TRENDGiven partial data about a linear trend, fits an ideal linear trend using the least squares method and/or predicts further values.

Sample Usage

TREND(B2:B10,A2:A10)

TREND(B2:B10,A2:A10,A11:A13,TRUE)

Syntax

TREND(known_data_y, [known_data_x], [new_data_x], [b])

known_data_y – The array or range containing dependent (y) values that are already known, used to curve fit an ideal linear trend.

If known_data_y is a two-dimensional array or range, known_data_x must have the same dimensions or be omitted.

If known_data_y is a one-dimensional array or range, known_data_x may represent multiple independent variables in a two-dimensional array or range. I.e. if known_data_y is a single row, each row in known_data_x is interpreted as a separated independent value, and analogously if known_data_y is a single column.

known_data_x – [ OPTIONAL – {1,2,3,…} with same length as known_data_y by default ] – The values of the independent variable(s) corresponding with known_data_y.

If known_data_y is a one-dimensional array or range, known_data_x may represent multiple independent variables in a two-dimensional array or range. I.e. if known_data_y is a single row, each row in known_data_x is interpreted as a separated independent value, and analogously if known_data_y is a single column.

new_data_x – [ OPTIONAL – same as known_data_x by default ] – The data points to return the y values for on the ideal curve fit.

The default behavior is to return the ideal curve fit values for the same x inputs as the existing data for comparison of known y values and their corresponding curve fit estimates.

b – [ OPTIONAL – TRUE by default ] – Given a general exponential form of y = m*x+b for a curve fit, calculates b if TRUE or forces b to be 0 and only calculates the m values if FALSE, i.e. forces the curve fit to pass through the origin.

See Also

LOGEST: Given partial data about an exponential growth curve, calculates various parameters about the best fit ideal exponential growth curve.

LINEST: Given partial data about a linear trend, calculates various parameters about the ideal linear trend using the least-squares method.

GROWTH: Given partial data about an exponential growth trend, fits an ideal exponential growth trend and/or predicts further values.

Examples