Correlations and Covariances

Statistics > Regression > Correlations

 

Calculates the Pearson product moment correlation coefficient between each pair of variables you list.

The correlation coefficient is used to quantify the strength of the linear relationship between two variables.

 

Dialog box items

Variables:
Choose the columns you want to describe.
To select nonconsecutive columns, press and hold down CTRL, and then click each item.
You can select many columns. Use the Ctrl key to select (or unselect) distant columns.

Report:
The display of outputs of VisualStat.

 

Data

Data must be in numeric columns of equal length. Missing value are omitted.

 

Statistics

Type:
Choose correlations, and covariances:

oCorrelations: Choose to display correlations.

oCovariances: Choose to display covariances.

Store Matrix:
Check to store correlation or covariance matrix.

Unbiased:
Check to use unbiased statistics in calculations.

 

Example

Human Height and Weight are mostly hereditable, but lifestyles, diet, health and environmental factors also play a role in determining individual's physical characteristics. The dataset below contains 25,000 records of human heights (in inches) and weights (in pounds). These data were obtained in 1993 by a Growth Survey of 25,000 children from birth to 18 years of age recruited from Maternal and Child Health Centres (MCHC) and schools and were used to develop Hong Kong's current growth charts for weight, height, weight-for-age, weight-for-height and body mass index (BMI).

Source: http://wiki.stat.ucla.edu/socr/index.php/SOCR_Data_Dinov_020108_HeightsWeights

1.Open the DataBook reg.vstz

2.Select the sheet Human

3.Choose the tab Statistics, the group Regression and the command Correlation

4.In Variables, select Height and Weight.

5.Click OK

 

Report window output

 

Correlations

       Height  Weight

Height  1.0000  0.5029

Weight  0.5029  1.0000

 

 

 

See Also:


Report | Numeric Formats

Web Resource: Probability and statistics EBook