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Anhang B: Referenz 571
8992APPB.DEU TI-89/TI-92 Plus:8992appb.doc (German) LOGOS GROUP Revised: 04/18/00 2:44 PM Printed: 04/19/00 2:38 PM Page 571 of 34
Regression Beschreibung
LnReg
Verwendet die Methode der kleinsten Quadrate und
die transformierten Werte ln(x) und y, um an folgende
Modellgleichung anzugleichen:
y=a+b ln(x)
Logistic
Verwendet die Methode der kleinsten Quadrate, um an
folgende Modellgleichung anzugleichen:
y=a/(1+b*e^(c*x))+d
MedMed
Verwendet die Methode der Median-Median-Geraden
zur Berechnung der statistischen Häufungspunkte x1,
y1, x2, y2, x3 und y3, und gleicht an folgende
Modellgleichung an:
y=ax+b
wobei a die Steigung und b der y-Achsenabschnitt ist.
PowerReg
Verwendet die Methode der kleinsten Quadrate und die
transformierten Werte ln(x) und ln(y), um an folgende
Modellgleichung anzugleichen:
y=ax
b
QuadReg
Verwendet die Methode der kleinsten Quadrate, um an
folgendes Polynom zweiten Grades anzugleichen:
y=ax
2
+bx+c
Bei drei Datenpunkten ist die Gleichung ein Polynom;
bei vier oder mehr Datenpunkten ist sie eine
polynomische Regression. Es sind mindestens drei
Datenpunkte erforderlich.
QuartReg
Verwendet die Methode der kleinsten Quadrate, um an
folgendes Polynom vierten Grades anzugleichen:
y=ax
4
+bx
3
+cx
2
+dx+e
Bei fünf Datenpunkten ist die Gleichung ein Polynom;
bei sechs oder mehr Datenpunkten ist sie eine
polynomische Regression. Es sind mindestens fünf
Datenpunkte erforderlich.
SinReg
Verwendet die Methode der kleinsten Quadrate, um an
folgende Modellgleichung anzugleichen:
y=a sin(bx+c)+d
588


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