Results:
Results:
items ({
x
n
; freq
n
} = {1;1, 2;2, 3;3, 4;2, 5;1}), and calculate the mean and
population standard deviation.
(SETUP)
(STAT)
(ON)
(STAT)
(1-VAR)
1
2
3
4
5
1
2
3
2
(STAT)
(Var)
(
x
)
3
(STAT)
(Var)
(σ
x
)
1.154700538
Mean: 3, Population Standard Deviation: 1.154700538
Example 3:
To calculate the linear regression and logarithmic regression
correlation coefficients 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.
(SETUP)
(STAT)
(OFF)
(SETUP)
(Fix)
(STAT)
(A+BX)
20
110
200
290
3150
7310
8800
9310
(STAT)
(Reg)
(
r
)
0.923
(STAT)
(Type)
(ln X)
(STAT)
(Reg)
(
r
)
0.998
(STAT)
(Reg)
(A)
-3857.984
(STAT)
(Reg)
(B)
2357.532
Linear Regression Correlation Coefficient: 0.923
Logarithmic Regression Correlation Coefficient: 0.998
Logarithmic Regression Formula:
y
= -3857.984 + 2357.532ln
x
Calculating Estimated Values
Based on the regression formula obtained by paired-variable statistical
calculation, the estimated value of
y
can be calculated for a given
x
-value.
The corresponding
x
-value (two values,
x
1
and
x
2
, in the case of quadratic
regression) also can be calculated for a value of
y
in the regression
formula.
Example 4:
To determine the estimate value for
x
when
y
= -130 in the
regression formula produced by logarithmic regression of the data in
48
