!known_ys,known_xs,const,stats!!Returns statistics that describe a linear trend matching known data points, by fitting a straight line using the least squares method!is the set of y-values you already know in the relationship y = mx + b!is an optional set of x-values that you may already know in the relationship y = mx + b!is a logical value: the constant b is calculated normally if Const = TRUE or omitted; b is set equal to 0 if Const = FALSE!is a logical value: return additional regression statistics = TRUE; return m-coefficients and the constant b = FALSE or omitted!
known_ys,known_xs!Calculates the point at which a line will intersect the y-axis by using a best-fit regression line plotted ...
known_ys,known_xs!Returns the slope of the linear regression line through the given data points!is an array or cell range ...
known_ys,known_xs!Returns the square of the Pearson product moment correlation coefficient through the given data points!is ...
known_ys,known_xs!Returns the standard error of the predicted y-value for each x in a regression!is an array or range of ...
known_ys,known_xs,const,stats!Returns statistics that describe a linear trend matching known data points, by fitting a straight ...
known_ys,known_xs,const,stats!Returns statistics that describe an exponential curve matching known data points!is the set ...
known_ys,known_xs,new_xs,const!Returns numbers in a linear trend matching known data points, using the least squares method!is ...
known_ys,known_xs,new_xs,const!Returns numbers in an exponential growth trend matching known data points!is the set of y-values ...
Limits the number of integer solutions found by the Branch & Bound algorithm, or the Evolutionary engine, before Solver pauses ...