User Guide

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Curve Estimati
on

Plot models. Plots the values of the dependent variable and each selected model
against the
independent variable. A separate chart is produced for each dependent
variable.
 Display ANOVA table. Displays a summary analysis-of-variance table for each
selected model.
Curve Estim
ation Models
You can choose one or more curve estimation regression models. To determine
which model to use, plot your data. If your variables appear to be related linearly,
useasimpl
e linear regression model. When your variables are not linearly related,
try transforming your data. When a transformation does not help, you may need a
more complicated model. View a scatterplot of your data; if the plot resembles
amathema
tical function you recognize, fit your data to that type of model. For
example, if your data resemble an exponential function, use an exponential model.
Linear. Model whose equation is Y = b0 + (b1 * t). The series values are modeled as a
linear fu
nction of time.
Logarithmic. Model whose equation is Y = b0 + (b1 * ln(t)).
Inverse. Model whose equation is Y = b0 + (b1 / t).
Quadrati
c.
Model whose equation is Y = b0 + (b1 * t) + (b2 * t**2). The quadratic
model can be used to model a series which "takes off" or a series which dampens.
Cubic. Model defined by the equation Y = b0 + (b1 * t) + (b2 * t**2) + (b3 * t**3).
Power. Mo
del whose equation is Y = b0 * (t**b1) or ln(Y) = ln(b0) + (b1 * ln(t)).
Compound. Model whose equation is Y = b0 * (b1**t) or ln(Y) = ln(b0) + (ln(b1) * t).
S-curve. Model whose equation is Y = e**(b0 + (b1/t)) or ln(Y) = b0 + (b1/t).
Logistic
.
Model whose equation is Y = 1 / (1/u + (b0 * (b1**t))) or ln(1/y-1/u)= ln
(b0) + (ln(b1)*t) where u is the upper boundary value. After selecting Logistic,
specify the upper boundary value to use in the regression equation. The value must be
apositi
ve number, greater than the largest dependent variable value.
Growth. Model whose equation is Y = e**(b0 + (b1 * t)) or ln(Y) = b0 + (b1 * t).
Exponential. Model whose equation is Y = b0 * (e**(b1 * t)) or ln(Y) = ln(b0) +
(b1*t).