Keyboard access:
[STAT]||Single-Var||

Example:

To DAT: [[1 5][3 3] [ 7 2] [9 6] [3 8] [2 6]] TYPE: Sample MEAN: 4.1666666667... STD DEV: 3.125666... VARIANCE: 9.766666... TOTAL: 25 MAXIMUM: 9 MINIMUM: 1 TYPE: Population MEAN: 4.1666666667... STD DEV: 2.8528737... VARIANCE: 8.138888... TOTAL: 25 MAXIMUM: 9 MINIMUM: 1

2) Summary Stats

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Keyboard access:
[STAT]||Summary stats||

Example:

To DAT: [[1 5][3 3] [ 7 2] [9 6] [3 8] [2 6]]   X:25 Y:30 X2:153 Y2:134: XY:118 N:6

3) Fit Data

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Keyboard access:
[STAT]||Fit data||

Example:

To DAT: [[1 5][3 3] [ 7 2] [9 6] [3 8] [2 6]]   Logarithm Fit '5.664034+-56646111*LN(X)' correlation:-.208890217097 covariance:-.36974928012 Exponential Fit '5.40143100114*EXP(-4.223223061E-2)' correlation:-.255960001612 covariance:-.412468119051   Linear Fit '5.59726962457+-.143344709898*X' correlation:-.204472183243 covariance:-1.4 Power Fit '5.63213404451*X^-.185792485677' correlation:-.291106339392 covariance:-.12127335139   Best Fit '5.63213404451*X^-.185792485677' correlation:-.291106339392 covariance:-.12127335139

Best Fit is the Fit with correlation, in absolute value, closest to 1.

4) PRED

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You can know the value of X for a given value of Y
or vice-versa using PRED into FIT DATA function.
Choose the type of FIT and press PRED. in menu, fill out the fields for
X or Y and press PRED and the calculator will give you the result.

To DAT: [[1 5][3 3] [ 7 2] [9 6] [3 8] [2 6]]
we have:

X: 25.0952380951      Y:2

X: 5.5      Y:4.80887372013


Where the red numbers are the ones we give to the calculator to predict the value
and the bold black numbers the result.
Press EDIT, in menu to see the number in full standard format (12 digits).