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A data set (or dataset) is a collection of data. In the case of tabular data, a data set corresponds to one or more database tables, where every column of a table represents a particular variable, and each row corresponds to a given record of the data set in question. The data set lists values for each of the variables, such as for example ...
if more than one variable is measured, a measure of statistical dependence such as a correlation coefficient; A common collection of order statistics used as summary statistics are the five-number summary, sometimes extended to a seven-number summary, and the associated box plot.
In statistics, an influential observation is an observation for a statistical calculation whose deletion from the dataset would noticeably change the result of the calculation. [1] In particular, in regression analysis an influential observation is one whose deletion has a large effect on the parameter estimates.
Analysis of extreme observations: outlying observations in the data are analyzed to see if they seem to disturb the distribution. [112] Comparison and correction of differences in coding schemes: variables are compared with coding schemes of variables external to the data set, and possibly corrected if coding schemes are not comparable. [113]
This DOMAIN code is used in the dataset name, the value of the DOMAIN variable within that dataset, and as a prefix for most variable names in the dataset. The dataset structure for observations is a flat file representing a table with one or more rows and columns. Normally, one dataset is submitted for each domain.
Some measures that are commonly used to describe a data set are measures of central tendency and measures of variability or dispersion. Measures of central tendency include the mean, median and mode, while measures of variability include the standard deviation (or variance), the minimum and maximum values of the variables, kurtosis and skewness ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Standard for structuring data such that "each variable is a column, each observation is a row, and each type of observational unit is a table". It is equivalent to Codd's third normal form. [4] time domain time series time series analysis time series forecasting treatments Variables in a statistical study that are conceptually manipulable.