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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