Large coefficients in logistic regression [duplicate]

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This is from the book The statistical sleuth--A course in methods of Data analysis Chapter 20, Exercise 12(c)-(e). I am using logistic regression to predict carrier with possible predictors CK and H. Here is my solution:

Carrier <- c(0,0,0,0,0,1,1,1,1,1)
CK      <- c(52,20,28,30,40,167,104,30,65,440)
H       <- c(83.5,77,86.5,104,83,89,81,108,87,107)
logCK   <- log(CK)
fit4    <- glm(Carrier~logCK+H, family="binomial", control=list(maxit=100))
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred
summary(fit4)
##
## Call:
## glm(formula = Carrier ~ logCK + H, family = "binomial", control = list(maxit = 100))
##
## Deviance Residuals:
##        Min          1Q      Median          3Q         Max
## -1.480e-05  -2.110e-08   0.000e+00   2.110e-08   1.376e-05
##
## Coefficients:
##              Estimate Std. Error z value Pr(>|z|)
## (Intercept)   -2292.8  4130902.8  -0.001        1
## logCK           315.6   589675.2   0.001        1
## H                11.5    21279.6   0.001        1


This results appear to be weird, because it seems that all coefficients are not significant. Also the next question is to do a drop-in-deviance test for this full model and the reduced model that neither of logCK and H is useful predictor. I get:

fit5 <- glm(Carrier~1, family="binomial")
1-pchisq(deviance(fit5)-deviance(fit4), df.residual(fit5)-df.residual(fit4))
## [1] 0.0009765625


So the p-value indicates that at least one of logCK and H is useful. Then I'm stuck at the next question, it asks me to calculate odds ratio for a woman with (CK, H)=(300,100) over one with (CK, H)=(80, 85).

But how can I get a meaningful result with all coefficients in this model ranging so wildly? Is there anything wrong with the way I did this logistic regression? Are there any remedial measures?