You want to compute confidence intervals along with your curve fits.

See the following discussion.

You can compute confidence intervals for the estimated values from your curve fit, or you can compute confidence intervals for the parameters in the model itself.

You can readily compute confidence intervals for the values predicted by a regression equation. In this case, you'd report estimated values as *y* ± *y*
_{c}, where *y* is the estimated value and each ±*y*
_{c} is the confidence limit corresponding to some degree of confidence (probability), *c*. To compute confidence limits, we'll utilize the *Student's t-distribution*
, which comes from an area of statistics called *small sampling theory*
. Excel provides support for the t-distribution through several built-in functions. See Recipe 5.6 for more information.

Let's reconsider the example from Recipe 8.6. Figure 8-12 shows the result of a nonlinear curve fit, along with upper and lower confidence limits. Figure 8-11 shows the original data along with the fit parameters. It also shows several other statistics I computed for this example. The ones of interest here are t-value and Confidence Interval, shown in cells M13 and M14.

Before computing these values, you must set up some auxiliary calculations. First you must compute the sum of squared residuals as shown in cell M5. The formula used in this example is `=SUMSQ(H5:H41)`

, which is just the sum ...

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