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1N(SETUP) c3(STAT) 1(ON)
N2(STAT) 1(1-VAR)
1 = 2 = 3 = 4 = 5 =ce
1 = 2 = 3 = 2 =
A11(STAT) 4(Var) 2( o) =
A11(STAT) 4(Var) 3( σ
x ) =
Results: Mean: 3 Population Standard Deviation: 1.154700538
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.
1N(SETUP) c3(STAT) 2(OFF)
1N(SETUP) 6(Fix) 3
N2(STAT) 2(A + BX)
20 = 110 = 200 = 290 =ce
3150 = 7310 =8800 = 9310 =
A11(STAT) 5(Reg) 3(r) =
A11(STAT) 1(Type) 4(In X)
A11(STAT) 5(Reg) 3(r) =
A11(STAT) 5(Reg) 1(A) =
A11(STAT) 5(Reg) 2(B) =
Results: 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.
To determine the estimate value for y when x = 160 in the
regression formula produced by logarithmic regression of the data
in
3
. Specify Fix 3 for the result. (Perform the following operation
after completing the operations in
3
.)
A 160 11(STAT) 5(Reg) 5( n) =
Result: 8106.898
Important: Regression coefficient, correlation coefficient, and estimated
value calculations can take considerable time when there are a large number
of data items.
STAT
3
STAT
FIX
4