1. (A). We run the following:
> smoke = data.frame(Age=as.factor(rep(c("35-44","45-54","55-64","65-74","75-84"),rep(2,5))),
+ Type=as.factor(rep(c("Nonsmokers","Smokers"),5)),
+ Deaths=c(2,32,12,104,28,206,28,186,31,102), Exposure=c(18793,52407,10673,43248,5710,28612,2585,12663,1462,5317))
> smoke.fit = glm(Deaths ~ Age*Type , offset = log(Exposure),
+ family=poisson, data=heart)\
> smoke.fit = glm(Deaths ~ Age*Type , offset = log(Exposure),family=poisson, data=smoke)
> summary(smoke.fit)

Call:
glm(formula = Deaths ~ Age * Type, family = poisson, data = smoke,
offset = log(Exposure))

Deviance Residuals:
[1] 0 0 0 0 0 0 0 0 0 0

Coefficients:
                        Estimate Std. Error z value Pr(>|z|)
(Intercept)                -9.1481 0.7071 -12.937 < 2e-16 ***
Age45-54                2.3575 0.7638 3.087 0.00202 **
Age55-64                3.8303 0.7319 5.233 1.67e-07 ***
Age65-74                4.6228 0.7319 6.316 2.68e-10 ***
Age75-84                5.2945 0.7296 7.257 3.95e-13 ***
TypeSmokers          1.7470 0.7289 2.397 0.01653 *
Age45-54:TypeSmokers -0.9868 0.7901 -1.249 0.21167
Age55-64:TypeSmokers -1.3630 0.7562 -1.802 0.07148 .
Age65-74:TypeSmokers -1.4424 0.7565 -1.907 0.05656 .
Age75-84:TypeSmokers -1.8472 0.7572 -2.440 0.01471 *

Choose any published database from the internet such as the Census...