TREND
Given 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 twodimensional array or range,known_data_x
must have the same dimensions or be omitted. 
If
known_data_y
is a onedimensional array or range,known_data_x
may represent multiple independent variables in a twodimensional array or range. I.e. ifknown_data_y
is a single row, each row inknown_data_x
is interpreted as a separated independent value, and analogously ifknown_data_y
is a single column.


known_data_x
 [ OPTIONAL {1,2,3,...}
with same length asknown_data_y
by default ]  The values of the independent variable(s) corresponding withknown_data_y
. If
known_data_y
is a onedimensional array or range,known_data_x
may represent multiple independent variables in a twodimensional array or range. I.e. ifknown_data_y
is a single row, each row inknown_data_x
is interpreted as a separated independent value, and analogously ifknown_data_y
is a single column.
 If

new_data_x
 [ OPTIONAL  same asknown_data_x
by default ]  The data points to return they
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 knowny
values and their corresponding curve fit estimates.
 The default behavior is to return the ideal curve fit values for the same

b
 [ OPTIONAL TRUE
by default ]  Given a general exponential form ofy = m*x+b
for a curve fit, calculatesb
ifTRUE
or forcesb
to be0
and only calculates them
values ifFALSE
, 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 leastsquares method.
GROWTH
: Given partial data about an exponential growth trend, fits an ideal exponential growth trend and/or predicts further values.