Microsoft Office Tutorials and References
In Depth Information
Now, make the columns in the Regression Summary Output section of your
spreadsheet wider so that you can read all of the column headings clearly.
Now, change the data in the following two cells to Number format (2 decimal
Next, change this cell to four decimal places: B67
Now, change the format for all other numbers that are in decimal format to
number format, three decimal places, and center all numbers within their cells.
Save the resulting file as: eggs36
Print the file so that it fits onto one page. ( Hint: Change the scale under “Page
Layout” to 60% to make it fit.) Your file should be like the file in Fig. 6.27 .
Note the following problem with the summary output.
Whoever wrote the computer program for this version of Excel made a mistake
and gave the name: “Multiple R” to cell A53.
This is not correct. Instead, cell A53 should say: “correlation r” since this is the
notation that we are using for the correlation between X and Y.
You can now use your printout of the regression analysis to find the regression
equation that is the best-fitting straight line through the data points.
But first, let’s review some basic terms. Finding the y-intercept, a, of the Regression Line
The point on the y-axis that the regression line would intersect the y-axis if it were
extended to reach the y-axis is called the “y-intercept” and we will use the letter “a”
to stand for the y-intercept of the regression line . The y-intercept on the
SUMMARY OUTPUT on the following page is 24.73 and appears in cell B66 .This
means that if you were to draw an imaginary line continuing down the regression
line toward the y-axis that this imaginary line would cross the y-axis at 24.73. This
is why it is called the “y-intercept.” Finding the Slope, b, of the Regression Line
The “tilt” of the regression line is called the “slope” of the regression line. It
summarizes to what degree the regression line is either above or below a horizontal
line through the data points. If the correlation between X and Y were zero, the
regression line would be exactly horizontal to the X-axis and would have a zero
If the correlation between X and Y is positive, the regression line would “slope
upward to the right” above the X-axis . Since the regression line in Figure 6.27
slopes upward to the right, the slope of the regression line is
0.0165 as given in
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