Chapter 12: Statistics 311
PwrReg (power regression) fits the model equation y=axb to the data using a least-
squares fit and transformed values ln(x) and ln(y). It displays values for a and b; when
DiagnosticOn is set, it also displays values for r2 and r.
PwrReg [Xlistname,Ylistname,freqlist,regequ]
Logistic—
Logistic—Logistic—
Logistic—c/
c/c/
c/(1+a
(1+a(1+a
(1+a…e
ee
e-bx)
))
)
Logistic fits the model equation y=c/(1+a…eLbx) to the data using an iterative least-squares
fit. It displays values for a, b, and c.
Logistic [Xlistname,Ylistname,freqlist,regequ]
SinReg—a sin(bx+c)+d
SinReg—a sin(bx+c)+dSinReg—a sin(bx+c)+d
SinReg—a sin(bx+c)+d
SinReg (sinusoidal regression) fits the model equation y=asin(bx+c)+d to the data using
an iterative least-squares fit. It displays values for a, b, c, and d. At least four data points
are required. At least two data points per cycle are required in order to avoid aliased
frequency estimates.
SinReg [iterations,Xlistname,Ylistname,period,regequ]
iterations is the maximum number of times the algorithm will iterate to find a solution. The
value for iterations can be an integer ‚1 and 16; if not specified, the default is 3. The
algorithm may find a solution before iterations is reached. Typically, larger values for
iterations result in longer execution times and better accuracy for SinReg, and vice versa.
A period guess is optional. If you do not specify period, the difference between time values
in Xlistname must be equal and the time values must be ordered in ascending sequential