# series_fit_line()

Applies linear regression on a series, returning multiple columns.

Takes an expression containing dynamic numerical array as input and does linear regression to find the line that best fits it. This function should be used on time series arrays, fitting the output of make-series operator. The function generates the following columns:

`rsquare`

: r-square is a standard measure of the fit quality. The value's a number in the range [0-1], where 1 - is the best possible fit, and 0 means the data is unordered and doesn't fit any line.`slope`

: Slope of the approximated line ("a" from y=ax+b).`variance`

: Variance of the input data.`rvariance`

: Residual variance that is the variance between the input data values the approximated ones.`interception`

: Interception of the approximated line ("b" from y=ax+b).`line_fit`

: Numerical array holding a series of values of the best fitted line. The series length is equal to the length of the input array. The value's used for charting.

## Syntax

`series_fit_line(`

*x*`)`

## Arguments

*x*: Dynamic array of numeric values.

Tip

The most convenient way of using this function is to apply it to the results of make-series operator.

## Examples

```
print id=' ', x=range(bin(now(), 1h)-11h, bin(now(), 1h), 1h), y=dynamic([2,5,6,8,11,15,17,18,25,26,30,30])
| extend (RSquare,Slope,Variance,RVariance,Interception,LineFit)=series_fit_line(y)
| render timechart
```

RSquare | Slope | Variance | RVariance | Interception | LineFit |
---|---|---|---|---|---|

0.982 | 2.730 | 98.628 | 1.686 | -1.666 | 1.064, 3.7945, 6.526, 9.256, 11.987, 14.718, 17.449, 20.180, 22.910, 25.641, 28.371, 31.102 |