Computes the covariance between two columns using the population method. Source values can be of Integer or Decimal type.

**Covariance** measures the joint variation between two sets of values. The sign of the covariance tends to show the linear relationship between the two datasets; positive covariance indicates that the numbers tend to increase with each other.

- The magnitude of the covariance is difficult to interpret, as it varies with the size of the source values.
- The normalized version of covariance is the correlation coefficient, in which covariance is normalized between -1 and 1. For more information, see CORREL Function.

This function is calculated across the entire population.

- For more information on a sampled version of this function, see COVARSAMP Function.

**Wrangle vs. SQL:** This function is part of Wrangle, a proprietary data transformation language. Wrangle is not SQL. For more information, see Wrangle Language.

## Basic Usage

`covar(squareFootage,purchasePrice)`

**Output:** Returns the covariance between the values in the `squareFootage`

column and the `purchasePrice`

column.

## Syntax and Arguments

`covar(function_col_ref1,<span>function_col_ref2</span>) [group:group_col_ref] [limit:limit_count]`

Argument | Required? | Data Type | Description |
---|---|---|---|

function_col_ref1 | Y | string | Name of column that is the first input to the function |

function_col_ref2 | Y | string | Name of column that is the second input to the function |

For more information on the `group`

and `limit`

parameters, see Pivot Transform.

For more information on syntax standards, see Language Documentation Syntax Notes.

### function_col_ref1, function_col_ref2

Name of the column the values of which you want to calculate the covariance. Column must contain Integer or Decimal values.

- Literal values are not supported as inputs.
- Multiple columns and wildcards are not supported.

**
Usage Notes:
**

Required? | Data Type | Example Value |
---|---|---|

Yes | String (column reference) | `myInputs` |

## Examples

**Tip:** For additional examples, see Common Tasks.

`CORREL`

- Correlation co-efficient between two columns. See CORREL Function.`COVAR`

- Calculates the covariance between two columns. See COVAR Function.`COVARSAMP`

- Calculates the covariance between two columns using the sample population method. See COVARSAMP Function.

**Source:**

The following table contains height in inches and weight in pounds for a set of students.

Student | heightIn | weightLbs |
---|---|---|

1 | 70 | 134 |

2 | 67 | 135 |

3 | 67 | 147 |

4 | 67 | 160 |

5 | 72 | 136 |

6 | 73 | 146 |

7 | 71 | 135 |

8 | 63 | 145 |

9 | 67 | 138 |

10 | 66 | 138 |

11 | 71 | 161 |

12 | 70 | 131 |

13 | 74 | 131 |

14 | 67 | 157 |

15 | 73 | 161 |

16 | 70 | 133 |

17 | 63 | 132 |

18 | 64 | 153 |

19 | 64 | 156 |

20 | 72 | 154 |

**Transformation:**

You can use the following transformations to calculate the correlation co-efficient, the covariance, and the sampling method covariance between the two data columns:

Transformation Name | `New formula` |
---|---|

Parameter: Formula type | `Single row formula` |

Parameter: Formula | `round(correl(heightIn, weightLbs), 3)` |

Parameter: New column name | `'corrHeightAndWeight'` |

Transformation Name | `New formula` |
---|---|

Parameter: Formula type | `Single row formula` |

Parameter: Formula | `round(covar(heightIn, weightLbs), 3)` |

Parameter: New column name | `'covarHeightAndWeight'` |

Transformation Name | `New formula` |
---|---|

Parameter: Formula type | `Single row formula` |

Parameter: Formula | `round(covarsamp(heightIn, weightLbs), 3)` |

Parameter: New column name | `'covarHeightAndWeight-Sample'` |

**Results:**

Student | heightIn | weightLbs | covarHeightAndWeight-Sample | covarHeightAndWeight | corrHeightAndWeight |
---|---|---|---|---|---|

1 | 70 | 134 | -2.876 | -2.732 | -0.074 |

2 | 67 | 135 | -2.876 | -2.732 | -0.074 |

3 | 67 | 147 | -2.876 | -2.732 | -0.074 |

4 | 67 | 160 | -2.876 | -2.732 | -0.074 |

5 | 72 | 136 | -2.876 | -2.732 | -0.074 |

6 | 73 | 146 | -2.876 | -2.732 | -0.074 |

7 | 71 | 135 | -2.876 | -2.732 | -0.074 |

8 | 63 | 145 | -2.876 | -2.732 | -0.074 |

9 | 67 | 138 | -2.876 | -2.732 | -0.074 |

10 | 66 | 138 | -2.876 | -2.732 | -0.074 |

11 | 71 | 161 | -2.876 | -2.732 | -0.074 |

12 | 70 | 131 | -2.876 | -2.732 | -0.074 |

13 | 74 | 131 | -2.876 | -2.732 | -0.074 |

14 | 67 | 157 | -2.876 | -2.732 | -0.074 |

15 | 73 | 161 | -2.876 | -2.732 | -0.074 |

16 | 70 | 133 | -2.876 | -2.732 | -0.074 |

17 | 63 | 132 | -2.876 | -2.732 | -0.074 |

18 | 64 | 153 | -2.876 | -2.732 | -0.074 |

19 | 64 | 156 | -2.876 | -2.732 | -0.074 |

20 | 72 | 154 | -2.876 | -2.732 | -0.074 |

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