# Multivariate Statistics at Universität Potsdam

## Flashcards and summaries for Multivariate Statistics at the Universität Potsdam

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## Study with flashcards and summaries for the course Multivariate Statistics at the Universität Potsdam

Types of Data

Para data

Nominal scale

Ordinal scale

Interval scale

### Exemplary flashcards for Multivariate Statistics at the Universität Potsdam on StudySmarter:

Ratio measurement

### Exemplary flashcards for Multivariate Statistics at the Universität Potsdam on StudySmarter:

Types of variables

Sampling

Cluster sampling

### Exemplary flashcards for Multivariate Statistics at the Universität Potsdam on StudySmarter:

Stratified random samples

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## Exemplary flashcards for Multivariate Statistics at the Universität Potsdam on StudySmarter:

Multivariate Statistics

Types of Data
• Micro Data: Individual Data of respondents like people, households or enterprises. The basic commodity for the statistican.
• Macro or tabular data: aggregate micro data, table cells
• Meta data: Data about data. Information about variables, sampling frame, questionnaire etc.

Multivariate Statistics

• Acting as identifiers and descriptors of the data, such as: dimensions of statistical cubes, variables, titles of tables, Nomenclatures (code lists)
• Always be associated with the data to allow their identification, retrieval and browsing
• Acting only as descriptors of the data, they don't help to actually identify the data
• Can be exchanged independently from the data they are related to, but are however often linked to them

Multivariate Statistics

Para data
• Data about the process by which the survey data were collected
• E.g.: day interviews were conducted, how long the interviews took or how many times there were contacts

Multivariate Statistics

Nominal scale
• Just name the attribute uniquely
• No ordering of the cases is implied
• Central tendency given by its mode; neither the mean nor the median can be defined

Multivariate Statistics

Ordinal scale
• Attributes can be rank-ordered
• Distances between attributes do not have any meaning
• Central tendency can be represented by its mode or its median, but the mean cannot be defined

Multivariate Statistics

Interval scale
• Distance between attributes does have a meaning, the interval between values is interpretable
• "zero point" of an interval scale is arbitrary and negative values can be used
• Central tendency can be represented by its mode, its median, or its arithmetic mean

Multivariate Statistics

Ratio measurement
• Always an absolute zero that is meaningful
• Possible to construct a meaningful fraction with a ratio variable
• All statistical measures can be used, as all necessary mathematical operations are defined

Multivariate Statistics

Types of variables
• Discrete or categorical variables: Countable set of categories and often small, the elements are from the set of natural numbers (e.g. sex)
• Continuous variables: infinitely set of possible numbers (e.g. income)

Multivariate Statistics

Sampling
• Sample = selection of units of a given population
• Sampling fraction = share of the population that is selected
• Sample is called representative if the statistical values of interest are equal to the corresponding values based on the whole population

Multivariate Statistics

• Considerably lower cost
• More practicable
• Shorter time for data producing and evaluation
• In general, higher accuracy of results

Multivariate Statistics

Cluster sampling
• Population is fragmented in many small subpopulations (=clusters)
• Only a fraction of the cluster is randomly drawn
• Every single unit of the drawn clusters end up in the sample

Multivariate Statistics

Stratified random samples
• Complete division of the population into disjoint groups
• Is called stratified random sampling if in every stratum an independent simple random sample is drawn

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