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In Stata, this test is performed by the command estat bgodfrey. [7] [8] In SAS, the GODFREY option of the MODEL statement in PROC AUTOREG provides a version of this test. In Python Statsmodels, the acorr_breusch_godfrey function in the module statsmodels.stats.diagnostic [9]
The five-number summary gives information about the location (from the median), spread (from the quartiles) and range (from the sample minimum and maximum) of the observations. Since it reports order statistics (rather than, say, the mean) the five-number summary is appropriate for ordinal measurements, as well as interval and ratio measurements.
(Radar charts are used to examine the relative values for a single data point (e.g., point 3 is large for variables 2 and 4, small for variables 1, 3, 5, and 6) and to locate similar points or dissimilar points.) [5] Are there outliers? Radar charts are a useful way to display multivariate observations with an arbitrary number of variables. [6]
Format is a function in Common Lisp that can produce formatted text using a format string similar to the print format string.It provides more functionality than print, allowing the user to output numbers in various formats (including, for instance: hex, binary, octal, roman numerals, and English), apply certain format specifiers only under certain conditions, iterate over data structures ...
Stata's proprietary file formats have changed over time, although not every Stata release includes a new dataset format. Every version of Stata can read all older dataset formats, and can write both the current and most recent previous dataset format, using the saveold command. [11]
Bivariate analysis is one of the simplest forms of quantitative (statistical) analysis. [1] It involves the analysis of two variables (often denoted as X, Y), for the purpose of determining the empirical relationship between them. [1] Bivariate analysis can be helpful in testing simple hypotheses of association.
Pearson's correlation coefficient is the covariance of the two variables divided by the product of their standard deviations. The form of the definition involves a "product moment", that is, the mean (the first moment about the origin) of the product of the mean-adjusted random variables; hence the modifier product-moment in the name.
With any number of random variables in excess of 1, the variables can be stacked into a random vector whose i th element is the i th random variable. Then the variances and covariances can be placed in a covariance matrix, in which the (i, j) element is the covariance between the i th random variable and the j th one.