# TREND

### Definition

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

### 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.

`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.

A
B
C
1
2
1
138130
3
2
139100
4
3
139900
5
4
141120
6
5
141890
7
6
141890
8
7
143230
9
8
145290
10
9
141120
11
10
141890
12
11
143230
13
12
145290
14
TREND
13
145053.6364
15
14
145337.8788
16
15
145607.9155
17
16
145854.1215
18
17
146181.7334
19
18
146566.6951
20
19
146884.8987
21
20
147380.8355
22
21
148312.9226
23
22
148576.1218
24
23
148763.4365
25
24
148993.5171

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