### 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) True or False to specify whether to force the constant b to equal 0.

#### Remarks

 * This function can return an array.* 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.office.com

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