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Using the JMP program, I input the data and it came up with the equation of a line to find the number of 100s of board feet per tree. The equation JMP calculated is 100s of Board Feet = -77.85915 + 1.7097418(Age of Tree) We know that the equation is linear and the correlation between the age of the tree and board feet is strong, we see this in the value of r-squared, which is 0.886148. The closer the value is to 1, the stronger the correlation. This means we can make a very accurate prediction of the board feet per tree using the regression equation.

If we want to fill in the missing spots in the table, we simply plug our information into the equation. For example, using a 60 year old tree we find that the board feet (x) is:

X=-77.85915+1.7097418(60)=24.70593, or 24. Doing this for the remaining points in the graph we get 161 for a 140-year-old tree and 229 for a 180-year-old tree. Now let’s try to predict some other values not on the graph. How about for trees that are 300, 500, and 1000 years old.

100s of Board Feet = -77.85915 + (1.7097418)(300)=435 100s of Board Feet = -77.85915 + (1.7097418)(500)=777 100s of Board Feet = -77.85915 + (1.7097418)(1000)=1631

We could also look at this from another point of view. Instead now let’s have the board feet and predict the age of the tree. Pick any arbitrary number, say 368.

Age of Tree	100s of Board Feet
20	1
40	6
60	24
80	33
100	56
120	88
140	161
160	182
180	229
200	320
260	368
300	435
500	777
1000	1631
39,501	67,459