User`s guide

E-19
Σx
3
, Σx
2
y, Σx
4
.... 11(S-SUM)ee 1 to 3 (Quadratic
Regression only)
Mean:
o*, p, Population Standard Deviation: σ
x
*, σ
y
, Sample
Standard Deviation: s
x
*, s
y
o, σ
x
, s
x
............12(S-VAR) 1 to 3
p, σ
y
, s
y
............12(S-VAR)e 1 to 3
Regression Coefficients: A, B, Correlation Coefficient:
r
Regression Coefficients for Quadratic Regression: A, B, C
12(S-VAR) ee 1 to 3
Estimated Values:
m, n
Estimated Values for Quadratic Regression:
m
1
, m
2
, n
12(S-VAR) eee 1 to 2 (or 3)
• m, m
1
, m
2
and n are not variables. They are commands of the type
that take an argument immediately before them. See “Calculating
Estimated Values” for more information.
Note: While single-variable statistical calculation is selected, you can
input the functions and commands for performing normal distribution
calculation from the menu that appears when you perform the following
key operation: 13(DISTR). See “Performing Normal Distribution
Calculations” for details.
To calculate the mean (o) and population standard deviation
(σ
x
) for the following data: 55, 54, 51, 55, 53, 53, 54, 52
,,b(SD)
55 7 54 7 51 7 55 7 53 77 54 7 52 7
!c(S-VAR) b( o) =
53.375
!c(S-VAR)c(σ
x
)= 1.316956719
To calculate the linear regression and logarithmic regression
correlation coefficients ( r ) for the following paired-variable
data and determine the regression formula for the strongest
correlation: ( x , y ) = (20, 3150), (110, 7310), (200, 8800), (290,
9310). Specify Fix 3 (three decimal places) for results.
NN2(REG) b(Lin) N b(Fix) d
20 , 3150 7 110 , 7310 7
200 , 8800 7 290 , 9310 7
1c(S-VAR) eed(r) =
0.923
NNc(REG) c(Log)
20 , 3150 7 110 , 7310 7
200 , 8800 7 290 , 9310 7
1c(S-VAR) eed(r) =
0.998
1c(S-VAR) eeb(A) = −3857.984
1c(S-VAR) eec(B) = 2357.532
Logarithmic Regression Formula:
y = –3857.984 + 2357.532lnx
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