There are a number of techniques for performing these table lookups. These techniques can be radically different both in terms of programming complexity and performance.
PROC TRANSPOSE provides the ability to go from a long dataset (where there are multiple rows for a given subject) to a wide dataset (where there are multiple columns for a subject).
Sorting the data is always a resource-intensive operation. Therefore, using PROC SORT efficiently can save you both time and computing resources.
There are a number of options associated with PROC SORT that can be used not only to control the performance and capabilities of the procedure but also to the resulting data set.
PROC SQL in SASis a Procedure that combines the functionality of DATA and PROC steps into a single step. PROC SQL can perform sorting of data, creating summaries of data, subsetting, joining (merge), concatenation of datasets, create new or calculated variables, printing the results or create a new table or view all in a single step.
The Proc Tabulate procedure is used to create tables in SAS. Unlike Proc FREQ, this procedure can handle multiple variables in the row and column expressions. PROC TABULATE has a number of statements that define how this procedure will summarize the data.
RETAIN in SAS is used to “remember” values from previous observations. Variables that do not come from SAS data sets are, by default, set to a missing value during each iteration of the DATA step.
A confidence interval is constructed from a sample data is a range of values that is likely to include the population parameter with a certain probability.
The objective of a confidence interval is to provide location and precision of population parameters.
Combining datasets vertically involves stacking one or more datasets. Before combining datasets It’s important to understand the descriptor portion or structure and contents of your input data sets.
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