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In general, John Aitchison defined compositional data to be proportions of some whole in 1982. [1] In particular, a compositional data point (or composition for short) can be represented by a real vector with positive components. The sample space of compositional data is a simplex: = {= [,, …,] | >, =,, …,; = =}.
The concept of data type is similar to the concept of level of measurement, but more specific. For example, count data requires a different distribution (e.g. a Poisson distribution or binomial distribution) than non-negative real-valued data require, but both fall under the same level of measurement (a ratio scale).
Not all multiples are based on earnings or cash flow drivers. The price-to-book ratio (P/B) is a commonly used benchmark comparing market value to the accounting book value of the firm's assets. The price/sales ratio and EV/sales ratios measure value relative to sales. These multiples must be used with caution as both sales and book values are ...
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The phenomenon of spurious correlation of ratios is one of the main motives for the field of compositional data analysis, which deals with the analysis of variables that carry only relative information, such as proportions, percentages and parts-per-million. [3] [4] Spurious correlation is distinct from misconceptions about correlation and ...
This is a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. General tests [ edit ]
The S&P 500 is trading at about 22 times earnings estimates for the next 12 months, well above the long-term average P/E ratio of 15.8, according to LSEG Datastream.
Certain types of problems involving multivariate data, for example simple linear regression and multiple regression, are not usually considered to be special cases of multivariate statistics because the analysis is dealt with by considering the (univariate) conditional distribution of a single outcome variable given the other variables.