Texas Instruments TI-89 Voyage 200, Руководство пользователя

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Statistics and Data Plots 547
LinReg Linear regression — Fits the data to the model y=ax+b
(where a is the slope, and b is the y-intercept) using a least-
squares fit and x and y.
LnReg Logarithmic regression — Fits the data to the model
equation
y=a+b ln(x) using a least-squares fit and
transformed values ln(x) and y .
Logistic Logistic regression — Fits the data to the model
y=a/(1+b
ù
e^(c
ù
x))+d and updates all the system statistics
variables.
MedMed Median-Median — Fits the data to the model y=ax+b (where
a is the slope, and b is the y-intercept) using the median-
median line, which is part of the resistant line technique.
Summary points
medx1 , medy1 , medx2 , medy2 , medx3 ,
and medy3 are calculated and stored to variables, but they
are not displayed on the STAT VARS screen.
PowerReg Power regression — Fits the data to the model equation
y=ax
b
using a least-squares fit and transformed values ln(x)
and ln(y) .
QuadReg Quadratic regression — Fits the data to the second-order
polynomial
y=ax
2
+bx+c . You must have at least three data
points.
• For three points, the equation is a polynomial fit.
• For four or more points, it is a polynomial regression.
Calc Type Description