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The conventions of this class calculate the number of days between two dates (e.g., between Date1 and Date2) as the Julian day difference. This is the function Days(StartDate, EndDate). The conventions are distinguished primarily by the amount of the CouponRate they assign to each day of the accrual period.
The number of days between two dates, which is simply the difference in their Julian day numbers. The dates of moveable holidays, like Christian Easter (the calculation is known as Computus) followed up by Ascension Thursday and Pentecost or Advent Sundays, or the Jewish Passover, for a given year. Converting a date between different calendars.
A frequency distribution shows a summarized grouping of data divided into mutually exclusive classes and the number of occurrences in a class. It is a way of showing unorganized data notably to show results of an election, income of people for a certain region, sales of a product within a certain period, student loan amounts of graduates, etc.
The statistical treatment of count data is distinct from that of binary data, in which the observations can take only two values, usually represented by 0 and 1, and from ordinal data, which may also consist of integers but where the individual values fall on an arbitrary scale and only the relative ranking is important. [example needed]
computes the difference in seconds between two time_t values time: returns the current time of the system as a time_t value, number of seconds, (which is usually time since an epoch, typically the Unix epoch). The value of the epoch is operating system dependent; 1900 and 1970 are often used. See RFC 868. clock
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In statistics and econometrics, cross-sectional data is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at a single point or period of time. Analysis of cross-sectional data usually consists of comparing the differences among selected subjects, typically with no regard to differences in time.
Data binning, also called data discrete binning or data bucketing, is a data pre-processing technique used to reduce the effects of minor observation errors. The original data values which fall into a given small interval, a bin , are replaced by a value representative of that interval, often a central value ( mean or median ).