multiple pasos sucesivos

Regression
Descriptive Statistics
Mean
2043,5250
1046,6285
400,88
PARADOS
IMPAGADOS
CONCURSO
Std. Deviation
392,00963
461,09341
297,913
N
16
16
16
Correlations
Pearson Correlation
Sig. (1-tailed)
N
PARADOS
1,000
,897
,971
,
,000
,000
16
16
16
PARADOS
IMPAGADOS
CONCURSO
PARADOS
IMPAGADOS
CONCURSO
PARADOS
IMPAGADOS
CONCURSO
IMPAGADO
S
,897
1,000
,943
,000
,
,000
16
16
16
CONCURSO
,971
,943
1,000
,000
,000
,
16
16
16
Variables Entered/Removeda
Model
1
Variables
Entered
Variables
Removed
CONCURS
O
,
Method
Stepwise
(Criteria:
F-to-enter
>= 3,840,
F-to-remo
ve <=
2,710).
a. Dependent Variable: PARADOS
Model Summaryb
Model
1
R
,971a
R Square
,942
Adjusted R
Square
,938
Std. Error of
the Estimate
97,56472
Model Summaryb
Change Statistics
R Square
Model
Change
F Change
df1
df2
1
,942
228,158
1
14
a. Predictors: (Constant), CONCURSO
b. Dependent Variable: PARADOS
Sig. F Change
,000
Durbin-Wa
tson
1,103
Page 1
ANOVAb
Sum of
Squares
Regression
2171809,0
Residual
133264,24
Total
2305073,3
a. Predictors: (Constant), CONCURSO
b. Dependent Variable: PARADOS
Model
1
df
1
14
15
Mean Square
2171809,047
9518,875
F
228,158
Sig.
,000a
Coefficientsa
Model
1
(Constant)
CONCURSO
Unstandardized
Coefficients
B
Std. Error
1531,508
41,761
1,277
,085
Standardiz
ed
Coefficient
s
Beta
,971
t
36,673
15,105
Sig.
,000
,000
Page 2
Coefficientsa
Model
1
(Constant)
CONCURSO
95% Confidence Interval for B
Lower Bound
Upper Bound
1441,940
1621,076
1,096
1,459
Page 3
Coefficientsa
Model
1
Zero-order
Correlations
Partial
(Constant)
CONCURSO
,971
a. Dependent Variable: PARADOS
Part
,971
Collinearity Statistics
Tolerance
VIF
,971
1,000
1,000
Excluded Variablesb
Model
1
Beta In
IMPAGADO
S
t
a
-,173
-,886
Sig.
,392
Partial
Correlation
-,239
Page 4
Excluded Variablesb
Model
1
Collinearity Statistics
Minimum
Tolerance
VIF
Tolerance
IMPAGADO
,110
9,102
S
a. Predictors in the Model: (Constant), CONCURSO
b. Dependent Variable: PARADOS
,110
Coefficient Correlationsa
Model
1
Correlations
CONCURSO
Covariances
CONCURSO
a. Dependent Variable: PARADOS
CONCURSO
1,000
7,150E-03
Collinearity Diagnosticsa
Model
1
Dimension
Eigenvalue
1
1,812
2
,188
a. Dependent Variable: PARADOS
Condition
Index
1,000
3,102
Variance Proportions
(Constant)
CONCURSO
,09
,09
,91
,91
Casewise Diagnosticsa
Case Number
PERIODO
Std. Residual
1
2005TI
3,015
a. Dependent Variable: PARADOS
PARADOS
2099,00
Predicted
Value
1804,8393
Residual
294,1607
Residuals Statisticsa
Minimum
Predicted Value
1766,5219
Residual
-107,4245
Std. Predicted Value
-,728
Std. Residual
-1,101
a. Dependent Variable: PARADOS
Maximum
3159,9993
294,1607
2,934
3,015
Mean
2043,5250
,0000
,000
,000
Std. Deviation
380,50922
94,25647
1,000
,966
N
16
16
16
16
Charts
Page 5
Histogram
Dependent Variable: PARADOS
6
5
4
3
Std. Dev = ,97
1
Mean = 0,00
N = 16,00
0
-1,00 -,50 0,00
,50
1,00 1,50 2,00 2,50 3,00
Regression Standardized Residual
Normal P-P Plot of Regression Standardized Residual
Dependent Variable: PARADOS
1,00
2008TI
2005TII
2008TIV
2006TI
,75
Expected Cum Prob
Frequency
2
2006TII
2006TIII
2005TIV
2008TIII
2007TI
,50
2006TIV
2007TIII
2007TIV
2008TII
,25
2005TIII
2007TII
0,00
0,00
,25
,50
,75
1,00
Observed Cum Prob
Page 6
Regression Standardized Predicted Value
Scatterplot
Dependent Variable: PARADOS
3
2
2008TIII
2008TII
1
2008TI
2007TIV
0
2006TI
2005TII
2007TI
2007TII
2005TIII
2006TIV
2005TIV
2007TIII
2006TII
2006TIII
-1
-2
-1
0
2005TI
1
2
3
4
Regression Standardized Residual
Page 7