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

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

Types of Data

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

Metadata types

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

Para data

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Nominal scale

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Ordinal scale

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Interval scale

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Ratio measurement

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Types of variables

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Sampling

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Advantages of sampling

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Cluster sampling

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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

Metadata types
Structural metadata: 
  • 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
Reference metadata:
  • 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

Advantages of sampling
  • 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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