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2. File:Employee Attrition Prediction.pdf - Wikipedia

   en.wikipedia.org/wiki/File:Employee_Attrition...

   English: We aim to predict whether an employee of a company will leave or not, using the k-Nearest Neighbors algorithm. We use evaluation of employee performance, average monthly hours at work and number of years spent in the company, among others, as our features.

3. PDF - Wikipedia

   en.wikipedia.org/wiki/PDF

   Many commercial offset printers have accepted the submission of press-ready PDF files as a print source, specifically the PDF/X-1a subset and variations of the same. [83] The submission of press-ready PDF files is a replacement for the problematic need for receiving collected native working files.

4. List of PDF software - Wikipedia

   en.wikipedia.org/wiki/List_of_PDF_software

   Open-source, cross-platform C library to generate PDF files. OpenPDF: GNU LGPLv3 / MPLv2.0: Open source library to create and manipulate PDF files in Java. Fork of an older version of iText, but with the original LGPL / MPL license. PDFsharp: MIT C# developer library to create, extract, edit PDF files. Poppler: GNU GPL

5. Confidence and prediction bands - Wikipedia

   en.wikipedia.org/wiki/Confidence_and_prediction...

   Confidence bands can be constructed around estimates of the empirical distribution function.Simple theory allows the construction of point-wise confidence intervals, but it is also possible to construct a simultaneous confidence band for the cumulative distribution function as a whole by inverting the Kolmogorov-Smirnov test, or by using non-parametric likelihood methods.

6. Best linear unbiased prediction - Wikipedia

   en.wikipedia.org/.../Best_linear_unbiased_prediction

   Best linear unbiased predictions" (BLUPs) of random effects are similar to best linear unbiased estimates (BLUEs) (see Gauss–Markov theorem) of fixed effects. The distinction arises because it is conventional to talk about estimating fixed effects but about predicting random effects, but the two terms are otherwise equivalent.

7. Explanatory power - Wikipedia

   en.wikipedia.org/wiki/Explanatory_power

   A theory or explanation is hard to vary if all details play a functional role, i.e., cannot be varied or removed without changing the predictions of the theory. Easy to vary (i.e., bad) explanations, in contrast, can be varied to be reconciled with new observations because they are barely connected to the details of the phenomenon of question.