Hi I need help with generating contingency tables, and then using ODS to output everything. I have included the question and also what I have so far, Thanks     Problem: The SAS data set Houston.sas7bdat was obtained from the Behavioral Risk Factor Surveillance System (BRFSS) in 2012. The samples are people ≥ 18 year-old in Houston. This data set contains the following categorical variables: asthma = (0, 1) = (never had asthma, ever had asthma) adult = (1, 2) = (adults, elders) sex = (1, 2) = (male, female) race = (1, 2, 3, 4) = (White, Black, Hispanic, Others) bmicat = (1, 2, 3, 4) = (underweight, normal, overweight, obese) smoking = (1, 2, 3) = (non-smoker, former smoker, current smoker) insurance = (1, 2) = (have insurance, no insurance) Need to generate a data set which can exactly fill out the table below.     Never had asthma Ever had asthma         N %† N %† χ2 value P-value Adult Adult               Elder             Sex Male               Female             Race White               Black               Hispanic               Others             † % = Row percentage             Step 1) Use a PROC FREQ to generate contingency tables with asthma in columns and adult, sex, and race in rows. Each cell only shows counts and row percentages. Have the Chi-square test for each variable, and then use ODS to generate two data sets (named freq and chisq) for cross-tables and statistics separately.             Step 2) Use a data step to clean the freq data set. Remove total frequencies and percentages (Hint: use _TYPE_ variable). Then use the IF-THEN-ELSE statement to output the value of frequency and row percentage into three different sub-datasets for adult, sex, and race, respectively. Step 3) Use an appropriate procedure to transpose frequency and row percentage by adult/sex/race in those sub-datasets.             Step 4) Use the MERGE statement to combine the same transposed data set twice. One data set only contains frequency and another one only contains row percentage. Rearrange the position of variables as shown in the table.             Step 5) Use a data step to exact the chi-square value and p-value for adult. (Hint: Use the scan function on the variable Table to keep wanted rows)             Step 6) Combine the merged data set and chi-square data set for adult, which looks like:             Step 7) Repeat step 3~6 for sex and race.             Step 😎 Combine three clean data sets.             Step 9) Export the data set to a csv file and directly copy all values from the excel file to the table, like:     Never had asthma Ever had asthma         N %† N %† χ2 value P-value Adult Adult 695 87.09 103 12.91 0.1339 0.7144   Elder 262 87.92 36 12.08 . . Sex Male               Female             Race White               Black               Hispanic               Others               b)  Use PROC FORMAT to create customized formats for variables asthma, bmicat, smoking, and insurance according to the description on page 3. Draw frequency plots for bmicat, smoking and insurance with grouped bars by asthma         THIS IS THE CODE I HAVE SO FAR, BUT GETTING STUCK AT OUTPUTTING THE FIRST STEP:   ODS LISTING; DATA houston ; set 'C:\Data\houston.sas7bdat' ; RUN; PROC FORMAT ; value asthmalbl 1 = 'never had asthma' 0 = 'ever had asthma'; value adultlbl 1 = 'adults' 2 = 'elders' ; value sexlbl 1 = 'male' 2 = 'female' ; value racelbl 1 = 'White' 2 = 'Black' 3 = 'Hispanic' 4 = 'Others'; value bmicatlbl 1 = 'underweight' 2 = 'normal' 3 = 'overweight' 4 = 'obese' ; value smokinglbl 1 = 'non-smoker' 2 = 'former smoker' 3 = 'current smoker' ; value insurancelbl 1 = 'have insurance' 2 = 'no insurance' ; RUN; PROC PRINT DATA = houston ; format asthma asthmalbl. adult adultlbl. sex sexlbl. race racelbl. bmicat bmicatlbl. smoking smokinglbl. insurance insurancelbl. ; RUN; PROC FREQ DATA = houston ; TABLES race adult sex * Asthma / NOROW NOCOL NOFREQ CHISQ CL; ODS OUTPUT CrosstabFreqs = houston2 TITLE ; format asthma asthmalbl. adult adultlbl. sex sexlbl. race racelbl. bmicat bmicatlbl. smoking smokinglbl. insurance insurancelbl. ; RUN;     This is a sample of the data in case you do not want to use the attachment   Obs asthma adult sex race bmicat educat smoking insurance 1 1 1 2 1 3 3 3 1 2 0 1 2 1 2 3 3 1 3 0 1 1 3 2 2 2 2 4 0 2 2 4 2 3 1 1 5 0 2 2 3 3 1 3 1 6 0 2 1 1 3 3 3 1 7 0 2 1 1 2 2 2 1 8 0 1 2 2 2 2 3 1 9 0 1 1 1 3 3 3 1 10 0 1 1 1 2 3 1 1 11 0 2 2 1 3 3 2 1 12 0 2 2 1 4 2 2 1 13 0 1 1 1 2 3 3 1 14 0 1 1 1 2 3 2 1 15 0 2 2 1 3 3 3 1 16 0 1 1 1 2 3 1 1 17 0 2 2 1 2 3 3 1 18 0 1 1 1 4 3 2 1 19 0 1 2 2 2 2 3 1 20 0 1 2 1 4 3 3 1   I appreciate any help, thank you! ... View more