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The correlation coefficient (a value between -1 and +1) tells you how strongly two variables are related to each other.
To use the Analysis Toolpak add-in in Excel to quickly generate correlation coefficients between multiple variables, execute the following steps.
Excel Data Analysis Tools: In addition to the various correlation functions described elsewhere, Excel provides the Covariance and Correlation data analysis tools.
Example 1: We expand the data in Example 2 of One Sample Hypothesis Testing for Correlation to include a number of other statistics.
Using Excel’s Correlation data analysis tool we can compute the pairwise correlation coefficients for the various variables in the table in Figure 1. Observation: Suppose we look at the relationship between GPA (grade point average) and Salary 5 years after graduation and discover there is a high correlation between these two variables.
In this case, it is possible that the correlation between GPA and Salary is a consequence of the correlation between IQ and GPA and between IQ and Salary.

Note that we can calculate B in a number of ways: (A + B –  A, (B + C) – C, (A + B + C) – (A + C), etc. It turns out that this can also be calculated by first finding the correlation matrix for R1, then taking its inverse and then selecting the kth element on the diagonal.
The Bibliography of the website contains a number of good references which covers these topics.
All variables give a significant correlation factor When I did the correlation factor by using excel data analysis.
We can use the CORREL function or the Analysis Toolpak add-in in Excel to find the correlation coefficient between two variables. To test this we need to determine the correlation between GPA and Salary eliminating the influence of IQ from both variables, i.e. At the end of that page you will find the supplemental function RSquare(R1,k) which calculates the square of this correlation coefficient.

This arrangement is for systematically analyze the large number of variable or computation of complex relationship of variable (play with multivariate relation) . WINKS reads Excel files, so you can read your Excel data into WINKS and do many more types of analysis than in Excel alone. If there are 4 (or more variables), I refer to a matrix (or range in Excel) R1 which contains the data for all 4 variables and then look for the function which gives the correlation coefficient of the kth variable in R1 with respect to the other variables in R1.
I have computed the correlation between variable 1 and 2 using the correlation function in Analysis ToolPak. This means that the being white (W) has virtually no impact on the correlation between P and I (and the little impact that it has is a dampening effect).