Casio FX-9750G, FX-9860GII ISelecting the Regression Type, IDisplaying Regression Calculation Results, MSe, 2VAR, graph, Example, (xList), (yList), 0.5, 1.2, 2.4, 4.0, –2.1,0.3, 1.5, 2.0

Example	Input the two sets of data shown below and plot the data on a scatter
	diagram. Next, perform logarithmic regression on the data to display the
	regression parameters, and then draw the corresponding regression
	graph.
	0.5, 1.2, 2.4, 4.0, 5.2	(xList)
	–2.1, 0.3, 1.5, 2.0, 2.4	(yList)

KSTAT

?DU@AUACUCUDAUC

A@U?BU@DUAUACU

(GRPH)(SET)A(Scat))(GPH1)

(CALC)(E)(Log)

(DRAW)

•You can perform trace on a regression graph. You cannot perform trace scroll.

•Input a positive integer for frequency data. Other types of values (decimals, etc.) cause an error.

ISelecting the Regression Type

After you graph paired-variable statistical data, you can use the function menu at the bottom of the display to select from a variety of different types of regression.

•{ax+b}/{a+bx}/{Med}/{X^2}/{X^3}/{X^4}/{Log}/{ae^bx}/{ab^x}/{Pwr}/{Sin}/{Lgst} ...

{linear regression (ax+b form)}/{linear regression (a+bx form)}/{Med-Med}/{quadratic regression}/{cubic regression}/{quartic regression}/{logarithmic regression}/{exponential regression (aebx form)}/{exponential regression (abx form)}/{power regression}/ {sinusoidal regression}/{logistic regression} calculation and graphing

•{2VAR}... {paired-variable statistical results}

IDisplaying Regression Calculation Results

Whenever you perform a regression calculation, the regression formula parameter (such as a and b in the linear regression y = ax + b) calculation results appear on the display. You can use these to obtain statistical calculation results.

Regression parameters are calculated as soon as you press a function key to select a regression type, while a graph is on the display.

The following parameters are used by linear regression, logarithmic regression, exponential regression, and power regression.

r ..............	correlation coefficient
r2 .............	coefficient of determination
MSe.........	mean square error
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