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An addendum or appendix, in general, is an addition required to be made to a document by its author subsequent to its printing or publication. It comes from the gerundive addendum , plural addenda , "that which is to be added", from addere [ 1 ] ( lit.
The title page often shows the title of the work, the person or body responsible for its intellectual content, and the imprint, which contains the name and address of the book's publisher and its date of publication. [2] Particularly in paperback editions it may contain a shorter title than the cover or lack a descriptive subtitle.
The English-language titles of compositions (books and other print works, songs and other audio works, films and other visual media works, paintings and other artworks, etc.) are given in title case, in which every word is given an initial capital except for certain less important words (as detailed at Wikipedia:Manual of Style/Capital letters ...
The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is usually determined based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power. In complex studies ...
When appendix sections are used, they should appear at the bottom of an article, with ==level 2 headings==, [h] followed by the various footers. When it is useful to sub-divide these sections (for example, to separate a list of magazine articles from a list of books), this should be done using level 3 headings ( ===Books=== ) instead of ...
The format and location of the page numbers is a matter of style for the publisher. If the page numbers appear after the heading text, they might be preceded by characters called leaders, usually dots or periods, that run from the chapter or section titles on the opposite side of the page, or the page numbers might remain closer to the titles ...
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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]