# LINEST

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

### Sample Usage

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

`LINEST(B2:B10, A2:A10, FALSE, TRUE)`

### Syntax

`LINEST(known_data_y, [known_data_x], [calculate_b], [verbose])`

• `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.
• `calculate_b` - [ OPTIONAL - `TRUE` by default ] - Given a linear form of `y = m*x+b`, calculates the y-intercept (`b`) if `TRUE`. Otherwise, forces `b` to be `0` and only calculates the `m` values if `FALSE`, i.e. forces the curve fit to pass through the origin.

• `verbose` - [ OPTIONAL - `FALSE` by default ] - A flag specifying whether to return additional regression statistics or only the linear coefficients and the y-intercept (default).

• If `verbose` is `TRUE`, in addition to the set of linear coefficients for each independent variable and the `y`-intercept, `LINEST` returns

• The standard error for each coefficient and the intercept,

• The coefficient of determination (between 0 and 1, where 1 indicates perfect correlation),

• Standard error for the dependent variable values,

• The F statistic, or F-observed value indicating whether the observed relationship between dependent and independent variables is random rather than linear,

• The degrees of freedom, useful in looking up F statistic values in a reference table to estimate a confidence level,

• The regression sum of squares, and

• The residual sum of squares.

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