Microsoft Office Tutorials and References
In Depth Information
Y
¼
24.73
þ
49.5
Y
¼
74.23 (000) or 74,230 eggs (since the eggs are measured in thousands of eggs)
Important note: If you look at your chart, if you go directly upwards for a weight of
3000 until you hit the regression line, you see that you hit this line between 70 and
80 on the y-axis to the left when you draw a line horizontal to the x-axis (actually, it
is 74.23), the result above for predicting eggs produced from a weight of 3000
grams.
Now, let’s do a second example and predict what the number of eggs produced
would be if we used a weight of 3500 grams.
Y
¼
24.73
þ
0.0165 X
Y
¼
24.73
þ
0.0165 (3500)
Y
¼
24.73
þ
57.75
Y
¼
82.48 or 82,480 eggs
Important note: If you look at your chart, if you go directly upwards for a weight of
3500 until you hit the regression line, you see that you hit this line between 80 and
90 on the y-axis to the left (actually it is 82.48), the result above for predicting the
number of eggs produced from a fish weighing 3500 grams.
For a more detailed discussion of regression, see Black ( 2010 ) and Gould et al.
(2002).
6.6 Adding the Regression Equation to the Chart
Objective : To Add the Regression Equation to the Chart
If you want to include the regression equation within the chart next to the
regression line, you can do that, but a word of caution first.
Throughout this topic, we are using the regression equation for one predictor and
one criterion to be the following:
Y
¼
a
þ
bX
(6.3)
where a
¼
y-intercept and b
¼
slope of the line
See, for example, the regression equation in Sect. 6.5.3 where the y-intercept
was a
¼
24.73 and the slope of the line was b
¼þ
0.0165 to generate the following
regression equation:
Y
¼
24.73
þ
0.0165 X
However, Excel 2007 uses a slightly different regression equation (which is
logically identical to the one used in this topic) when you add a regression equation
to a chart:
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