page 51 Table 3.2 Crossclassification of age dichotomized at 55 years and chd for 100 subjects
get file='chdage.sav'. recode age (55 thru highest=1) (else=0) into aged. execute. CROSSTABS /TABLES=chd BY aged /FORMAT= AVALUE TABLES /CELLS= COUNT.

Cases  

Valid  Missing  Total  
N  Percent  N  Percent  N  Percent  
CHD * AGED  100  100.0%  0  .0%  100  100.0% 

AGED  Total  

.00  1.00  
CHD  .00  51  6  57 
1.00  22  21  43  
Total  73  27  100 
page 52 Table 3.3 Results of fitting the logistic regression model to the data in Table 3.2.
LOGISTIC REGRESSION VAR=chd /METHOD=ENTER aged.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  100  100.0 
Missing Cases  0  .0  
Total  100  100.0  
Unselected Cases  0  .0  
Total  100  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

CHD  Percentage Correct  
Observed  .00  1.00  
Step 0  CHD  .00  57  0  100.0 
1.00  43  0  .0  
Overall Percentage  

57.0  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .282  .202  1.947  1  .163  .754 

Score  df  Sig.  

Step 0  Variables  AGED  18.252  1  .000 
Overall Statistics  18.252  1  .000 

Chisquare  df  Sig.  

Step 1  Step  18.704  1  .000 
Block  18.704  1  .000  
Model  18.704  1  .000 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  117.959  .171  .229 

Predicted  

CHD  Percentage Correct  
Observed  .00  1.00  
Step 1  CHD  .00  51  6  89.5 
1.00  22  21  48.8  
Overall Percentage  

72.0  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  AGED  2.094  .529  15.690  1  .000  8.114 
Constant  .841  .255  10.865  1  .001  .431  
a Variable(s) entered on step 1: AGED. 
page 56 Table 3.5 Crossclassification of hypothetical data on race and chd status for 100 subjects.
data list list / race chd cnt. begin data. 1 1 5 2 1 20 3 1 15 4 1 10 1 0 20 2 0 10 3 0 10 4 0 10 end data. execute . weight by cnt. CROSSTABS /TABLES=chd BY race /FORMAT= AVALUE TABLES /CELLS= COUNT .

Cases  

Valid  Missing  Total  
N  Percent  N  Percent  N  Percent  
CHD * RACE  100  100.0%  0  .0%  100  100.0% 

RACE  Total  

1.00  2.00  3.00  4.00  
CHD  .00  20  10  10  10  50 
1.00  5  20  15  10  50  
Total  25  30  25  20  100 
LOGISTIC REGRESSION VAR=chd /METHOD=ENTER race /CONTRAST (race)=Indicator(1) /PRINT=SUMMARY CI(95).
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  8  100.0 
Missing Cases  0  .0  
Total  8  100.0  
Unselected Cases  0  .0  
Total  8  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Frequency  Parameter coding  

(1)  (2)  (3)  
RACE  1.00  2  .000  .000  .000 
2.00  2  1.000  .000  .000  
3.00  2  .000  1.000  .000  
4.00  2  .000  .000  1.000 

Predicted  

CHD  Percentage Correct  
Observed  .00  1.00  
Step 0  CHD  .00  0  50  .0 
1.00  0  50  100.0  
Overall Percentage  

50.0  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .000  .200  .000  1  1.000  1.000 

Score  df  Sig.  

Step 0  Variables  RACE  13.333  3  .004 
RACE(1)  4.762  1  .029  
RACE(2)  1.333  1  .248  
RACE(3)  .000  1  1.000  
Overall Statistics  13.333  3  .004 

Chisquare  df  Sig.  

Step 1  Step  14.042  3  .003 
Block  14.042  3  .003  
Model  14.042  3  .003 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  124.587  .131  .175 

Predicted  

CHD  Percentage Correct  
Observed  .00  1.00  
Step 1  CHD  .00  20  30  40.0 
1.00  5  45  90.0  
Overall Percentage  

65.0  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  95.0% C.I.for EXP(B)  

Lower  Upper  
Step 1(a)  RACE  

11.771  3  .008  


RACE(1)  2.079  .632  10.810  1  .001  8.000  2.316  27.633  
RACE(2)  1.792  .645  7.705  1  .006  6.000  1.693  21.261  
RACE(3)  1.386  .671  4.271  1  .039  4.000  1.074  14.895  
Constant  1.386  .500  7.687  1  .006  .250  


a Variable(s) entered on step 1: RACE. 
NOTE: The above code also gives the coding scheme shown in Table 3.6.
page 58 Table 3.7 Results of fitting the logistic regression model to the data in Table 3.5 using the design variables in Table 3.6.
NOTE: The above code also gives the output shown in Table 3.7.
page 59 Table 3.8 Specification of the design variables for race using deviation from means coding.
LOGISTIC REGRESSION VAR=chd /METHOD=ENTER race /CONTRAST (race)=Deviation(1) /PRINT=SUMMARY CI(95).
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  8  100.0 
Missing Cases  0  .0  
Total  8  100.0  
Unselected Cases  0  .0  
Total  8  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Frequency  Parameter coding  

(1)  (2)  (3)  
RACE  1.00  2  1.000  1.000  1.000 
2.00  2  1.000  .000  .000  
3.00  2  .000  1.000  .000  
4.00  2  .000  .000  1.000 

Predicted  

CHD  Percentage Correct  
Observed  .00  1.00  
Step 0  CHD  .00  0  50  .0 
1.00  0  50  100.0  
Overall Percentage  

50.0  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .000  .200  .000  1  1.000  1.000 

Score  df  Sig.  

Step 0  Variables  RACE  13.333  3  .004 
RACE(1)  11.416  1  .001  
RACE(2)  8.000  1  .005  
RACE(3)  5.028  1  .025  
Overall Statistics  13.333  3  .004 

Chisquare  df  Sig.  

Step 1  Step  14.042  3  .003 
Block  14.042  3  .003  
Model  14.042  3  .003 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  124.587  .131  .175 

Predicted  

CHD  Percentage Correct  
Observed  .00  1.00  
Step 1  CHD  .00  20  30  40.0 
1.00  5  45  90.0  
Overall Percentage  

65.0  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  95.0% C.I.for EXP(B)  

Lower  Upper  
Step 1(a)  RACE  

11.771  3  .008  


RACE(1)  .765  .351  4.762  1  .029  2.149  1.081  4.273  
RACE(2)  .477  .362  1.736  1  .188  1.612  .792  3.279  
RACE(3)  .072  .385  .035  1  .852  1.075  .506  2.284  
Constant  .072  .219  .108  1  .743  .931  


a Variable(s) entered on step 1: RACE. 
page 60 Table 3.9 Results of fitting the logistic regression model to the data in Table 3.5 using the design variables in Table 3.8.
NOTE: The above code also gives the output shown in Table 3.9.
NOTE: To get the values listed in the column labeled z, you need to take the square root of the Wald statistics given in the SPSS output.
page 67 Table 3.10 Descriptive statistics for two groups of 50 men on age and whether they had seen a physician (PHY) (1 = yes, 0 = no) within the last six months.
NOTE: These data are hypothetical and are not available.
page 69 Table 3.11 Results of fitting the logistic regression model to the data summarized in Table 3.10.
NOTE: These data are hypothetical and are not available.
page 72 Table 3.12 Estimated logistic regression coefficients, deviance, and the likelihood ratio test statistic (G) for an example showing evidence of confounding but no interaction (n = 400).
NOTE: These data are hypothetical and are not available.
page 73 Table 3.13 Estimated logistic regression coefficients, deviance, and the likelihood ratio test statistic (G) for an example showing evidence of confounding and interaction (n = 400).
NOTE: These data are hypothetical and are not available.
page 77 Table 3.14 Estimated logistic regression coefficients, deviance, and the likelihood ratio test statistic (G), and the pvalue for the change for models containing lwd and age from the low birth weight data (n = 189).
NOTE: We have run the logistic regression models from the largest to the smallest so that the difference between the larger and the smaller model can be determined. This is the reverse of the presentation in the table in the book.
NOTE: To get the ln[l(beta)], divide the 2 log likelihood given in the output by 2. To obtain the values of G, subtract the value of ln[l(beta)]. from that of the model with one more term in it (for example, 117.34(113.12)=8.44).
Get file='lowbwt.sav'. compute lwd=(lwt<110). compute lwdage=lwd*age. execute. LOGISTIC REGRESSION VAR=low /METHOD=ENTER lwd age lwdage.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  LWD  8.873  1  .003 
AGE  2.674  1  .102  
LWDAGE  9.639  1  .002  
Overall Statistics  13.357  3  .004 

Chisquare  df  Sig.  

Step 1  Step  13.532  3  .004 
Block  13.532  3  .004  
Model  13.532  3  .004 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  221.140  .069  .097 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  124  6  95.4 
1.00  47  12  20.3  
Overall Percentage  

72.0  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  LWD  1.944  1.725  1.270  1  .260  .143 
AGE  .080  .040  4.029  1  .045  .924  
LWDAGE  .132  .076  3.049  1  .081  1.141  
Constant  .774  .910  .724  1  .395  2.169  
a Variable(s) entered on step 1: LWD, AGE, LWDAGE. 
LOGISTIC REGRESSION VAR=low /METHOD=ENTER lwd age.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  LWD  8.873  1  .003 
AGE  2.674  1  .102  
Overall Statistics  10.670  2  .005 

Chisquare  df  Sig.  

Step 1  Step  10.385  2  .006 
Block  10.385  2  .006  
Model  10.385  2  .006 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  224.287  .053  .075 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  115  15  88.5 
1.00  50  9  15.3  
Overall Percentage  

65.6  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  LWD  1.010  .364  7.690  1  .006  2.746 
AGE  .044  .032  1.884  1  .170  .957  
Constant  .027  .762  .001  1  .972  .973  
a Variable(s) entered on step 1: LWD, AGE. 
LOGISTIC REGRESSION VAR=low /METHOD=ENTER lwd.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  LWD  8.873  1  .003 
Overall Statistics  8.873  1  .003 

Chisquare  df  Sig.  

Step 1  Step  8.431  1  .004 
Block  8.431  1  .004  
Model  8.431  1  .004 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  226.241  .044  .061 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  109  21  83.8 
1.00  38  21  35.6  
Overall Percentage  

68.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  LWD  1.054  .362  8.494  1  .004  2.868 
Constant  1.054  .188  31.288  1  .000  .349  
a Variable(s) entered on step 1: LWD. 
NOTE: To get the model with only the intercept, you need to create a variable equal to one and use that as the dependent variable.
compute x = 1. LOGISTIC REGRESSION VAR=low /METHOD=ENTER x /ORIGIN.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  0  130  .0 
1.00  0  59  100.0  
Overall Percentage  

31.2  
a No terms in the model.  
b Initial Loglikelihood Function: 2 Log Likelihood = 262.010  
c The cut value is .500 

Score  df  Sig.  

Step 0  Variables  X  26.672  1  .000 
Overall Statistics  26.672  1  .000 

Chisquare  df  Sig.  

Step 1  Step  27.338  1  .000 
Block  27.338  1  .000  
Model  27.338  1  .000 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  234.672  .135  .180 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  X  .790  .157  25.327  1  .000  .454 
a Variable(s) entered on step 1: X. 
page 78 Figure 3.3 Plot of the estimated logit for women with LWD = 1 and for women with LWD = 0 from Model 3 in Table 3.17.
LOGISTIC REGRESSION VAR=low /METHOD=ENTER lwd age lwdage /SAVE PRED.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  LWD  8.873  1  .003 
AGE  2.674  1  .102  
LWDAGE  9.639  1  .002  
Overall Statistics  13.357  3  .004 

Chisquare  df  Sig.  

Step 1  Step  13.532  3  .004 
Block  13.532  3  .004  
Model  13.532  3  .004 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  221.140  .069  .097 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  124  6  95.4 
1.00  47  12  20.3  
Overall Percentage  

72.0  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  LWD  1.944  1.725  1.270  1  .260  .143 
AGE  .080  .040  4.029  1  .045  .924  
LWDAGE  .132  .076  3.049  1  .081  1.141  
Constant  .774  .910  .724  1  .395  2.169  
a Variable(s) entered on step 1: LWD, AGE, LWDAGE. 
GRAPH /SCATTERPLOT(BIVAR)=age WITH pre_1.
page 78 Table 3.15 Estimated covariance matrix for the estimated parameters in Model 3 of Table 3.14.
NOTE: There are likely typos in this table.
LOGISTIC REGRESSION VAR=low /METHOD=ENTER lwd age lwdage /PRINT=corr.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  LWD  8.873  1  .003 
AGE  2.674  1  .102  
LWDAGE  9.639  1  .002  
Overall Statistics  13.357  3  .004 

Chisquare  df  Sig.  

Step 1  Step  13.532  3  .004 
Block  13.532  3  .004  
Model  13.532  3  .004 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  221.140  .069  .097 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  124  6  95.4 
1.00  47  12  20.3  
Overall Percentage  

72.0  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  LWD  1.944  1.725  1.270  1  .260  .143 
AGE  .080  .040  4.029  1  .045  .924  
LWDAGE  .132  .076  3.049  1  .081  1.141  
Constant  .774  .910  .724  1  .395  2.169  
a Variable(s) entered on step 1: LWD, AGE, LWDAGE. 

Constant  LWD  AGE  LWDAGE  

Step 1  Constant  1.000  .528  .978  .512 
LWD  .528  1.000  .516  .977  
AGE  .978  .516  1.000  .524  
LWDAGE  .512  .977  .524  1.000 
constant/constant: (.910)**2 = .8281
constant/lwd: (.910)*(1.725)*(.528) = .828828
constant/age: (.910)*(.040)*(.978) = .0355992
constant/lwd*age: (.910)*(.076)*(.512) = .03603236
lwd/lwd: (1.725)**2 = 2.975625
lwd/age: (1.725)*(.040)*(.516) = .035604
lwd/lwd*age: (1.725)*(.076)*(.977) = .1280847
age/age: (.040)**2 = .0016
age/lwd*age: (.040)*(.076)*(.524) = .001593
lwd*age/lwd*age: (.076)**2 = .005776
page 79 Table 3.16 Estimated odds ratios and 95% confidence intervals for lwd, controlling for age.
NOTE: We were unable to reproduce this table.
page 80 Table 3.17 Crossclassification of low birth weight by smoking status.
CROSSTABS /TABLES=low BY smoke /FORMAT= AVALUE TABLES /CELLS= COUNT.

Cases  

Valid  Missing  Total  
N  Percent  N  Percent  N  Percent  
< 2500g * SMOKE  189  100.0%  0  .0%  189  100.0% 

SMOKE  Total  

.00  1.00  
< 2500g  .00  86  44  130 
1.00  29  30  59  
Total  115  74  189 
page 81 Table 3.18 Crossclassification of low birth weight by smoking status stratified by race.
CROSSTABS /TABLES=low BY smoke BY race /FORMAT= AVALUE TABLES /CELLS= COUNT.

Cases  

Valid  Missing  Total  
N  Percent  N  Percent  N  Percent  
< 2500g * SMOKE * RACE  189  100.0%  0  .0%  189  100.0% 

SMOKE  Total  

RACE  .00  1.00  
white  < 2500g  .00  40  33  73 
1.00  4  19  23  
Total  44  52  96  
black  < 2500g  .00  11  4  15 
1.00  5  6  11  
Total  16  10  26  
other  < 2500g  .00  35  7  42 
1.00  20  5  25  
Total  55  12  67 
page 82 Table 3.19 Tabulation of the estimated odds ratios, ln(estimated odds ratios), estimated variance of the ln(estimated odds ratios), and the inverse of the estimated variance, w, for smoking status within each stratum of race.
NOTE: The estimated variance of the ln(estimated odds ratios), and the inverse of the estimated variance, w, were not calculated because they were needed only to do a handcomputation.
compute race1=0. recode race1 (0=1) (1,2=2) (3 thru 15=3) (16 thru highest=4). recode race (2=1) (else=0) into race2. recode race (3=1) (else=0) into race3. compute race1sm=race1*smoke. compute race2sm=race2*smoke. compute race3sm=race3*smoke. execute.
NOTE: Values for White:
LOGISTIC REGRESSION VAR=low /METHOD=ENTER race2 race3 race2sm race3sm smoke.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  RACE2  1.727  1  .189 
RACE3  1.797  1  .180  
RACE2SM  4.074  1  .044  
RACE3SM  .652  1  .420  
SMOKE  4.924  1  .026  
Overall Statistics  15.865  5  .007 

Chisquare  df  Sig.  

Step 1  Step  17.854  5  .003 
Block  17.854  5  .003  
Model  17.854  5  .003 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  216.818  .090  .127 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  126  4  96.9 
1.00  53  6  10.2  
Overall Percentage  

69.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  RACE2  1.514  .752  4.051  1  .044  4.545 
RACE3  1.743  .595  8.592  1  .003  5.714  
RACE2SM  .557  1.032  .291  1  .590  .573  
RACE3SM  1.527  .883  2.993  1  .084  .217  
SMOKE  1.750  .598  8.561  1  .003  5.757  
Constant  2.303  .524  19.280  1  .000  .100  
a Variable(s) entered on step 1: RACE2, RACE3, RACE2SM, RACE3SM, SMOKE. 
NOTE: Values for Black:
LOGISTIC REGRESSION VAR=low /METHOD=ENTER race1 race3 race1sm race3sm smoke.
Unweighted Cases(b)  N  Percent  

Selected Cases(a)  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a The variable RACE1 is constant for all selected cases. Since a constant was requested in the model, it will be removed from the analysis.  
b If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  RACE3  1.797  1  .180 
RACE1SM  4.924  1  .026  
RACE3SM  .652  1  .420  
SMOKE  4.924  1  .026  
a Residual ChiSquares are not computed because of redundancies. 

Chisquare  df  Sig.  

Step 1  Step  11.930  3  .008 
Block  11.930  3  .008  
Model  11.930  3  .008 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  222.742  .061  .086 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  RACE3  1.175  .457  6.593  1  .010  3.237 
RACE1SM  1.342  .445  9.111  1  .003  3.827  
RACE3SM  1.119  .787  2.023  1  .155  .327  
Constant  1.734  .362  23.014  1  .000  .177  
a Variable(s) entered on step 1: RACE3, RACE1SM, RACE3SM. 
NOTE: Values for Other:
LOGISTIC REGRESSION VAR=low /METHOD=ENTER race1 race2 race1sm race2sm smoke.
Unweighted Cases(b)  N  Percent  

Selected Cases(a)  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a The variable RACE1 is constant for all selected cases. Since a constant was requested in the model, it will be removed from the analysis.  
b If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  RACE2  1.727  1  .189 
RACE1SM  4.924  1  .026  
RACE2SM  4.074  1  .044  
SMOKE  4.924  1  .026  
a Residual ChiSquares are not computed because of redundancies. 

Chisquare  df  Sig.  

Step 1  Step  6.993  3  .072 
Block  6.993  3  .072  
Model  6.993  3  .072 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  227.679  .036  .051 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  126  4  96.9 
1.00  53  6  10.2  
Overall Percentage  

69.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  RACE2  .351  .588  .356  1  .551  1.420 
RACE1SM  .629  .349  3.248  1  .072  1.875  
RACE2SM  .565  .911  .385  1  .535  1.760  
Constant  1.139  .235  23.606  1  .000  .320  
a Variable(s) entered on step 1: RACE2, RACE1SM, RACE2SM. 
page 84 Table 3.20 Estimated logistic regression coefficients for the variable smoke, loglikelihood, the likelihood ratio test statistic (G), and the resulting pvalue for estimation of the stratified odds ratio and assessment of homogeneity of odds ratios across strata defined by race.
LOGISTIC REGRESSION VAR=low /METHOD=ENTER smoke.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  SMOKE  4.924  1  .026 
Overall Statistics  4.924  1  .026 

Chisquare  df  Sig.  

Step 1  Step  4.867  1  .027 
Block  4.867  1  .027  
Model  4.867  1  .027 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  229.805  .025  .036 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  SMOKE  .704  .320  4.852  1  .028  2.022 
Constant  1.087  .215  25.627  1  .000  .337  
a Variable(s) entered on step 1: SMOKE. 
LOGISTIC REGRESSION VAR=low /METHOD=ENTER smoke race2 race3.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  SMOKE  4.924  1  .026 
RACE2  1.727  1  .189  
RACE3  1.797  1  .180  
Overall Statistics  14.127  3  .003 

Chisquare  df  Sig.  

Step 1  Step  14.697  3  .002 
Block  14.697  3  .002  
Model  14.697  3  .002 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  219.975  .075  .105 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  119  11  91.5 
1.00  48  11  18.6  
Overall Percentage  

68.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  SMOKE  1.116  .369  9.135  1  .003  3.052 
RACE2  1.084  .490  4.894  1  .027  2.956  
RACE3  1.108  .400  7.668  1  .006  3.030  
Constant  1.840  .353  27.205  1  .000  .159  
a Variable(s) entered on step 1: SMOKE, RACE2, RACE3. 
LOGISTIC REGRESSION VAR=low /METHOD=ENTER smoke race2 race3 race2sm race3sm.
Unweighted Cases(a)  N  Percent  

Selected Cases  Included in Analysis  189  100.0 
Missing Cases  0  .0  
Total  189  100.0  
Unselected Cases  0  .0  
Total  189  100.0  
a If weight is in effect, see classification table for the total number of cases. 
Original Value  Internal Value 

.00  0 
1.00  1 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 0  < 2500g  .00  130  0  100.0 
1.00  59  0  .0  
Overall Percentage  

68.8  
a Constant is included in the model.  
b The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 0  Constant  .790  .157  25.327  1  .000  .454 

Score  df  Sig.  

Step 0  Variables  SMOKE  4.924  1  .026 
RACE2  1.727  1  .189  
RACE3  1.797  1  .180  
RACE2SM  4.074  1  .044  
RACE3SM  .652  1  .420  
Overall Statistics  15.865  5  .007 

Chisquare  df  Sig.  

Step 1  Step  17.854  5  .003 
Block  17.854  5  .003  
Model  17.854  5  .003 
Step  2 Log likelihood  Cox & Snell R Square  Nagelkerke R Square 

1  216.818  .090  .127 

Predicted  

< 2500g  Percentage Correct  
Observed  .00  1.00  
Step 1  < 2500g  .00  126  4  96.9 
1.00  53  6  10.2  
Overall Percentage  

69.8  
a The cut value is .500 

B  S.E.  Wald  df  Sig.  Exp(B)  

Step 1(a)  SMOKE  1.750  .598  8.561  1  .003  5.757 
RACE2  1.514  .752  4.051  1  .044  4.545  
RACE3  1.743  .595  8.592  1  .003  5.714  
RACE2SM  .557  1.032  .291  1  .590  .573  
RACE3SM  1.527  .883  2.993  1  .084  .217  
Constant  2.303  .524  19.280  1  .000  .100  
a Variable(s) entered on step 1: SMOKE, RACE2, RACE3, RACE2SM, RACE3SM. 
page 86 Figure 3.4 Graph of the estimated logit of low birth weight and 95 percent confidence intervals as a function of weight at the last menstrual period for white women.
NOTE: This graph cannot be reproduced in SPSS.
page 87 Figure 3.5 Graph of the estimated probability of low weight birth and 95 percent confidence intervals as a function of weight at the last menstrual period for white women.
NOTE: This graph cannot be reproduced in SPSS.