1N(SETUP)c3(STAT)1(ON) N2(STAT)1(1-VAR)

1 =2 =3 =4 =5 =ce 1 =2 =3 =2 =

STAT

A11(STAT)4(Var)2(o)=

A11(STAT)4(Var)3(σx)=

Results: Mean: 3 Population Standard Deviation: 1.154700538

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.

1N(SETUP)c3(STAT)2(OFF)

1N(SETUP)6(Fix)3

N2(STAT)2(A + BX)

STAT

FIX

 

 

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 + 2357.532lnx

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, x1 and x2, in the case of quadratic regression) also can be calculated for a value of y in the regression formula.

4 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.)

A160 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.

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