Some R code for HW 1.10:

help(binom.test)
binom.test(60,100,alternative="greater")
prop.test(60,100,alternative="greater")

SAS code for HW 3.9:

data table;
 input Diagnosis$ Drugs$ count @@;
 datalines;
1 1 105 1 2 8 2 1 12 2 2 2 3 1 18 3 2 19 4 1 47 4 2 52 5 1 0 5 2 13
;
proc format;
value $dc '1' = 'schitzophrenic' '2' = 'affective disorder' '3' = 'neurosis' 
          '4'='Personality' '5' = 'special symptoms';
value $mc '1' = 'yes' '2' = 'no';
proc freq  order=data; weight count;
 format Diagnosis $dc. Drugs $mc.;
 tables Diagnosis*Drugs / plcorr;
 exact chisq;
proc genmod  order=data;  class Diagnosis Drugs;
 model count = Diagnosis Drugs / dist=poi link=log residuals;
data table2;
 input Diagnosis$ Drugs$ count @@;
 datalines;
1 1 105 1 2 8 2 1 12 2 2 2
;
proc freq  order=data; weight count;
 tables Diagnosis*Drugs / chisq;
data table3;
 input Diagnosis$ Drugs$ count @@;
 datalines;
1 1 18 1 2 19 2 1 47 2 2 52
;
proc freq  order=data; weight count;
 tables Diagnosis*Drugs / chisq;
 data table4;
 input Diagnosis$ Drugs$ count @@;
 datalines;
1 1 117 1 2 10 2 1 65 2 2 71 3 1 0 3 2 13
;
proc freq  order=data; weight count;
 tables Diagnosis*Drugs / chisq;
run;

SAS code for HW 3.11:

data table;
 input Aspiration$ Income$ count @@;
 datalines;
1 1 9 2 1 44 3 1 13 4 1 10 1 2 11 2 2 52 3 2 23 4 2 22 1 3 9 2 3 41 3 3 12 4 3 27 
;
proc freq  order=data; weight count;
 tables Aspiration*Income / chisq plcorr;
proc genmod  order=data;  class Aspiration Income;
 model count = Aspiration Income / dist=poi link=log residuals;
run;