dep var: totalsdlgs n: 33 multiple r: 0.728 squared multiple r: 0.529
DESCRIPTION
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 EffectCoefficientStd ErrorStd CoefTolerancetP(2 Tail) CONSTANT-0.2451.2370.000.-0.1980.844 - PowerPoint PPT PresentationTRANSCRIPT
Total Sdlgs ACRU ACSA FAGR FRAM PIST PRSE QURU TSCA OTHER3 0.91327 0 0 0 0.09786 0 0 0 03 0.76206 0 0 0 0.09786 0 0.01606 0 01 0.01832 0.34419 0 0 0 0.01629 0.09062 0 0.022274 0.16188 0 0 0 0.39214 0 0 0 01 0.47193 0 0 0 0.18548 0 0 0 0.026590 0.75966 0 0.02994 0 0.06845 0 0 0 0.026594 0.49819 0 0.04087 0 0.05432 0 0 0.09079 00 0.56881 0 0.01094 0 0 0 0 0.09079 00 0.56881 0 0 0 0 0 0 0.09079 06 0.60207 0 0 0 0 0.38849 0 0 0.010390 0.41845 0 0 0 0 0.14253 0 0 00 0.53881 0 0 0 0 0 0 0 06 0.35334 0.06026 0.01227 0 0 0 0 0 00 0.09953 0.06026 0.01227 0 0 0 0 0 0.093480 0.09662 0.31469 0 0 0 1.02136 0 0 00 0.0172 0 0 0 0 0.36382 0 0 05 0.11702 0 0 0 0.88484 0 0 0 00 0.58728 0 0 0 0 0 0 0.09348 01 0 0.01674 0.01188 0 0.09457 0 0 0.08398 0.074020 0.1301 0.01674 0.01188 0 0.37542 0 0 0.08398 0.042510 0.1301 0.01674 0 0 0.37542 0 0 0 0.023561 0.1301 0 0 0 0 0 0 0 0.038964 0.36686 0 0 0 0 0 0 0 0.086574 0.55774 0 0 0 0 0 0 0 0.086570 0.60339 0.01075 0 0 0 0 0 0.08919 00 0.04829 0.01075 0 0 0 0.31768 0 0 0.079423 0.43312 0.08393 0 0 0 0.31768 0 0 0.079422 0.55193 0.07354 0 0 0 0.31768 0 0 0.113458 0 0.28937 0.0721 0 0 0 0 0 0.113452 0 0.28937 0.0721 0 0 0 0 0 00 0.39491 0.04676 0 0 0 0.43267 0 0.11162 02 0.39491 0.04676 0 0 0 0.47098 0 0.11162 0.04791
10 0.2801 0.03048 0 0 0 0 0.40488 0 0.06019
ACRU
AC
RU
ACSA FAGR FRAM PIST PRSE QURU TSCA OTHER TOTALSDLGS
AC
RU
AC
SA
AC
SA
FAG
R
FAG
R
FR
AM
FR
AM
PIS
T
PIS
T
PR
SE
PR
SE
QU
RU
QU
RU
TS
CA
TS
CA
OT
HE
R
OT
HE
R
ACRU
TOTA
LSD
LGS
ACSA FAGR FRAM PIST PRSE QURU TSCA OTHER TOTALSDLGS
TOTA
LSD
LGS
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 0.844ACRU 2.876 1.843 0.285 0.589 1.560 0.132ACSA -2.323 5.623 -0.089 0.422 -0.413 0.683FAGR 62.668 26.510 0.451 0.538 2.364 0.027PIST 4.215 2.403 0.296 0.691 1.754 0.092PRSE 1.627 2.069 0.140 0.620 0.786 0.439QURU 21.159 5.471 0.579 0.875 3.867 0.001TSCA -13.789 9.250 -0.226 0.857 -1.491 0.149OTHER 14.832 10.582 0.218 0.809 1.402 0.174 Analysis of Variance
Source Sum-of-Squares df Mean-Square F-ratio PRegression 116.183 8 14.523 3.373 0.010
Residual 103.332 24 4.306
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
Principal Components Analysis
Scree Plot
0 1 2 3 4 5 6 7 8 9Number of Factors
0.0
0.5
1.0
1.5
2.0
2.5
Eig
enva
lue
Factor Loadings Plot
FACTOR(1)
FAC
TO
R(1
)
FACTOR(2)
PRSE
TSCA
ACRU
ACSA
PIST
QURU
FAGROTHER
FACTOR(3)
FAGR
TSCAPIST
ACSA
QURU
OTHER
ACRU
PRSE
FACTOR(4)
FAC
TO
R(1)
PRSE
PIST
ACSA
QURU
FAGR
ACRU
OTHER
TSCA
FAC
TO
R( 2
)
ACRUTSCA
PIST
QURU
ACSA
FAGROTHER
PRSE
FAGR
TSCA
PIST
ACSA
QURUOTHER
ACRU
PRSE
FAC
TO
R(2)PRSE
PIST
ACSA
QURUFAGR
ACRU
OTHER
TSCA
FAC
TO
R( 3
)
ACRU
TSCAPIST
QURU
ACSA
FAGR
OTHER
PRSE PRSE
TSCA
ACRU
ACSA PIST
QURU
FAGR
OTHER
FAC
TO
R(3)
PRSE
PIST ACSA
QURU
FAGR
ACRU
OTHER
TSCA
FACTOR(1)
FAC
TO
R(4
)
ACRUTSCA
PIST
QURU
ACSA
FAGR
OTHER
PRSE
FACTOR(2)
PRSE
TSCAACRU
ACSA
PIST
QURU
FAGR
OTHER
FACTOR(3)
FAGR
TSCA
PIST
ACSA
QURUOTHER
ACRU
PRSE
FACTOR(4)
FAC
TO
R(4)
Canopy Tree Principal Components Analysis
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
-5 -4 -3 -2 -1 0 1 2 3 4 5
Canopy Tree Neighborhood Axis
Per
cen
t B
asal
Are
a
Red Maple
Sugar Maple
Red Oak
White Ash
Eastern Hemlock
Other
Black Cherry
Beech
White Pine
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
C
A
B
Principal Components Axis
Res
po
nse
Tree Neighborhoods: Seed Survival
0
2
4
6
8
10
12
14
-5 -3 -1 1 3 5
Canopy Tree Neighborhood Axis
Av
era
ge
# S
ee
ds
Re
ma
inin
g (
of
25
) 1995
1996
High Sugar MapleHigh White Ash
High Red OakHigh Beech
