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Dep Var: TOTALSDLGS N: 33 Multiple R: 0.728 Squared multiple R: 0.529 Adjusted squared multiple R: 0.372 Standard error of estimate: 2.075 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT -0.245 1.237 0.000 . -0.198 0.844
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Dep Var: TOTALSDLGS N: 33 Multiple R: 0.728 Squared multiple R: 0.529 Adjusted squared multiple R: 0.372 Standard error of estimate: 2.075 Effect Coefficient Std Error Std Coef Tolerance t P(2 Tail) CONSTANT -0.245 1.237 0.000 . -0.198 0.844 ACRU 2.876 1.843 0.285 0.589 1.560 0.132 ACSA -2.323 5.623 -0.089 0.422 -0.413 0.683 FAGR 62.668 26.510 0.451 0.538 2.364 0.027 PIST 4.215 2.403 0.296 0.691 1.754 0.092 PRSE 1.627 2.069 0.140 0.620 0.786 0.439 QURU 21.159 5.471 0.579 0.875 3.867 0.001 TSCA -13.789 9.250 -0.226 0.857 -1.491 0.149 OTHER 14.832 10.582 0.218 0.809 1.402 0.174 Analysis of Variance Source Sum-of-Squares df Mean-Square F-ratio P Regression 116.183 8 14.523 3.373 0.010 Residual 103.332 24 4.306
Principal Components Analysis Component loadings 1 2 3 4 ACRU -0.708 -0.263 0.114 0.373 ACSA 0.848 -0.209 -0.186 -0.069 FAGR 0.581 0.166 -0.526 0.499 PIST -0.198 0.710 -0.243 -0.537 PRSE 0.228 -0.739 0.075 -0.558 QURU 0.161 0.182 0.681 0.158 TSCA -0.390 -0.339 -0.424 0.245 OTHER 0.470 0.090 0.435 0.264 Variance Explained by Components 1 2 3 4 2.049 1.347 1.221 1.148 Percent of Total Variance Explained 1 2 3 4 25.611 16.844 15.261 14.351
Other Eastern Hemlock White Ash Black Cherry Red Oak White Pine Sugar Maple Beech Red Maple
Maximum Likelihood Estimation -Used a global optimization procedure to find the best parameter estimates for the Type IV Exponential Equation: RESPONSE = A*B(X-C) 2 A B Response C Principal Components Axis
High Sugar Maple High White Ash High Red Oak High Beech