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R: propensity score matching is available as part of the MatchIt, [7] [8] optmatch, [9] or other packages. SAS: The PSMatch procedure, and macro OneToManyMTCH match observations based on a propensity score. [10] Stata: several commands implement propensity score matching, [11] including the user-written psmatch2. [12]
ISBN 1-58603-426-X. Zhu, Xiaoping; Kuljaca, Ognjen (2005). "A Short Preview of Free Statistical Software Packages for Teaching Statistics to Industrial Technology Majors" (PDF). Journal of Industrial Technology. 21 (2). Archived from the original (PDF) on October 25, 2005.
PDF/A (since 2005 - series of ISO 19005 standards) - a.k.a. "PDF for Archive" - Document management - Electronic document file format for long-term preservation (working in ISO Technical committee 171), based on PDF 1.4 and later also ISO 32000-1 - PDF 1.7; PDF/E (since 2008 - ISO 24517) - a.k.a. "PDF for Engineering" - Document management ...
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]
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]
Before PDF version 1.5, the table would always be in a special ASCII format, be marked with the xref keyword, and follow the main body composed of indirect objects. Version 1.5 introduced optional cross-reference streams, which have the form of a standard stream object, possibly with filters applied. Such a stream may be used instead of the ...
The origin of the theorem is uncertain, but it was well-established in the realm of linear regression before the Frisch and Waugh paper. George Udny Yule's comprehensive analysis of partial regressions, published in 1907, included the theorem in section 9 on page 184. [8]
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.