Fit Results
Linear Fit - INVERSA_DE_1_D
Data Source Plot = INVERSA_DE_1_D
Equation Y = 0.4476871 * X + 7.5627898
Number of data points used = 19
Average X = 18.052632
Average Y = 15.644719
Residual sum of squares = 1649.6605
Regression sum of squares = 449.94067
Coefficient of determination, R-sq'd = 0.2142982
Correlation coefficient, R = 0.4629235
Residual mean square, sigma-hat-sq'd = 97.038852
P-Value = 0.0459432
Standard error of intercept (A) = +/-4.3811383
Standard error of slope (B) = +/-0.2079073
Exponential Fit - INVERSA_DE_1_D
Data Source Plot = INVERSA_DE_1_D
Equation ln(Y) = 0.0389324 * X + 1.7191931
Alternate Y = exp(0.0389324 * X) * 5.5800240
Number of data points used = 19
Average X = 18.052632
Average ln(Y) = 2.4220245
Residual sum of squares = 2310.4432
Regression sum of squares = 2358.1442
Coefficient of determination, R-sq'd = 0.5051087
Correlation coefficient, R = 0.7107100
Residual mean square, sigma-hat-sq'd = 135.90842
P-Value = 0.1342118
Standard error of intercept (A) = +/-0.3809039
Standard error of slope (B) = +/-0.0180758
Power Fit - INVERSA_DE_1_D
Data Source Plot = INVERSA_DE_1_D
Equation ln(Y) = 0.5378599 * ln(X) + 1.0385330
Alternate Y = pow(X,0.5378599) * 2.8250695
Number of data points used = 19
Average ln(X) = 2.5722155
Average ln(Y) = 2.4220245
Residual sum of squares = 108.25138
Regression sum of squares = 117.95449
Coefficient of determination, R-sq'd = 0.5214475
Correlation coefficient, R = 0.7221132
Residual mean square, sigma-hat-sq'd = 6.3677285
P-Value = 0.0154227
Polynomial Fit - INVERSA_DE_1_D
Data Source Plot = INVERSA_DE_1_D
Equation Y = -11.248161 + 15.512023 * X - 4.1411990 * pow(X,2) + 0.4707449 *
pow(X,3) - 0.0248023 * pow(X,4) + 0.0006082 * pow(X,5) - 5.6316201e-06 *
pow(X,6)
Degree = 6
Number of data points used = 19
Average X = 18.052632
Average Y = 15.644719
Coefficients:
Degree 0 = -11.248161
Degree 1 = 15.512023
Degree 2 = -4.1411990
Degree 3 = 0.4707449
Degree 4 = -0.0248023
Degree 5 = 0.0006082
Degree 6 = -5.6316201e-06
Degree: 0
Residual sum of squares = 2099.6011
Coefficient of determination, R-sq'd = 0
Correlation coefficient, R = 0
Degree: 1
Residual sum of squares = 1649.6605
Coefficient of determination, R-sq'd = 0.2142982
Correlation coefficient, R = 0.4629235
Degree: 2
Residual sum of squares = 1178.1990
Coefficient of determination, R-sq'd = 0.4388463
Correlation coefficient, R = 0.6624548
Degree: 3
Residual sum of squares = 1073.8067
Coefficient of determination, R-sq'd = 0.4885663
Correlation coefficient, R = 0.6989752
Degree: 4
Residual sum of squares = 1073.7439
Coefficient of determination, R-sq'd = 0.4885963
Correlation coefficient, R = 0.6989966
Degree: 5
Residual sum of squares = 1039.6203
Coefficient of determination, R-sq'd = 0.5048487
Correlation coefficient, R = 0.7105271
Degree: 6
Residual sum of squares = 780.61342
Coefficient of determination, R-sq'd = 0.6282087
Correlation coefficient, R = 0.7925962
P-Value = Less than 0.0001
Through origin Fit - INVERSA_DE_1_D
Data Source Plot = INVERSA_DE_1_D
Equation Y = 0.7551467 * X
Number of data points used = 19
Average X = 18.052632
Average Y = 15.644719
Residual sum of squares = 1938.8189
Coefficient of determination, R-sq'd = 0.7127671
Correlation coefficient, R = 0.8442554
Residual mean square, sigma-hat-sq'd = 107.71216
P-Value = 0.0459432
