Engineering Fundamentals: An Introduction to Engineering, 4th ed.c

450 Chapter 14 Electronic Spreadsheets

Load (N)

25

20

15

10

5

0

0 10203040

Deflection (mm)

y  0.5542 x

■Figure 14.16 The linear fit to data of Example 14.10.

Load (N)

25

20

15

10

5

0

0 10203040

Deflection (mm)

F  0.5542 x, where F  load (N),

and x  deflection (mm)

■Figure 14.17 The edited linear fit to data of Example 14.10.

TABLE 14.9 The Comparison between the Measured

and Predicted Spring Force

The Measured The Predicted

Deflection of the Measured Spring Force (N) using:

Spring, x(mm) Force (N) F0.5542 x

9 5.0 5.0

17 10 9.4

29 15.0 16.1

35 20.0 19.4

In order to examine how good the linear equationF0.5542x fits the data, we compare

the force results obtained from the equation to the actual data points as shown in Table 14.9.

As you can see the equation fits the data reasonably well.

Example 14.11 Find the equation that best fits the following set of data points in Table 14.10.

We first plot the data points using the XY (Scatter) without the data points connected as

shown in Figure 14.18.

Right-click on any of the data points to add a trendline. From the plot of the data points,

it should be obvious that an equation describing the relationship betweenxandyis nonlinear.

Select a polynomial of second order (Order: 2), toggle on theDisplay equation on chartand

theDisplay R-squared value on chart, as shown in Figure 14.19. After you press close, you

should see the equation yx

2

3x2 and R

2

1 on the chart, as shown in Figure 14.20.

TheR

2

is called the coefficient of determination, and its value provides an indication of how

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