---
title: statsmodels.linearRegression() function
description: >
  `statsmodels.linearRegression()` performs a linear regression.
menu:
  flux_v0_ref:
    name: statsmodels.linearRegression
    parent: contrib/anaisdg/statsmodels
    identifier: contrib/anaisdg/statsmodels/linearRegression
weight: 301
---

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`statsmodels.linearRegression()` performs a linear regression.

It calculates and returns [*ŷ*](https://en.wikipedia.org/wiki/Hat_operator#Estimated_value) (`y_hat`),
and [residual sum of errors](https://en.wikipedia.org/wiki/Residual_sum_of_squares) (`rse`).
Output data includes the following columns:

- **N**: Number of points in the calculation.
- **slope**: Slope of the calculated regression.
- **sx**: Sum of x.
- **sxx**: Sum of x squared.
- **sxy**: Sum of x*y.
- **sy**: Sum of y.
- **errors**: Residual sum of squares.
  Defined by `(r.y - r.y_hat) ^ 2` in this context
- **x**: An index [1,2,3,4...n], with the assumption that the timestamps are regularly spaced.
- **y**: Field value
- **y\_hat**: Linear regression values

##### Function type signature

```js
(
    <-tables: stream[A],
) => stream[{
    B with
    y_hat: float,
    y: float,
    x: float,
    sy: H,
    sxy: G,
    sxx: F,
    sx: E,
    slope: D,
    errors: float,
    N: C,
}] where A: Record, D: Divisible + Subtractable
```

{{% caption %}}
For more information, see [Function type signatures](/flux/v0/function-type-signatures/).
{{% /caption %}}

## Parameters

### tables

Input data. Default is piped-forward data (`<-`).




## Examples

### Perform a linear regression on a dataset

```js
import "contrib/anaisdg/statsmodels"
import "sampledata"

sampledata.float()
    |> statsmodels.linearRegression()

```