This scratchpad let's you see the build up to the correlationi coefficient. If there's a strong association then each pair of z-scores will be generally in the same direction (pos/neg) and the same quantity (small/medium/large). The correlation is simply the average of the z-score products.

.00

([s1v1]-[mean1])/[SD1]

.00

([s2v1]-[mean1])/[SD1]

.00

([s3v1]-[mean1])/[SD1]

.00

([s4v1]-[mean1])/[SD1]

.00

([s5v1]-[mean1])/[SD1]

.00

([s6v1]-[mean1])/[SD1]

.00

([s1v2]-[mean2])/[SD2]

.00

([s2v2]-[mean2])/[SD2]

.00

([s3v2]-[mean2])/[SD2]

.00

([s4v2]-[mean2])/[SD2]

.00

([s5v2]-[mean2])/[SD2]

.00

([s6v2]-[mean2])/[SD2]

.00

[Z11]*[Z21]

.00

[Z12]*[Z22]

.00

[Z13]*[Z23]

.00

[Z14]*[Z24]

.00

[Z15]*[Z25]

.00

[Z16]*[Z26]

([s1v1]+[s2v1]+[s3v1]+[s4v1]+[s5v1]+[s6v1])/6

([s1v1]-[mean1])**2

([s2v1]-[mean1])**2

([s3v1]-[mean1])**2

([s4v1]-[mean1])**2

([s5v1]-[mean1])**2

([s6v1]-[mean1])**2

[dev2s1v1]+[dev2s2v1]+[dev2s3v1]+[dev2s4v1]+[dev2s5v1]+[dev2s6v1]

[SumOfSquares1]/5

sqrt([Variance1])

([s1v2]+[s2v2]+[s3v2]+[s4v2]+[s5v2]+[s6v2])/6

([s1v2]-[mean2])**2

([s2v2]-[mean2])**2

([s3v2]-[mean2])**2

([s4v2]-[mean2])**2

([s5v2]-[mean2])**2

([s6v2]-[mean2])**2

[dev2s1v2]+[dev2s2v2]+[dev2s3v2]+[dev2s4v2]+[dev2s5v2]+[dev2s6v2]

[SumOfSquares2]/5

sqrt([Variance2])

([s1v1]-[mean1])/[SD1]

([s2v1]-[mean1])/[SD1]

([s3v1]-[mean1])/[SD1]

([s4v1]-[mean1])/[SD1]

([s5v1]-[mean1])/[SD1]

([s6v1]-[mean1])/[SD1]

([s1v2]-[mean2])/[SD2]

([s2v2]-[mean2])/[SD2]

([s3v2]-[mean2])/[SD2]

([s4v2]-[mean2])/[SD2]

([s5v2]-[mean2])/[SD2]

([s6v2]-[mean2])/[SD2]

([Z11]*[Z21])+([Z12]*[Z22])+([Z13]*[Z23])+([Z14]*[Z24])+([Z15]*[Z25])+([Z16]*[Z26])

[SZP]/5

[Corr]*([SD1]/[SD2])

[mean2]-([mean1]*[Slope])

m1: .00

[mean1]

s1: .00

[SD1]

m2: .00

[mean2]

s2: .00

[SD2]

SZP: .00

[SZP]

r: .00

[Corr]

slope: .00

[Slope]

constant: .00

[Constant]

Once you have entered the raw scores for 2 variables above then you can now play around with predicted Y scores. To do that enter a score for your X variable below. It need not be an actual score (although it should be a "possible" score given the variable you are measuring). You can change scores and see the resulting Predicted Y score.

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[Constant]+([Slope]*[actual])