
This page includes Step-by-Step instructions to use MS Excel to calculate the standardized regression equation for the Temperature/Mowing Time/Water example.
Example:
Assume that during a three-hour period spent outside, a person recorded the
outside temperature, the time
spent mowing the grass, and their water consumption.
Use Excel to find the standardized
regression equation for the amount of water consumed
based on the temperature and the time
spent mowing. You will still have to input some of the formulas
to standardize the coefficients when using Excel.
| Water Consumption (ounces) | Temperature (F) | Time mowing the grass (hours) |
| 16 | 75 | 1.85 |
| 20 | 83 | 1.25 |
| 25 | 85 | 1.5 |
| 27 | 85 | 1.75 |
| 32 | 92 | 1.15 |
| 48 | 97 | 1.75 |
| 48 | 99 | 1.6 |
Preliminaries:
Entering
the data.
Procedure for Finding the Standardized Regression Equation
Type mean in A9 and standard deviation in A10 then calculate the mean and standard deviation for each column. (If you need help with this, refer to the mean and standard deviation tutorial.)
Type in 'New Water' in cell E1 then hit Enter.
In the formula toolbar, click the = sign and type in: (1/SQRT(6))*((B2-B$9)/B$10). Click OK.
The value -0.47536 should appear in cell E2.
Now click the bottom right corner of this cell and drag it all the way down to cell E8. This will apply the formula to all of those cells.
Type in 'New Temp' in cell F1 and 'New Mowing' in cell G1.
Click on cell E2. In the toolbar, select edit>copy then click on cell F2 and select edit>paste.
Now click on the bottom right corner of cell F2 and drag it across to cell G2 and down both of those columns to copy the formula.
Your table values should be the same as the ones below.
|
Water |
Temperature |
Mowing |
New Water |
New Temp |
New Mowing |
|
|
16 |
75 |
1.85 |
-0.47536 |
-0.62692 |
0.46017899 |
|
|
20 |
83 |
1.25 |
-0.34738 |
-0.24112 |
-0.460179 |
|
|
25 |
85 |
1.5 |
-0.1874 |
-0.14467 |
-0.0766965 |
|
|
27 |
85 |
1.75 |
-0.12341 |
-0.14467 |
0.306786 |
|
|
32 |
92 |
1.15 |
0.036566 |
0.192897 |
-0.613572 |
|
|
48 |
97 |
1.75 |
0.548489 |
0.434019 |
0.306786 |
|
|
48 |
99 |
1.6 |
0.548489 |
0.530467 |
0.0766965 |
|
|
mean |
30.85714 |
88 |
1.55 |
|||
|
standard deviation |
12.75968 |
8.465616733 |
0.266145 |
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*Don't forget to review the terms from the standardized multiple regression tutorial!*
Learn the Procedure for finding the Standardized Multiple Regression Equation
Regression Tutorial Menu Dictionary