Correlation and Regression

Hai Jody,

Ah now things are clearer.

# data appears to be large enough. In the Scatterplot you should see a point (#18) far away from the regression line.
Maybe analysis is improved when this point is removed from the data. But this may only be done if you can find out why this point shows different behaviour than the other points.

Both Correlation and Regression analysis say that there is a reasonable strong relation between Y and X (p-value=0.005).

But r=0.367 only so the relation is not strong.  In Industry often -0.5<r<0.5 is considered as useless.
The Regression agrees because it says R-sq=13.5%.
So this X is NOT THE ONLY X that influences the Y (assumption: the X influences the Y at all). This X only explains an influence of 13.5% on Y and there iss till missing 86.5%.

Next: check if R-sq goes up when not using all 65 points (visual screening of the data; do they form several subgroups that should be analysed seperately). Find out what other X could possibly be missing (this is why DMAIC6 is thinking about X's first and analysing patterns later).

If you have that X: do you have measurements and perform Regression again with the new X. If you'r unlucky the X you have started with shows up in a Pareto as #1 and you have a lot of other X's with even smaller R-sq.
Note: If you have >1 X analyse them all together because interactions can play a role.

So you did nothing wrong (as far as I can tell) you just happen to have an X with a weak influence on Y.

Good luck, Remi