Outline of statistics

The Outline of statistics is a structured guide to the subject of statistics, which is the discipline that concerns the collection, organization, analysis, interpretation, and presentation of data. This outline provides an overview of and topical guide to the various concepts, measures, and techniques that are commonly used in the field of statistics.

Overview of statistics

Statistics is a branch of mathematics dealing with data collection, data analysis, interpretation, presentation, and organization of data. Statistics applies mathematical theories and formulas to make sense of data gathered from research, surveys, and experiments. It is essential in a wide range of academic disciplines, including biology, economics, engineering, medicine, public health, psychology, marketing, and many more.

Branches of statistics

• Descriptive statistics – Methods for summarizing or describing a set of data.
• Inferential statistics – Methods for making inferences about the larger population from which a sample is drawn.
• Biostatistics – Specialized branch of statistics applied to biological or health sciences.
• Business statistics – Application of statistical methods in business and commercial contexts.
• Econometrics – Application of statistical methods to economic data to give empirical content to economic relations.
• Psychological statistics – Application of statistical methods to data in psychology and related fields.

Key concepts in statistics

• Data – Quantitative or qualitative information from which statistics are derived.
• Population – The complete set of items from which data can be selected.
• Sample – A subset of the population used to represent the entire group as a whole.
• Variable – Any characteristic, number, or quantity that can be measured or counted.
• Mean – The average of the numbers in a data set.
• Median – The middle value in a data set.
• Mode – The value that appears most frequently in a data set.
• Standard deviation – A measure of the amount of variation or dispersion in a set of values.

Statistical methods

• Regression analysis – Techniques for modeling and analyzing several variables.
• ANOVA – Analysis of variance, a collection of statistical models.
• Time series analysis – Techniques for analyzing time series data.
• Multivariate statistics – Involves observation and analysis of more than one statistical outcome variable at a time.

Statistical tools

• Statistical software – Software used for statistical analysis. Common examples include R, SAS, and SPSS.
• Survey methodology – Study of survey methods and how survey data are collected and interpreted.

History of statistics

• History of statistics – The development of statistical thinking from early empirical science to modern statistical methods.

See also

• List of statistical topics
• Glossary of statistics

External links

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