# GROWTH

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

### Sample Usage

`GROWTH(B2:B10,A2:A10)`

`GROWTH(B2:B10,A2:A10,A11:A13)`

### Syntax

`GROWTH(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 exponential growth curve.

• 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 = b*m^x` for a curve fit, calculates `b` if `TRUE` or forces `b` to be `1` and only calculates the `m` values if `FALSE`.

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