SAS Interview Questions

SAS is a software platform that provides solutions for data management, analytics, and reporting. Some of the functions it performs are:

Access, manipulate, transform, and analyze data from various sources and formats, such as databases, files, web services, and APIs.

Perform statistical analysis, data mining, machine learning, forecasting, optimization, and simulation on the data, using a variety of methods and techniques.

Create and deliver reports, dashboards, visualizations, and interactive applications that present the data and insights in a user-friendly and customizable way.

Integrate with other software and systems, such as Microsoft Office, Python, R, Java, and cloud platforms, to extend its functionality and interoperability.

The SUBSTR function is a common string function that extracts a part of a string. It takes three arguments: the string to extract from, the starting position, and the length of the substring. It returns the substring as a new string.

For example, SUBSTR ("Hello World", 2, 4) returns "ello", which is the substring of "Hello World" starting from the second position and having four characters.

The TRANSLATE function is a string function that replaces certain characters in a string with other characters. It takes three arguments: the input string, the characters to be replaced, and the new characters. It returns the modified string as the output.

For example, TRANSLATE ('Hello', 'eo', 'ai') returns 'Halla', which is the input string with 'e' replaced by 'a' and 'o' replaced by 'i'.

PROC SORT is a procedure in SAS that orders the observations in a data set by the values of one or more variables. The syntax of PROC SORT is as follows:

PROC SORT DATA=input_dataset OUT=output_dataset; BY variable(s); RUN;

The DATA option specifies the input data set that needs to be sorted. The OUT option specifies the output data set that contains the sorted observations. The BY statement lists the variables that are used to sort the data set, in ascending or descending order.

For example, the following code sorts the data set original_data by the variable points in descending order and creates a new data set data5:

PROC SORT DATA=original_data OUT=data5; BY descending points; RUN;

PROC UNIVARIATE is a procedure in SAS that performs univariate analysis on numeric variables. Some of the features and benefits of using PROC UNIVARIATE are:

It can compute a wide range of descriptive statistics, such as mean, median, mode, standard deviation, skewness, kurtosis, percentiles, and confidence intervals.

It can test for location, normality, and outliers, using various methods, such as t-test, sign test, Shapiro-Wilk test, and Dixon's test.

It can fit and compare various continuous distributions, such as normal, lognormal, gamma, beta, and Weibull, and provide goodness-of-fit tests, such as Kolmogorov-Smirnov test and chi-square test.

It can produce high-resolution graphics, such as histograms, box plots, stem-and-leaf plots, probability plots, and quantile-quantile plots, to visualize the data distribution and the fitted models.

It can create output data sets that contain summary statistics, percentiles, fitted parameters, and kernel density estimates, which can be used for further analysis or reporting.

The APPEND procedure is a procedure in SAS that adds the observations from one data set to the end of another data set. To use it, you need to specify the base data set, which is the data set that you want to append to, and the data set that contains the observations that you want to append. You can also use the FORCE option to concatenate data sets that have different variables or attributes.

For example, the following code appends the data set WORK.SECURITY to the data set WORK.SALES and creates a new data set WORK.SALES_SECURITY:

PROC APPEND BASE=WORK.SALES DATA=WORK.SECURITY OUT=WORK.SALES_SECURITY FORCE; RUN;

The BMDP procedure is a procedure in SAS that allows you to call any BMDP program to analyze data in a SAS data set or convert a SAS data set to a BMDP save file. BMDP is a library of statistical analysis programs that were originally developed at the UCLA Health Sciences Computing Facility. Some of the main features and applications of the BMDP procedure are:

Access a wide range of BMDP programs that perform various types of statistical analysis, such as regression, ANOVA, factor analysis, cluster analysis, survival analysis, and multivariate analysis.

Use a common input convention and a simple command language to specify the BMDP program, the input and output data sets, and the options and parameters for the analysis.

Print the BMDP program output as part of the SAS log or create output data sets that contain summary statistics, test results, fitted values, and residuals.

Convert a SAS data set to a BMDP save file that can be used by other BMDP programs or by other SAS procedures that support the BMDP engine.

RUN-group Processing is a feature of some SAS procedures that allows you to submit multiple groups of statements within the same procedure without restarting it. Each group of statements, followed by a RUN statement, is called a RUN-group. The procedure can contain other SAS statements such as AXIS, BY, GOPTIONS, LEGEND, TITLE, or WHERE.

For example, the following code uses RUN-group Processing with PROC REG to fit two different models to the same data set and compare the results:

proc reg data=sales;   model sales = price / vif; run;   model sales = price size / vif; run; quit;

BY-group Processing is a feature of SAS that enables you to process data sets that are grouped or ordered by one or more common variables. By using the BY statement with a DATA or PROC step, you can perform analysis or manipulation on each group of observations separately. Some of the benefits of BY-group Processing are:

It can simplify the coding and reduce the number of steps required to perform complex tasks on multiple groups of data.

It can enhance the efficiency and performance of the SAS program by avoiding unnecessary sorting or merging of data sets.

It can produce more informative and customized output by adding BY-group information to the titles, labels, footnotes, or statistics.

The CALENDAR procedure is a procedure in SAS that enables you to create and display calendars from SAS data sets in a monthly format. You can use it to produce two types of calendars: a schedule calendar, which shows events, holidays, and nonwork periods, or a summary calendar, which summarizes data and shows only one-day events and holidays.

To use the CALENDAR procedure, you need to specify the input data sets that contain the information for the calendar, such as activities, holidays, calendar, and workdays. You can also use various options and statements to customize the appearance and content of the calendar, such as colors, fonts, titles, labels, and footnotes.

For example, the following code creates and displays a schedule calendar for January 2024, using the data sets WORK.ACTIVITIES, WORK.HOLIDAYS, WORK.CALENDAR, and WORK.WORKDAYS:

PROC CALENDAR DATA=WORK.ACTIVITIES HOLIDAY=WORK.HOLIDAYS                CALENDAR=WORK.CALENDAR WORKDAY=WORK.WORKDAYS;    MONTH 1 2024;    TITLE 'Schedule Calendar for January 2024';    FOOTNOTE 'Source: WORK data sets'; RUN;

SAP Business Objects and SAS Business Intelligence are both software platforms that provide business analytics and reporting capabilities. However, they have some differences in terms of features, pricing, usability, and performance. Here are some of the main differences:

SAS Business Intelligence has more advanced and comprehensive analytics tools, such as predictive analysis, model-driven analytics, and in-database processing. SAP Business Objects has more intuitive and user-friendly interface, such as interactive dashboards, visualizations, and score cards.

SAS Business Intelligence is more expensive and less scalable than SAP Business Objects. SAS Business Intelligence does not offer perpetual licenses, while SAP Business Objects does. SAS Business Intelligence also requires more hardware and software resources to run smoothly, while SAP Business Objects can handle large data sets with less resources.

SAS Business Intelligence has better quality of support and product direction than SAP Business Objects. SAS Business Intelligence has a higher customer satisfaction rate, a more responsive and knowledgeable support team, and a more clear and innovative roadmap. SAP Business Objects has a lower customer satisfaction rate, a less responsive and knowledgeable support team, and a less clear and innovative roadmap.