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Excel maintains 15 figures in its numbers, but they are not always accurate; mathematically, the bottom line should be the same as the top line, in 'fp-math' the step '1 + 1/9000' leads to a rounding up as the first bit of the 14 bit tail '10111000110010' of the mantissa falling off the table when adding 1 is a '1', this up-rounding is not undone when subtracting the 1 again, since there is no ...
Formulas in the B column multiply values from the A column using relative references, and the formula in B4 uses the SUM() function to find the sum of values in the B1:B3 range. A formula identifies the calculation needed to place the result in the cell it is contained within. A cell containing a formula, therefore, has two display components ...
[[Category:Date-computing templates based on current time]] to the <includeonly> section at the bottom of that page. Otherwise, add <noinclude>[[Category:Date-computing templates based on current time]]</noinclude> to the end of the template code, making sure it starts on the same line as the code's last character.
Up until the 2007 version, Microsoft Excel used a proprietary binary file format called Excel Binary File Format (.XLS) as its primary format. [39] Excel 2007 uses Office Open XML as its primary file format, an XML-based format that followed after a previous XML -based format called "XML Spreadsheet" ("XMLSS"), first introduced in Excel 2002.
Time series: random data plus trend, with best-fit line and different applied filters. In mathematics, a time series is a series of data points indexed (or listed or graphed) in time order. Most commonly, a time series is a sequence taken at successive equally spaced points in time.
From a model based perspective, we are interested in estimating the variance of the weighted mean when the different are not i.i.d random variables. An alternative perspective for this problem is that of some arbitrary sampling design of the data in which units are selected with unequal probabilities (with replacement).
The earliest reference to a similar formula appears to be Armstrong (1985, p. 348), where it is called "adjusted MAPE" and is defined without the absolute values in the denominator. It was later discussed, modified, and re-proposed by Flores (1986).
In the statistical analysis of time series, autoregressive–moving-average (ARMA) models are a way to describe a (weakly) stationary stochastic process using autoregression (AR) and a moving average (MA), each with a polynomial. They are a tool for understanding a series and predicting future values.