This example illustrates the following two-column statistical functions:
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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