Basic Analysis and Graphing - SAS

Chapter 4 Performing Bivariate Analysis 113
Nonpar Density
Table 4.13 Description of the Correlation Report (Continued)
Correlation
Signif. Prob
Number
The Pearson correlation coefficient. If there is an exact linear relationship between two
variables, the correlation is 1 or –1 depending on whether the variables are positively or
negatively related. If there is no relationship, the correlation tends toward zero.
For more information, see “Statistical Details for the Correlation Report” on page 126.
Probability of obtaining, by chance alone, a correlation with greater absolute value than
the computed value if no linear relationship exists between the X and Y variables.
Gives the number of observations used in the calculations.
Related Information
• “Fitting Menus” on page 115
Nonpar Density
When a plot shows thousands of points, the mass of points can be too dark to show patterns in density.
Using the Nonpar Density (nonparametric density) option makes it easier to see the patterns.
Bivariate density estimation models a smooth surface that describes how dense the data points are at each
point in that surface. The plot adds a set of contour lines showing the density (Figure 4.16). The contour
lines are quantile contours in 5% intervals. This means that about 5% of the points are below the lowest
contour, 10% are below the next contour, and so on. The highest contour has about 95% of the points
below it.
Figure 4.16 Example of Nonpar Density
Nonparametric Bivariate Density Report
The nonparametric bivariate density report shows the kernel standard deviations used in creating the
nonparametric density.