TREND

TREND(known_y's [,known_x's] [,new_x's] [,const])

Returns the y-values along a linear trend given a set of x-values.

known_y's	The set of y-values you already know in the relationship y = mx + b.
known_x's	(Optional) The set of x-values that you may already know in the relationship y = mx + b.
new_x's	(Optional) The set of x-values for which you want the corresponding y-values.
const	(Optional) A logical value indicating whether to force the constant b to equal 0: True (<> 0) = b is calculated normally (default) False (or 0) = b is set to the value 0

REMARKS

* This function can return multiple values.
* Fits a straight line (using the method of least squares) to the arrays "known_y" and "known_x". Returns the y-values along that line for the array of new_x's that you specify.
* If the array known_y's is in a single column, then each column of known_x's is interpreted as a separate variable.
* If the array known_y's is in a single row, then each row of known_x's is interpreted as a separate variable.
* The array known_x's can include one or more sets of variables. If only one variable is used, "known_y" and "known_x" can be ranges of any shape, as long as they have equal dimensions. If more than one variable is used, known_y's must be a vector (that is, a range with a height of one row or a width of one column).
* If "known_x" is left blank, it is assumed to be the array {1,2,3,} that is the same size as known_y's.
* "new_x" must include a column (or row) for each independent variable, just as known_x's does. So, if "known_y" is in a single column, known_x's and new_x's must have the same number of columns. If known_y's is in a single row, known_x's and new_x's must have the same number of rows.
* If "new_x" is left blank, it is assumed to be the same as "known_x".
* If both "known_x" and "new_x" are left blank, they are assumed to be the array {1,2,3,} that is the same size as known_y's.
* If "const" = True, then, b is calculated normally.
* If "const" = False, then b is set equal to 0 (zero), and the m-values are adjusted so that y = mx.
* If "const" is left blank, then True is used.
* You can use this function for polynomial curve fitting by regressing against the same variable raised to different powers. For example, suppose column A contains y-values and column B contains x-values. You can enter x^2 in column C, x^3 in column D, and so on, and then regress columns B through D against column A.
* When entering an array constant for an argument such as known_x's, use commas to separate values in the same row and semicolons to separate rows.
* Suppose a business wants to purchase a tract of land in July, the start of the next fiscal year. The business collects cost information that covers the most recent 12 months for a typical tract in the desired area. Known_y values are in cells B2:B13; the known_y values are $133,890, $135,000, $135,790, $137,300, $138,130, $139,100, $139,900, $141,120, $141,890, $143,230, $144,000, $145,290.
* The company can expect a typical tract of land to cost about $150,244 if it waits until July. The preceding formula uses the default array {1;2;3;4;5;6;7;8;9;10;11;12} for the known_x's argument, corresponding to the 12 months of sales data. The array {13;14;15;16;17} corresponds to the next five months.
* For the Microsoft documentation refer to support.microsoft.com
* For the Google documentation refer to support.google.com

	A
1	TREND(B2:B13,,{13;14;15;16;17})