ANOVA of Dissimilarities

VassarStats: Multiple Regression
Number of variables = 5 X and 1 Y
Observations per variable =70

Computation time: 0.14 seconds


Correlation Matrix2

X1
X2
X3
X4
X5
Y
X1
1
0.253
0.182
0.324
0.317
0.078
X2
0.253
1
0.155
0.237
0.267
0.193
X3
0.182
0.155
1
0.21
0.098
0.499
X4
0.324
0.237
0.21
1
0.305
0.32
X5
0.317
0.267
0.098
0.305
1
0.133
Y
0.078
0.193
0.499
0.32
0.133
1


Regression Coefficients:
The multiple regression equation is of the general form
Y = a + b1X1 + b2X2 + ··· + bkXk
where a is a starting-point constant analogous to the intercept2
in a simple two-variable regression, and b1, b2, etc., are the
unstandardized regression weights for X1, X2, etc., each analogous
to the slope in a simple two-variable regression. In the present2
analysis, a = 1.5189 and the values of b are as indicated below.
The values listed as B are the standardized regression weights.2

b
B
B x rxy
X1
-0.1424
-0.1105
-0.0087
X2
0.1108
0.0882
0.017
X3
0.5545
0.4541
0.2266
X4
0.2777
0.23
0.0735
X5
0.0392
0.0301
0.004
Multiple R2 = 0.3125
Adjusted Multiple R2 = 0.2588
Standard Error of
Multiple Estimate
2.492


ANOVA Table2

Source
SS
df
MS
F
P
Regression
194.7777
5
38.9555
5.82
0.0002
Residual
428.4937
64
6.6952
Total
623.2714
69

Names of Variables:

X1.
Escudero-Villar
X2.
Escudero-Legarda
X3.
Escudero-Lacson
X4.
Escudero-De Castro
X5.
Escudero-Roxas
Y.
Escudero-Estrada

VassarStats: Multiple Regression
Number of variables = 5 X and 1 Y
Observations per variable =70

Computation time: 0.16 seconds


Correlation Matrix2

X1
X2
X3
X4
X5
Y
X1
1
0.389
0.59
0.316
0.304
0.178
X2
0.389
1
0.278
0.19
0.305
0.17
X3
0.59
0.278
1
0.353
0.221
0.043
X4
0.316
0.19
0.353
1
0.106
0.302
X5
0.304
0.305
0.221
0.106
1
-0.119
Y
0.178
0.17
0.043
0.302
-0.119
1


Regression Coefficients:
The multiple regression equation is of the general form
Y = a + b1X1 + b2X2 + ··· + bkXk
where a is a starting-point constant analogous to the intercept2
in a simple two-variable regression, and b1, b2, etc., are the
unstandardized regression weights for X1, X2, etc., each analogous
to the slope in a simple two-variable regression. In the present2
analysis, a = 4.4987 and the values of b are as indicated below.
The values listed as B are the standardized regression weights.2

b
B
B x rxy
X1
0.2295
0.1902
0.0338
X2
0.151
0.1528
0.0259
X3
-0.1628
-0.1679
-0.0071
X4
0.3082
0.295
0.089
X5
-0.1926
-0.218
0.026
Multiple R2 = 0.1676
Adjusted Multiple R2 = 0.1026
Standard Error of
Multiple Estimate
2.1277


ANOVA Table2

Source
SS
df
MS
F
P
Regression
62.9052
5
12.581
2.58
0.0345
Residual
312.3662
64
4.8807
Total
375.2714
69

Names of Variables:

X1.
Villar-Legarda
X2.
Villar-Lacson
X3.
Villar-De Castro
X4.
Villar-Roxas
X5.
Villar-Estrada
Y.
Villar-Escudero

VassarStats: Multiple Regression
Number of variables = 5 X and 1 Y
Observations per variable =70

Computation time: 0.16 seconds


Correlation Matrix2

X1
X2
X3
X4
X5
Y
X1
1
0.213
0.428
0.306
0.108
0.451
X2
0.213
1
0.581
0.076
0.548
0.121
X3
0.428
0.581
1
0.011
0.314
0.437
X4
0.306
0.076
0.011
1
0.127
-0.049
X5
0.108
0.548
0.314
0.127
1
0.079
Y
0.451
0.121
0.437
-0.049
0.079
1


Regression Coefficients:
The multiple regression equation is of the general form
Y = a + b1X1 + b2X2 + ··· + bkXk
where a is a starting-point constant analogous to the intercept2
in a simple two-variable regression, and b1, b2, etc., are the
unstandardized regression weights for X1, X2, etc., each analogous
to the slope in a simple two-variable regression. In the present2
analysis, a = 3.0409 and the values of b are as indicated below.
The values listed as B are the standardized regression weights.2

b
B
B x rxy
X1
0.3217
0.3752
0.1692
X2
-0.1577
-0.1877
-0.0227
X3
0.3615
0.3735
0.1632
X4
-0.1368
-0.1593
0.0078
X5
0.0541
0.0437
0.0034
Multiple R2 = 0.3209
Adjusted Multiple R2 = 0.2679
Standard Error of
Multiple Estimate
1.9717


ANOVA Table2

Source
SS
df
MS
F
P
Regression
126.7542
5
25.3508
6.05
0.0001
Residual
268.2315
64
4.1911
Total
394.9857
69

Names of Variables:

X1.
Legarda-Lacson
X2.
Legarda-De Castro
X3.
Legarda-Roxas
X4.
Legarda-Estrada
X5.
Legarda-Villar
Y.
Legarda-Escudero
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