The nature of statistical data

If one tries to correlate the core and log data for the entire reservoir, the correlation coefficient is usually not very high. For example, consider the dataset in Fig. The correlation coefficient between core permeability at net overburden pressure is only 0. However, if the cores containing visible natural or coring induced fractures are removed and only cores from clean, fluvial-deltaic channel sands are correlated, the correlation coefficient between permeability and porosity increases to 0.

The nature of statistical data

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Please help improve this article by adding citations to reliable sources. Unsourced material may be challenged and removed. June Learn how and when to remove this template message In statisticsgroups of individual data points may be classified as belonging to any of various statistical data types, e.

The data type is a fundamental component of the semantic content of the variable, and controls which sorts of probability distributions can logically be used to describe the variable, the permissible operations on the variable, the type of regression analysis used to predict the variable, etc.

The concept of data type is similar to the concept of level of measurementbut more specific: For example, count data require a different distribution e.

The nature of statistical data

Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one transformation.

The nature of statistical data

Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary as in the case with longitude and temperature measurements in Celsius or Fahrenheitand permit any linear transformation.

Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.

Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variableswhereas ratio and interval measurements are grouped together as quantitative variableswhich can be either discrete or continuousdue to their numerical nature.

Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data typepolytomous categorical variables with arbitrarily assigned integers in the integral data typeand continuous variables with the real data type involving floating point computation.

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But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented. Other categorizations have been proposed. For example, Mosteller and Tukey [1] distinguished grades, ranks, counted fractions, counts, amounts, and balances.

Nelder [2] described continuous counts, continuous ratios, count ratios, and categorical modes of data. See also Chrisman[3] van den Berg Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer" Hand,p.

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Regardless of the logical possible values, all of these data types are generally coded using real numbersbecause the theory of random variables often explicitly assumes that they hold real numbers.The purpose of this page is to provide resources in the rapidly growing area of computer-based statistical data analysis.

This site provides a web-enhanced course on various topics in statistical data analysis, including SPSS and SAS program listings and introductory routines.

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Using Excel for Data Analysis Southeast Michigan Population Characteristics, Ring Color Key The tool above provides a visual representation of the change over time in the median household income and the racial composition of the population in the City of Detroit and the surrounding communities and townships. There are four variables shown on this chart:
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