A00-215 Questions and Answers

When using a BY statement in a DATA step, SAS creates two temporary variables for each BY variable: FIRST.variable and LAST.variable. These are used to indicate the beginning and end of each BY group.

For Ellen's observation:

• First.State=1 because this is the first occurrence of the state "OH" in the dataset when sorted by the state variable, which makes it the beginning of a new group for "OH".
• Last.State=0 because Ellen is not the last observation

for the state "OH" group. In the given dataset snippet, there appears to be only one observation for the state "OH", but since the data is sorted by state, and we don't see another "OH" below Ellen, we can deduce that Ellen's observation is not the last "OH" observation in the entire dataset. If there were no more "OH" observations following Ellen's, then Last.State would be 1.

The FIRST.variable is set to 1 for the first observation in each BY group and 0 for all other observations in the group. Conversely, LAST.variable is set to 1 for the last observation in each BY group and 0 otherwise. Since we're assuming that the dataset is larger than the snippet shown, and the data continues beyond what is visible, we must assume there are more observations for "OH" unless indicated otherwise.

References

• "SAS 9.4 Language Reference: Concepts." SAS Institute Inc.
• "BY-Group Processing" in "SAS Programming 1: Essentials" course material, SAS Institute.

In SAS, iterative DO statements are used to repeat a block of statements a specified number of times. These DO statements have a specific syntax that includes initializing a variable, setting an ending value, and specifying an increment (or decrement). The syntax generally follows the pattern: DO variable = start TO end BY increment;

Let’s evaluate each option provided:

A) Do 100 to 1200 by 100;This statement is syntactically incorrect because it lacks a variable to iterate over. An iterative DO loop must specify a variable that will take on each value in the specified range. The correct form should be something like do i = 100 to 1200 by 100;.

B) Do num = 1.1 to 1.9 by 0.1;This statement is valid. It initializes the variable num at 1.1 and increments by 0.1 until it reaches 1.9. This is a typical use of the iterative DO loop for non-integer increments.

C) Do year = 2000 to 2016 by 2;This statement is also valid. It initializes year at 2000 and increments by 2, going through values like 2002, 2004, etc., up to and including 2016. This is a standard use for iterating over years or other sequentially numbered items.

D) Do reverse = 10 to 1 by -1;This statement is valid. It initializes reverse at 10 and decrements by 1 until it reaches 1. Using negative increments is a legitimate approach for counting downwards.

References

• SAS 9.4 Statements: Reference, "DO Statement."
• SAS Support: DO Loop Documentation

Understanding how to properly format DO loops in SAS is fundamental for effectively managing repetitive tasks in your data analysis. Mistakes like those seen in option A can lead to syntax errors, preventing your code from executing. Always ensure that your loops are correctly structured and that each component of the loop is clearly defined.

The PROC MEANS report shown in the image displays statistics for a single variable Age with no decimal places. The correct SAS code to generate such a report is option D, which uses the PROC MEANS procedure with the maxdec=0 option to control the number of decimal places displayed (in this case, zero) and specifies Age as the analysis variable using the var statement.

The PROC MEANS procedure computes descriptive statistics for numeric variables. The maxdec=0 option is used here to remove decimal places from the report output, which matches the output shown where the mean, standard deviation, minimum, and maximum are all integers.

The other options are incorrect for the following reasons:

• A uses the class statement, which is not appropriate here because Age is not a classification variable but an analysis variable.
• B uses the group statement, which is not a valid statement in the PROC MEANS procedure.
• C uses the by statement, which requires the data to be sorted by the BY variable and does not fit the output provided since it would produce separate statistics for each value of Age.

References:

• SAS 9.4 documentation for the PROC MEANS statement: SAS Help Center: PROC MEANS

In SAS, the DATA step is a powerful tool that allows programmers to perform a variety of tasks such as reporting, managing, and analyzing data. The DATA step processes data one observation at a time, making it highly efficient for data manipulation tasks. It enables the creation of new datasets, modification of existing ones, and complex data transformations. Additionally, within a DATA step, you can use a wide range of programming statements and functions to calculate new variables, merge or sort datasets, and perform conditional processing. The flexibility and functionality provided by DATA steps make them a fundamental part of SAS programming for handling and preparing data for further analysis or reporting.References: SAS 9.4 Language Reference: Concepts.

In SAS, footnotes are set using the footnote statement and they will appear on all subsequent output until they are either changed or cleared. Based on the second image provided with the SAS code, the footnote for the second PROC PRINT report is set immediately before it runs.

The code sets footnote1 as 'Created by HR' and footnote2 as 'Confidential' initially. However, before the second PROC PRINT step, footnote2 is redefined as 'Draft - Do Not Distribute'. Since footnote1 is not redefined or cleared, it is no longer in effect for the second report.

Therefore, the only footnote that appears for the second PROC PRINT report is what is defined for footnote2 at that point in the code, which is 'Draft – Do Not Distribute'. That's why the correct answer is D.

References:

• SAS 9.4 documentation for the FOOTNOTE statement: SAS Help Center: FOOTNOTE Statement

The report shown in the image is a two-way frequency table generated by the PROC FREQ procedure which shows the frequency and percentage distribution of two categorical variables. The correct answer is option B:

• proc freq data=cars; tells SAS to use the PROC FREQ procedure on the dataset cars.
• tables make*drivetrain; requests the frequency table for the cross of make and drivetrain. The asterisk (*) denotes a two-way table of the two categorical variables.
• run; indicates the end of the proc freq step.

The resulting table in the output has the statistics for Make broken down by Drivetrain category, which matches the structure seen in the provided image.

Options A, C, and D do not correctly request the two-way table of make by drivetrain. Specifically, C and D reverse the order, which would change the orientation of the table in the output, and A is missing the asterisk (*) which is needed for the cross-tabulation of the two variables.

References:

• SAS 9.4 documentation for the PROC FREQ statement: SAS Help Center: PROC FREQ

In SAS, the code you provided involves creating two datasets, table1 and table2, from the dataset sashelp.shoes. The key part to understand here is how the DATA statement and OUTPUT statement interact with the specified datasets.

• DATA Statement: The statement data table1 table2; initiates the creation of two new datasets named table1 and table2.
• SET Statement: The set sashelp.shoes; statement is used to read data from the sashelp.shoes dataset. This dataset includes data on shoe sales from SASHELP library, which is commonly used for demonstration purposes in SAS.
• OUTPUT Statement: In the context of the DATA step where multiple datasets are specified in the DATA statement (as in table1 table2), the OUTPUT statement without a dataset name specified outputs the current observation to all datasets listed in the DATA statement. This is a critical point because it determines where the data goes after processing in the DATA step.
• Execution: When the run; statement is executed, it processes each observation from sashelp.shoes. For each observation, because there is no condition or additional OUTPUT statements specifying dataset names, each observation is output to both table1 and table2.

Therefore, the correct behavior as described is that each observation in sashelp.shoes is written to both table1 and table2. This effectively duplicates each row from the source into both target datasets.

References:

• SAS 9.4 Language Reference: Concepts, "DATA Step Processing" and "OUTPUT Statement" sections provide detailed explanations on how DATA steps process and how OUTPUT statement works in different contexts.
• Practical examples and explanations from SAS programming courses and official SAS documentation, which discuss DATA and SET statements, and their interaction with OUTPUT in data duplication scenarios.

The correct syntax for creating an Excel workbook using the ODS (Output Delivery System) Excel destination with a specific style is provided by option D. The syntax is as follows: ods excel file='c:\report.xlsx' style=analysis;This statement instructs SAS to output the results of subsequent procedures to an Excel workbook located at c:\report.xlsx, applying the ANALYSIS style to the output. The file= option specifies the path and name of the Excel file to be created or overwritten, and the style= option indicates the style template to be used for the output. The ANALYSIS style is one of the predefined styles that can be applied to the output to control the appearance of tables and graphs. Other options like A) and B) are incorrect due to syntax errors and misplacement of keywords. Option C) has a typo in the file path and incorrect syntax usage.References: SAS 9.4 Output Delivery System: User's Guide.

The PROC MEANS statement is used to compute descriptive statistics of data in SAS. Option A is the correct code to produce the report shown in the first image because of the following reasons:

• data=sashelp.shoes specifies the dataset on which the procedure is to be performed.
• sum mean specifies that the summary statistics should include the sum and mean of the variables.
• var Sales; specifies that the variable Sales is the analysis variable for which the summary statistics are to be computed.
• class Product; specifies that the procedure should classify results by unique values of the Product variable. This will produce separate statistics for each type of product, which aligns with the structure of the report provided in the image.

Options B, C, and D are incorrect for the following reasons:

• B uses group instead of class, and group is not a valid statement in the context of PROC MEANS. Also, var Sale; is incorrect as the variable name is Sales.
• C includes nobe; which is not a valid SAS option and seems to be a typo. The by statement is used for sorting data, not for classifying groups as class does.
• D incorrectly uses sum Salad; and mean Sales; as separate statements and has an invalid use of by product; which is not needed here.

References:

• SAS 9.4 documentation for the PROC MEANS statement: SAS Help Center: PROC MEANS

The program fails because option C is correct: you must include the dataset height2 in the DATA statement. The DATA step is trying to write to a dataset named height2, which has not been defined in the DATA statement. The correct syntax to create two datasets in one DATA step is to list them both in the DATA statement like so:

data height1 height2;

The use of numbers in dataset names is not a problem in SAS, nor is outputting to different data sets within a single DATA step. Additionally, using two different DATA statements for HEIGHT1 and HEIGHT2 is not required and is not the cause of the failure.

References:

• SAS documentation on the DATA statement and creating multiple data sets.

https://documentation.sas.com/?docsetId=proc &docsetTarget=n12jw1r2n7auqqn1urrh8jwezk00.htm&docsetVersion=9.4&locale=en#:~:text=ASCENDING%20is%20the%20default%20sort%20order.&text=In%20a%20PROC%20SORT%20KEY,is%20sorted%20in%20ascending%20order.

The default sort order for PROC SORT in SAS is ascending. This means that if no other sort order is specified, SAS will arrange the data in ascending order based on the values of the variable(s) listed in the BY statement.

Here's an example:

• proc sort data=mydata; by variable; run; sorts mydata by variable in ascending order by default.

The other options are incorrect in the context of the default sort order:

• A. 'Internal' is not a sort order.
• C. 'Formatted' refers to sorting data based on formatted values, not the default order.
• D. 'Descending' is an alternative sort order that must be explicitly specified with the DESCENDING keyword.

References:

• SAS 9.4 documentation for the PROC SORT statement: [SAS Help Center: PROC SORT]

Option A is correct. The (obs=10) option is used as a data set option within the proc print statement to limit the number of observations processed to the first 10. The syntax should be placed immediately after the data set name within parentheses. Options B, C, and D are incorrect because they either use the wrong option or place the obs option incorrectly outside of the parentheses or with incorrect syntax.

References:

• SAS 9.4 documentation on the obs= data set option.

In the context of the PROC MEANS procedure in SAS, the CLASS statement is used to specify categorical variables whose unique values group the data before calculating statistics. This allows PROC MEANS to calculate statistics like the mean, sum, or standard deviation for each class or group of data defined by the CLASS variables.

The CLASS statement works as follows:

• class variable-list; specifies one or more variables to define groups for analysis.
• When used, PROC MEANS computes the requested statistics for each level of the variables listed, which can be very helpful for analyzing how groups compare across different statistics.

The other options are not used in the PROC MEANS procedure to specify grouping of data:

• B. GROUP is not a valid statement in PROC MEANS.
• C. SUMBY is not a valid statement in SAS.
• D. VAR is used in PROC MEANS to specify the analysis variables for which statistics are to be computed, not to group the data.

The PROC SORT statement that specifies the sort variable is: BY

The BY statement in PROC SORT is used to specify the variable(s) by which the data should be sorted.

The default length of numeric variables in SAS data sets is 8 bytes.

In SAS, the default byte size of a numeric variable is 8 bytes. This allows for a significant amount of precision and a wide range of numeric values to be stored within a numeric variable, regardless of the actual number of digits in the number being stored.

References:

• SAS documentation on numeric variables, SAS Institute.

Option B is correct. In SAS, variable names must be 1 to 32 characters in length. The other statements about variable names are incorrect:

• A is incorrect because variable names are not case sensitive in SAS.
• C is incorrect because variable names are required when you create a data set; each column must have a unique name.
• D is incorrect because variable names cannot start with a number, though they can start with an underscore or a letter.

References:

• SAS 9.4 documentation on SAS variables.

https://v8doc.sas.com/sashtml/lrcon/z0961108.htm

In SAS, the automatic variable _N_ represents the number of times the DATA step has iterated. It starts at 1 and increments by 1 each time the DATA step loops back to the top. This variable can be used within the DATA step to monitor the iteration count but is not written to the output data set. The other options, A, C, and D, are not automatically created variables that track iterations in the DATA step.

References:

• SAS documentation on automatic variables.