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2. File:Statistics.pdf - Wikipedia

   en.wikipedia.org/wiki/File:Statistics.pdf

   You are free: to share – to copy, distribute and transmit the work; to remix – to adapt the work; Under the following conditions: attribution – You must give appropriate credit, provide a link to the license, and indicate if changes were made.

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. 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.

5. 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

6. Prediction interval - Wikipedia

   en.wikipedia.org/wiki/Prediction_interval

   Given a sample from a normal distribution, whose parameters are unknown, it is possible to give prediction intervals in the frequentist sense, i.e., an interval [a, b] based on statistics of the sample such that on repeated experiments, X n+1 falls in the interval the desired percentage of the time; one may call these "predictive confidence intervals".

7. 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.