential Statistics

Answer 5

Refer to methods that use data collected from a small group to reach conclusions about a larger group.

Question 6

Big Data

Answer 6

The collections of data that cannot be easily browsed or analyzed using traditional methods.

Question 7

Categorical Variables

Answer 7

Take categories as their values (also known as qualitative variables)

Question 8

Numerical Variables

Answer 8

Have values that represent a counted or measured quantity (also known as quantitative variables).

Question 9

Discrete Variables

Answer 9

(Numerical variables) Are numerical values that arise from a counting process (e.g. total amount paid).

Question 10

Continuous Variables

Answer 10

(Numerical variables) Are numerical values that arise from a measuring process and those values depend on the precision of the measuring instrument used (e.g. distance form home to store).

Question 11

Primary Data Source

Answer 11

Data collected on your own.

Question 12

Secondary Data Source

Answer 12

Data collected by someone else.

Question 13

Population

Answer 13

Consists of all the items or individuals about which you want to reach conclusions.

Question 14

Parameter

Answer 14

When you analyze data from a population you compute a parameter.

Question 15

Sample

Answer 15

A portion of a population selected for analysis.

Question 16

Statistics

Answer 16

When you analyze data from a sample you compute statistics.

Question 17

Structured Data

Answer 17

Refers to all types of data that are structured or organized in any form.

Question 18

Unstructured Data

Answer 18

Data having very little or no repeating structure or organization.

Question 19

Frame

Answer 19

A complete or partial listing of the items that make up the population from which the sample will be selected.

Question 20

Nonprobability Sample

Answer 20

Selecting items or individuals without knowing their probabilities of selection.
ADVANTAGES: convenience, speed, and low cost.
CANNOT be used for statistical inference.

Question 21

Probability Sample

Answer 21

Selecting items or individuals based on known probabilities.

Question 22

Convenience and Judgement Samples

Answer 22

Subcategories of nonprobability sample.
In a convenience sample you select items that are easy, inexpensive, or convenient to sample.
In a judgement sample you collect the opinions of preselected experts in the subject matter.

Question 23

Simple Random Sample

Answer 23

Subcategory of probability sample. It is the most elementary sampling technique. every item from a frame has the same chance of selection as every other item, and every sample of a fixed size has the same chance of selection as every other sample of that size.
Sampling with replacement means that after you select an item, you return it to the frame, where it has the same probability of being selected again.
Sampling without replacement means that once you select an item, you cannot select it again.

Question 24

Systematic Sample

Answer 24

Subcategory of probability sample. In a systematic sample, you partition the N items in the frame into n groups of k items, where K=N/n

Question 25

Stratified Sample

Answer 25

Subcategory of probability sample. In a stratified sample, you first subdivide the N items in the frame into separate subpopulations,
or strata. A stratum is defined by some common characteristic, such as gender or year in school. You select a simple random sample within each of the strata and combine the results from the separate simple random samples. Stratified sampling is more efficient than either simple random sampling or systematic sampling because you are ensured of the representation of items across the entire population.

Question 26

Cluster Sample

Answer 26

Subcategory of probability sample. In a cluster sample, you divide the N items in the frame into clusters that contain several items. Clusters are often naturally occurring groups, such as counties, election districts, city blocks, households, or sales territories. You then take a random sample of one or more clusters and study all items in each selected cluster.

Question 27

Coverage Error

Answer 27

Coverage error occurs if certain groups of items are excluded from the frame so that they have no chance of being selected in the sample or if items are included from outside the frame. Coverage error results in a selection bias.

Question 28

Nonresponse Error

Answer 28

Nonresponse error arises from failure to collect data on all items in the sample and results in a nonresponse bias.

Question 29

Sampling Error

Answer 29

Sampling error reflects the variation, or “chance differences,” from sample to sample, based on the probability of particular individuals or items being selected in the particular samples. This margin of error is the sampling error.

Question 30

Measurement Error

Answer 30

Certain information is impossible or impractical to obtain directly. When surveys rely on self-reported information, the mode of data collection, the respondent to the survey, and or the survey itself can be possible sources of measurement error.