![]() With growth data, often the variation goes up as Y goes up. When you fit any model with nonlinear regression, you assume that the variation of residuals is Gaussian with the same SD all the way along the curve. Consider fitting a line (linear regression) to transformed data It is computed as the reciprocal of K.ĭoubling-time is in the time units of the X axis. Tau is the time constant, expressed in the same units as the X axis. ![]() If X is in minutes, then K is expressed in inverse minutes. K is the rate constant, expressed in reciprocal of the X axis time units. To do this, go to the Constrain tab of the nonlinear regression dialog, set the drop down next to Y0 to "Constant equal to" and enter its value. If so, you should constrain that parameter to be a constant value. In many cases, you will know this value precisely. The parameter Y0 is the Y value at time zero. Consider constraining Y0 to a constant value If you transform all the values to logarithms, then it rarely would make sense to fit this equation.Īfter entering data, click Analyze, choose nonlinear regression, choose the panel of exponential equations, and choose Exponential growth. Note that Y values must be the actual values. If you have several experimental conditions, place the first into column A, the second into column B, etc. Enter time into X, and response (cell number. This equation describes the growth with a constant doubling time.
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