TREND(known_y's [,known_x's] [,new_x's] [,const]) 
Returns the yvalues along a linear trend given a set of xvalues. 
known_y's  The set of yvalues you already know in the relationship y = mx + b. 
known_x's  (Optional) The set of xvalues that you may already know in the relationship y = mx + b. 
new_x's  (Optional) The set of xvalues for which you want the corresponding yvalues. 
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 yvalues 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 mvalues 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 yvalues and column B contains xvalues. 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 

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