Call:
glm(formula = y ~ 1, family = binomial("logit"))
Coefficients:
            Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.26937    0.06427  -4.191 2.78e-05 ***
---
    Null deviance: 101.62  on 7  degrees of freedom
Residual deviance: 101.62  on 7  degrees of freedom
AIC: 141.82
glm(formula = y ~ age, family = binomial("logit"))
Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -0.915497   0.274247  -3.338 0.000843 ***
age          0.016486   0.006785   2.430 0.015114 *  
---
    Null deviance: 101.618  on 7  degrees of freedom
Residual deviance:  95.684  on 6  degrees of freedom
AIC: 137.89
> anova(mod0,mod1,test="Chisq")
Analysis of Deviance Table
Model 1: y ~ 1
Model 2: y ~ age
  Resid. Df Resid. Dev Df Deviance Pr(>Chi)  
1         7    101.618                       
2         6     95.684  1   5.9332  0.01486 *
---
glm(formula = y ~ age + mr, family = binomial("logit"))
Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.602726   0.299229  -5.356  8.5e-08 ***
age          0.025036   0.007186   3.484 0.000494 ***
mr           1.468761   0.163629   8.976  < 2e-16 ***
---
    Null deviance: 101.6176  on 7  degrees of freedom
Residual deviance:   7.8012  on 5  degrees of freedom
AIC: 52.002
> anova(mod1,mod2,test="Chisq")
Analysis of Deviance Table
Model 1: y ~ age
Model 2: y ~ age + mr
  Resid. Df Resid. Dev Df Deviance  Pr(>Chi)    
1         6     95.684                          
2         5      7.801  1   87.883 < 2.2e-16 ***
---
glm(formula = y ~ age + mr + age:mr, family = binomial("logit"))
Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept) -1.257466   0.334854  -3.755 0.000173 ***
age          0.016436   0.008141   2.019 0.043509 *  
mr           0.013092   0.676970   0.019 0.984570    
age:mr       0.039497   0.018047   2.189 0.028628 *  
---
    Null deviance: 101.6176  on 7  degrees of freedom
Residual deviance:   2.7998  on 4  degrees of freedom
AIC: 49.001
> anova(mod2,mod3,test="Chisq")
Analysis of Deviance Table
Model 1: y ~ age + mr
Model 2: y ~ age + mr + age:mr
  
Resid. Df Resid. Dev Df Deviance Pr(>Chi)  
1         5     7.8012                       
2         4     2.7998  1   5.0014  0.02533 *
---
glm(formula = y ~ factor(sat), family = binomial("logit"))
Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -0.78846    0.20386  -3.868 0.000110 ***
factor(sat)2  0.08057    0.23736   0.339 0.734278    
factor(sat)3  0.25707    0.24936   1.031 0.302582    
factor(sat)4  0.46829    0.27226   1.720 0.085430 .  
factor(sat)5  1.12493    0.33186   3.390 0.000699 ***
factor(sat)6  1.33005    0.30165   4.409 1.04e-05 ***
factor(sat)7  1.93359    0.36839   5.249 1.53e-07 ***
factor(sat)8  3.39115    0.76065   4.458 8.27e-06 ***
---
    Null deviance: 1.0162e+02  on 7  degrees of freedom
Residual deviance: 5.1958e-14  on 0  degrees of freedom
AIC: 54.201
> anova(mod3,mod4,test="Chisq")
Analysis of Deviance Table

Model 1: y ~ age + mr + age:mr
Model 2: y ~ factor(sat)
  Resid. Df Resid. Dev Df Deviance Pr(>Chi)
1         4     2.7998                     
2         0     0.0000  4   2.7998   0.5919
> 
> logLik(mod1)
'log Lik.' -66.94255 (df=2)
> logLik(mod2)
'log Lik.' -23.00093 (df=3)
> logLik(mod3)
'log Lik.' -20.50026 (df=4)
> logLik(mod4)
'log Lik.' -19.10034 (df=8)