Statistics: A Primer Lecture BigData Analytics Julian M. Kunkel julian.kunkel@googlemail.com University of Hamburg / German Climate Computing Center (DKRZ) 2017-11-27 Disclaimer: Big Data software is constantly updated, code samples may be outdated. Descriptive Statistics Distribution of Values Inductive Statistics Summary Outline 1 Descriptive Statistics 2 Distribution of Values 3 Inductive Statistics 4 Summary Julian M. Kunkel Lecture BigData Analytics, WiSe 17/18 2 / 34 Descriptive Statistics Distribution of Values Inductive Statistics Summary Statistics: Overview Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data [21] Either describe properties of a sample or infer properties of a population Important terms [10] Unit of observation: the entity described by the data, e.g., people Unit of analysis: the major entity that is being analyzed Example: Observe income of each person, analyse differences of countries Statistical population: Complete set of items that share at least one property that is subject of analysis Subpopulation share additional properties, e.g., gender of people Sample: (sub)set of data collected and/or selected from a population If chosen properly, they can represent the population There are many sampling methods, we can never capture ALL items Independence: one observation does not effect another Example: select two people living in Germany randomly Dependent: select one household and pick a married couple Julian M. Kunkel Lecture BigData Analytics, WiSe 17/18 3 / 34 Descriptive Statistics Distribution of Values Inductive Statistics Summary Statistics: Variables Dependent variable: represents the output/effect Example: word count of a Wikipedia article; income of people Independent variable: assumed input/cause/explanation Example: number of sentences; age, educational level Characterization Univariate analysis: characterize a single variable Bivari