SPSS OUTPUT
Logistic Regression: Model 1
Total number of cases: 196 (Unweighted)
Number of selected cases: 196
Number of unselected cases: 0
Number of selected cases: 196
Number rejected because of missing data: 0
Number of cases included in the analysis: 196
Dependent Variable Encoding:
Original Internal
Value Value
0 0
1 1
Dependent Variable.. YES Would have taken trips in 1991 if cost of all
trips is $[cost] more?
Beginning Block Number 0. Initial Log Likelihood Function
-2 Log Likelihood 270.0583
* Constant is included in the model.
Beginning Block Number 1. Method: Enter
Variable(s) Entered on Step Number
1.. COST Increase in the total cost of taking trips
Estimation terminated at iteration number 3 because
parameter estimates changed by less than .001
-2 Log Likelihood 255.570
Goodness of Fit 196.083
Cox & Snell - R^2 .071
Nagelkerke - R^2 .095
Chi-Square df Significance
Model 14.488 1 .0001
Block 14.488 1 .0001
Step 14.488 1 .0001
Classification Table for YES
The Cut Value is .50
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 26 I 63 I 29.21%
+-------+-------+
Yes Y I 14 I 93 I 86.92%
+-------+-------+
Overall 60.71%
---------------------- Variables in the Equation -----------------------
Variable B S.E. Wald df Sig R Exp(B)
COST -.0019 .0005 13.4326 1 .0002 -.2058 .9981
Constant .9767 .2619 13.9103 1 .0002
Logistic Regression: Model 2
Total number of cases: 196 (Unweighted)
Number of selected cases: 196
Number of unselected cases: 0
Number of selected cases: 196
Number rejected because of missing data: 0
Number of cases included in the analysis: 196
Dependent Variable Encoding:
Original Internal
Value Value
0 0
1 1
Dependent Variable.. YES Would have taken trips in 1991 if cost of all
trips is $[cost] more?
Beginning Block Number 0. Initial Log Likelihood Function
-2 Log Likelihood 270.0583
* Constant is included in the model.
Beginning Block Number 1. Method: Enter
Variable(s) Entered on Step Number
1.. COST Increase in the total cost of taking trips
Estimation terminated at iteration number 3 because
parameter estimates changed by less than .001
-2 Log Likelihood 255.570
Goodness of Fit 196.083
Cox & Snell - R^2 .071
Nagelkerke - R^2 .095
Chi-Square df Significance
Model 14.488 1 .0001
Block 14.488 1 .0001
Step 14.488 1 .0001
Classification Table for YES
The Cut Value is .50
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 26 I 63 I 29.21%
+-------+-------+
Yes Y I 14 I 93 I 86.92%
+-------+-------+
Overall 60.71%
---------------------- Variables in the Equation -----------------------
Variable B S.E. Wald df Sig R Exp(B)
COST -.0019 .0005 13.4326 1 .0002 -.2058 .9981
Constant .9767 .2619 13.9103 1 .0002
Beginning Block Number 2. Method: Enter
Variable(s) Entered on Step Number
1.. CATCH About how many bass-trout did you catch in 1991
INCOME annual income (in thousands)
Estimation terminated at iteration number 4 because
parameter estimates changed by less than .001
-2 Log Likelihood 241.691
Goodness of Fit 200.974
Cox & Snell - R^2 .135
Nagelkerke - R^2 .180
Chi-Square df Significance
Model 28.367 3 .0000
Block 13.879 2 .0010
Step 13.879 2 .0010
Classification Table for YES
The Cut Value is .50
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 53 I 36 I 59.55%
+-------+-------+
Yes Y I 28 I 79 I 73.83%
+-------+-------+
Overall 67.35%
---------------------- Variables in the Equation -----------------------
Variable B S.E. Wald df Sig R Exp(B)
COST -.0020 .0005 13.3348 1 .0003 -.2106 .9980
CATCH .0057 .0023 6.0436 1 .0140 .1258 1.0057
INCOME .0166 .0085 3.7664 1 .0523 .0831 1.0167
Constant .1040 .4102 .0643 1 .7999
Logistic Regression: Model 3
Total number of cases: 196 (Unweighted)
Number of selected cases: 196
Number of unselected cases: 0
Number of selected cases: 196
Number rejected because of missing data: 0
Number of cases included in the analysis: 196
Dependent Variable Encoding:
Original Internal
Value Value
0 0
1 1
Dependent Variable.. YES Would have taken trips in 1991 if cost of all
trips is $[cost] more?
Beginning Block Number 0. Initial Log Likelihood Function
-2 Log Likelihood 270.0583
* Constant is included in the model.
Beginning Block Number 1. Method: Enter
Variable(s) Entered on Step Number
1.. COST Increase in the total cost of taking trips
CATCH About how many bass-trout did you catch in 1991
INCOME annual income (in thousands)
Estimation terminated at iteration number 4 because
parameter estimates changed by less than .001
-2 Log Likelihood 241.691
Goodness of Fit 200.974
Cox & Snell - R^2 .135
Nagelkerke - R^2 .180
Chi-Square df Significance
Model 28.367 3 .0000
Block 28.367 3 .0000
Step 28.367 3 .0000
Classification Table for YES
The Cut Value is .50
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 53 I 36 I 59.55%
+-------+-------+
Yes Y I 28 I 79 I 73.83%
+-------+-------+
Overall 67.35%
---------------------- Variables in the Equation -----------------------
Variable B S.E. Wald df Sig R Exp(B)
COST -.0020 .0005 13.3348 1 .0003 -.2049 .9980
CATCH .0057 .0023 6.0436 1 .0140 .1224 1.0057
INCOME .0166 .0085 3.7664 1 .0523 .0809 1.0167
Constant .1040 .4102 .0643 1 .7999
Beginning Block Number 2. Method: Enter
Variable(s) Entered on Step Number
1.. EDUCATIO Years of completed schooling
MARRIED Marital Status
SEX Sex of respondent
AGE Age of Respondent
EMPLOYED Has a job-business
Estimation terminated at iteration number 4 because
Log Likelihood decreased by less than .01 percent.
-2 Log Likelihood 230.301
Goodness of Fit 197.233
Cox & Snell - R^2 .184
Nagelkerke - R^2 .245
Chi-Square df Significance
Model 39.758 8 .0000
Block 11.390 5 .0442
Step 11.390 5 .0442
Classification Table for YES
The Cut Value is .50
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 52 I 37 I 58.43%
+-------+-------+
Yes Y I 25 I 82 I 76.64%
+-------+-------+
Overall 68.37%
---------------------- Variables in the Equation -----------------------
Variable B S.E. Wald df Sig R Exp(B)
COST -.0018 .0006 10.0119 1 .0016 -.1821 .9982
CATCH .0059 .0023 6.6256 1 .0101 .1383 1.0059
INCOME .0153 .0099 2.3922 1 .1219 .0403 1.0155
EDUCATIO -.0760 .0620 1.5017 1 .2204 .0000 .9268
MARRIED .3666 .4012 .8349 1 .3609 .0000 1.4428
SEX .9826 .4753 4.2743 1 .0387 .0970 2.6715
AGE -.0045 .0156 .0817 1 .7750 .0000 .9956
EMPLOYED 1.3769 .5973 5.3145 1 .0211 .1171 3.9625
Constant -.4473 1.1585 .1491 1 .6994
Logistic Regression: Model 3 with C.I. for Odds Ratio
Total number of cases: 196 (Unweighted)
Number of selected cases: 196
Number of unselected cases: 0
Number of selected cases: 196
Number rejected because of missing data: 0
Number of cases included in the analysis: 196
Dependent Variable Encoding:
Original Internal
Value Value
0 0
1 1
Dependent Variable.. YES Would have taken trips in 1991 if cost of all
trips is $[cost] more?
Beginning Block Number 0. Initial Log Likelihood Function
-2 Log Likelihood 270.0583
* Constant is included in the model.
Beginning Block Number 1. Method: Enter
Variable(s) Entered on Step Number
1.. COST Increase in the total cost of taking trips
CATCH About how many bass-trout did you catch in 1991
INCOME annual income (in thousands)
EMPLOYED Has a job-business
EDUCATIO Years of completed schooling
MARRIED Marital Status
SEX Sex of respondent
AGE Age of Respondent
Estimation terminated at iteration number 4 because
Log Likelihood decreased by less than .01 percent.
-2 Log Likelihood 230.301
Goodness of Fit 197.233
Cox & Snell - R^2 .184
Nagelkerke - R^2 .245
Chi-Square df Significance
Model 39.758 8 .0000
Block 39.758 8 .0000
Step 39.758 8 .0000
Classification Table for YES
The Cut Value is .50
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 52 I 37 I 58.43%
+-------+-------+
Yes Y I 25 I 82 I 76.64%
+-------+-------+
Overall 68.37%
----------------- Variables in the Equation ------------------
Variable B S.E. Wald df Sig R
COST -.0018 .0006 10.0119 1 .0016 -.1722
CATCH .0059 .0023 6.6256 1 .0101 .1309
INCOME .0153 .0099 2.3922 1 .1219 .0381
EMPLOYED 1.3769 .5973 5.3145 1 .0211 .1108
EDUCATIO -.0760 .0620 1.5017 1 .2204 .0000
MARRIED .3666 .4012 .8349 1 .3609 .0000
SEX .9826 .4753 4.2743 1 .0387 .0918
AGE -.0045 .0156 .0817 1 .7750 .0000
Constant -.4473 1.1585 .1491 1 .6994
95% CI for Exp(B)
Variable Exp(B) Lower Upper
COST .9982 .9971 .9993
CATCH 1.0059 1.0014 1.0104
INCOME 1.0155 .9959 1.0354
EMPLOYED 3.9625 1.2291 12.7751
EDUCATIO .9268 .8207 1.0466
MARRIED 1.4428 .6572 3.1675
SEX 2.6715 1.0524 6.7816
AGE .9956 .9656 1.0264
Logistic Regression: Model 3 (NC=1)
Total number of cases: 196 (Unweighted)
Number of selected cases: 108
Number of unselected cases: 88
Number of selected cases: 108
Number rejected because of missing data: 0
Number of cases included in the analysis: 108
Dependent Variable Encoding:
Original Internal
Value Value
0 0
1 1
Dependent Variable.. YES Would have taken trips in 1991 if cost of all
trips is $[cost] more?
Beginning Block Number 0. Initial Log Likelihood Function
-2 Log Likelihood 149.71979
* Constant is included in the model.
Beginning Block Number 1. Method: Enter
Variable(s) Entered on Step Number
1.. COST Increase in the total cost of taking trips
CATCH About how many bass-trout did you catch in 1991
INCOME annual income (in thousands)
EMPLOYED Has a job-business
EDUCATIO Years of completed schooling
MARRIED Marital Status
SEX Sex of respondent
AGE Age of Respondent
Estimation terminated at iteration number 4 because
Log Likelihood decreased by less than .01 percent.
-2 Log Likelihood 122.848
Goodness of Fit 106.283
Cox & Snell - R^2 .220
Nagelkerke - R^2 .294
Chi-Square df Significance
Model 26.872 8 .0007
Block 26.872 8 .0007
Step 26.872 8 .0007
Classification Table for YES
The Cut Value is .50
Selected cases NC EQ 1
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 36 I 18 I 66.67%
+-------+-------+
Yes Y I 17 I 37 I 68.52%
+-------+-------+
Overall 67.59%
Classification Table for YES
The Cut Value is .50
Unselected cases NC NE 1
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 21 I 14 I 60.00%
+-------+-------+
Yes Y I 20 I 33 I 62.26%
+-------+-------+
Overall 61.36%
----------------- Variables in the Equation ------------------
Variable B S.E. Wald df Sig R
COST -.0021 .0008 6.4549 1 .0111 -.1725
CATCH .0051 .0027 3.5632 1 .0591 .1022
INCOME .0154 .0132 1.3629 1 .2430 .0000
EMPLOYED 1.9155 .8994 4.5357 1 .0332 .1301
EDUCATIO -.0873 .0873 .9991 1 .3175 .0000
MARRIED .2419 .5380 .2023 1 .6529 .0000
SEX 1.0829 .6624 2.6730 1 .1021 .0670
AGE .0195 .0221 .7774 1 .3779 .0000
Constant -1.6041 1.5765 1.0353 1 .3089
95% CI for Exp(B)
Variable Exp(B) Lower Upper
COST .9979 .9963 .9995
CATCH 1.0051 .9998 1.0104
INCOME 1.0156 .9896 1.0423
EMPLOYED 6.7903 1.1650 39.5787
EDUCATIO .9164 .7722 1.0875
MARRIED 1.2737 .4438 3.6560
SEX 2.9532 .8063 10.8168
AGE 1.0196 .9765 1.0647
Logistic Regression: Model 3 (NC=0)
Total number of cases: 196 (Unweighted)
Number of selected cases: 88
Number of unselected cases: 108
Number of selected cases: 88
Number rejected because of missing data: 0
Number of cases included in the analysis: 88
Dependent Variable Encoding:
Original Internal
Value Value
0 0
1 1
Dependent Variable.. YES Would have taken trips in 1991 if cost of all
trips is $[cost] more?
Beginning Block Number 0. Initial Log Likelihood Function
-2 Log Likelihood 118.28597
* Constant is included in the model.
Beginning Block Number 1. Method: Enter
Variable(s) Entered on Step Number
1.. COST Increase in the total cost of taking trips
CATCH About how many bass-trout did you catch in 1991
INCOME annual income (in thousands)
EMPLOYED Has a job-business
EDUCATIO Years of completed schooling
MARRIED Marital Status
SEX Sex of respondent
AGE Age of Respondent
Estimation terminated at iteration number 4 because
Log Likelihood decreased by less than .01 percent.
-2 Log Likelihood 101.644
Goodness of Fit 87.096
Cox & Snell - R^2 .172
Nagelkerke - R^2 .233
Chi-Square df Significance
Model 16.641 8 .0341
Block 16.641 8 .0341
Step 16.641 8 .0341
Classification Table for YES
The Cut Value is .50
Selected cases NC EQ 0
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 17 I 18 I 48.57%
+-------+-------+
Yes Y I 8 I 45 I 84.91%
+-------+-------+
Overall 70.45%
Classification Table for YES
The Cut Value is .50
Unselected cases NC NE 0
Predicted
No Yes Percent Correct
N I Y
Observed +-------+-------+
No N I 24 I 30 I 44.44%
+-------+-------+
Yes Y I 5 I 49 I 90.74%
+-------+-------+
Overall 67.59%
----------------- Variables in the Equation ------------------
Variable B S.E. Wald df Sig R
COST -.0018 .0009 4.3435 1 .0372 -.1408
CATCH .0076 .0043 3.1321 1 .0768 .0978
INCOME .0062 .0165 .1432 1 .7051 .0000
EMPLOYED .9334 .8417 1.2297 1 .2675 .0000
EDUCATIO -.0651 .0958 .4610 1 .4972 .0000
MARRIED .4865 .6659 .5338 1 .4650 .0000
SEX .7256 .7191 1.0182 1 .3130 .0000
AGE -.0348 .0247 1.9863 1 .1587 .0000
Constant 1.4162 1.9127 .5482 1 .4591
95% CI for Exp(B)
Variable Exp(B) Lower Upper
COST .9982 .9965 .9999
CATCH 1.0076 .9992 1.0160
INCOME 1.0062 .9743 1.0392
EMPLOYED 2.5431 .4885 13.2385
EDUCATIO .9370 .7766 1.1306
MARRIED 1.6267 .4410 5.9997
SEX 2.0660 .5047 8.4577
AGE .9658 .9202 1.0137