# EXAMPLE - Two-Column Statistical Functions

This example illustrates statistical functions that can be applied across two columns of values.

Functions:

Item

Description

CORREL Function

Computes the correlation coefficient between two columns. Source values can be of Integer or Decimal type.

COVAR Function

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

COVARSAMP Function

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

ROUND Function

Rounds input value to the nearest integer. Input can be an Integer, a Decimal, a column reference, or an expression. Optional second argument can be used to specify the number of digits to which to round.

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 Single row formula round(correl(heightIn, weightLbs), 3) 'corrHeightAndWeight'
 Transformation Name New formula Single row formula round(covar(heightIn, weightLbs), 3) 'covarHeightAndWeight'
 Transformation Name New formula Single row formula round(covarsamp(heightIn, weightLbs), 3) '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