PROC CONTENTS displays the descriptor portion of a dataset, showing variable names, types (numeric/character), lengths, formats, and informats. This is the most direct and professional way to confirm how SAS interpreted each column’s data type after import, providing the necessary information to decide on the correct corrective action.

In a DATA step, statements execute sequentially for each observation. If a subsetting IF statement (e.g., `IF Region = ‘North’;`) that filters observations is placed *before* the assignment statement for DiscountPrice, and the condition is false for all observations, then the calculation never occurs for any record, resulting in a missing value for DiscountPrice. The syntax in option 1 is correct, a missing RUN statement would typically generate an error, and option 4 uses a non-existent function.

The WORK library is a temporary library that exists only for the current SAS session. The DATA statement `DATA work.new;` explicitly places the output dataset NEW in this temporary WORK library. The WHERE statement subsets the incoming observations from SASHELP.CLASS *before* they are processed by the DATA step, so only observations with Age > 12 are written to WORK.NEW.

To produce a sorted printed list, you must first sort the data using PROC SORT. The WHERE clause in the PROC SORT step correctly subsets the data to Quantity < 10 before sorting by Name. The sorted dataset is then printed using PROC PRINT. Option 1 sorts by the wrong variable. Options 2 and 4 incorrectly try to use SORT BY or BY within PROC PRINT, which is not valid for simple listing; the BY statement in PROC PRINT requires the data to already be sorted and creates separate sections for each BY group.

In PROC REPORT, the COLUMN statement is used to specify which variables from the input dataset appear in the report and in what order. The GROUP statement, when applied to a variable, tells PROC REPORT to combine (aggregate) all rows that share the same value for that variable. The procedure then displays one summary row per unique group value, making it essential for creating summarized, break-down reports.

The INPUT function is used to convert character data to numeric (or other types) using a specified informat. The pattern ‘2023-10-31’ matches the `YYMMDD10.` informat, which reads a 4-digit year, 2-digit month, and 2-digit day with a hyphen separator. The PUT function (option 2) converts numeric to character. DATEPART (option 3) extracts the date from a SAS datetime value. Option 4 uses the wrong informat for the given pattern.

The VAR (variable) statement within PROC MEANS explicitly lists the numeric variables for which you want to calculate statistics (like N, MEAN, SUM, etc.). This is the standard and most direct method to control the procedure’s analysis variables. The KEEP= option (option 1) would work but is less conventional for this specific purpose within a procedure. Options 3 and 4 use invalid syntax for this context.

The requirement is to keep all records from the SALES_Q3 dataset (the ‘right’ table in this merge context) and bring in matching information from EMPLOYEE_MASTER. This is a right join. The code `IF b;` is a subsetting IF that keeps only observations that came from the SALES_Q3 dataset (where the IN= variable `b` is true). This ensures all sales are included, and non-matching sales will have missing values for Name and Dept. Option 1 and 2 would produce a full join (all matches). The terminology in option 3 is slightly off as the code shown technically performs a right join.

In PROC FREQ, listing variables separated by a space in the TABLE statement (e.g., `TABLE Region Product;`) produces separate one-way frequency tables for each variable. To create a single two-way crosstabulation table, the variables must be connected with an asterisk (`*`), as in `TABLE Region * Product;`. This syntax tells SAS to cross the categories of the first variable with the categories of the second. Option 2 uses a comma, which is not the standard operator for a simple crosstab in PROC FREQ.

PROC CONTENTS displays the descriptor portion of a dataset, showing variable names, types (numeric/character), lengths, formats, and informats. This is the most direct and professional way to confirm how SAS interpreted each column’s data type after import, providing the necessary information to decide on the correct corrective action.0