SAS is a powerful statistical software package used for data analysis and reporting. It is used by many organizations to leverage data for business intelligence, predictive analytics, and more. The SAS tutorial is designed to help users learn the basics of the software, from the basics of data analysis to more advanced techniques. The tutorial introduces the user to the SAS language, data management, graphics, and more. It also covers topics such as debugging, optimization, and data integration. It is a great way to learn the basics of the software, as well as more advanced concepts.
Audience
The SAS tutorial audience includes business analysts, data scientists, and other professionals who are interested in learning the SAS programming language. It is also suitable for students and beginners who are looking to gain a comprehensive understanding of the software.
Prerequisites
SAS is a powerful software package used for data manipulation, analysis, and reporting. It is used for a variety of tasks, including data processing, data mining, and statistical analysis. In order to use SAS effectively, it is important to have an understanding of the basics of the software package. This SAS tutorial will provide you with an introduction to the software and its capabilities.
This SAS tutorial is designed for beginners with no prior experience in the software. It will cover the basic concepts of using SAS, such as data manipulation, data analysis, and reporting. It will also provide an overview of the different types of tasks that can be accomplished with SAS, as well as the various features available.
This SAS tutorial assumes that you have a basic understanding of computers and basic math skills. If you do not have this knowledge, then it is recommended that you take a course or find a tutorial that provides an overview of these topics. Additionally, if you are new to data analysis, then you may want to find a tutorial that covers the basics of data analysis.
SAS – Overview
SAS (Statistical Analysis System) is a widely used software suite for data management, predictive analytics, and business intelligence. It is used by organizations to analyze and visualize data, identify trends, and create predictive models. It includes a graphical user interface, a programming language, and various other tools for data manipulation and analysis. Additionally, SAS provides data mining and text mining capabilities to help organizations gain insights from their data. It is popular among data scientists, statisticians, and business analysts. SAS can be used to process large amounts of data and generate complex reports with ease.
Why we use SAS
SAS is an analytics software suite that provides an integrated system for data access, preparation, exploration and analysis. It is used by businesses and organizations in many industries to analyze large volumes of data and gain insights from it. SAS is widely used in the fields of business intelligence, predictive analytics, data mining, and statistical analysis. It provides the tools needed to effectively manage and analyze data, develop meaningful insights, and make well-informed decisions.
SAS – Environment
SAS is an abbreviation for Statistical Analysis System, a software suite developed by SAS Institute. It is used to manage data and perform statistical analysis. SAS is used in many industries, including finance, healthcare, retail, education, and government. The software provides users with a wide range of statistical and data analysis tools, as well as reporting and visualization capabilities. It contains modules for data management, analytics, reporting, and other tasks. The software is available in multiple versions, including Base SAS, Enterprise Guide, and Enterprise Miner.
Download SAS University Edition
SAS University Edition is a free version of SAS software for statistical analysis. It can be downloaded from the SAS website. Once downloaded, it can be installed on any computer running the Windows, Mac, or Linux operating systems. It includes a set of tools for data manipulation, statistical analysis, and predictive modeling. It also includes a set of sample projects to get users started.
SAS – User Interface
SAS has several user interfaces. The most common user interfaces are the SAS Studio, SAS Enterprise Guide, and the SAS programming language. The SAS Studio is a web-based interface that provides access to SAS programming, data management, and analytics capabilities. SAS Enterprise Guide is a graphical user interface that provides easy access to SAS programming and data management. The SAS programming language is a command-based interface that allows users to write and execute SAS programs.
Below is a description of various windows and their usage
• Program Editor: The Program Editor window is used to create and edit SAS programs. It is the main source for entering SAS statements, which are then submitted to the SAS Log window for execution.
• Log Window: The Log window displays the output of SAS programs after it has been submitted for execution. It contains messages, notes, and errors related to the program.
• Output Window: The Output window is used to display the results of SAS procedures. It can show summary statistics, tables, charts, and other visual representations of data.
• Explorer Window: The Explorer window is used to view the SAS environment and its objects. It includes the SAS Library and Data Libraries, as well as catalogs, tables, and views.
• Results Window: The Results window is used to display the results of SAS procedures and queries. It can show summary statistics, tables, charts, and other visual representations of data.
• Explorer Tree: The Explorer Tree window is used to view the structure of the SAS environment. It displays the contents of the SAS Library and Data Libraries, as well as catalogs, tables, and views.
• Debugger Window: The Debugger window is used to debug SAS programs. It allows users to step through the program line by line, view the values of variables, and set breakpoints.
SAS – Program Structure
The structure of a SAS program is as follows:
1. LIBNAME Statement: Assigns a library name to a SAS data library.
2. OPTIONS Statement: Sets global SAS options such as line size, printing, and error messages.
3. DATA Step: A step that reads, creates, or modifies datasets.
4. PROC Step: A step that performs a procedure such as SORT, MEANS, or REPORT.
5. RUN Statement: Executes the program.
6. QUIT Statement: Exits SAS.
SAS – Basic Syntax
The basic syntax of SAS is as follows:
Data Step:
DATA <dataset-name>;
SET <dataset-name(s)>;
[<data-step-options>];
<data-statements>;
RUN;
Procedure Step:
PROC <procedure-name> <options>;
<procedure-statements>;
RUN;
Macro Step:
%<macro-name>(<macro-parameters>);
[<macro-options>];
<macro-steps>;
%MEND;
End Step:
%END;
SAS Statements
1. DATA my_dataset;
2. SET my_data;
3. BY var1 var2;
4. IF FIRST.var1 THEN DO;
5. var3 = 0;
6. END;
7. ELSE DO;
8. var3 + 1;
9. END;
10. RUN;
SAS Variable Names
In SAS, variable names must begin with a letter or an underscore, and can be up to 32 characters long. They can contain only letters, numbers, and underscores, and must not contain any spaces or other special characters.
SAS Data Set
A SAS data set is a collection of data values organized into a specific structure. It is a type of file format used to store, access, and manipulate data in SAS (Statistical Analysis System). A SAS data set is stored as a single file and includes a header, a data descriptor table, and observations. The header contains information about the data set, such as the version of SAS used to create it, the date and time it was created, and the number of observations. The data descriptor table identifies the variables in the data set and their properties, such as data type and length. The observations are the actual data values.
SAS File Extensions
Common SAS file extensions include “.sas”, “.sas7bdat”, “.sas7bcat”, “.sas7bpgm”, “.sas7bvw”, “.sas7bxpt”, and “.sas7cat”.
Comments
In SAS, comments are indicated by an asterisk (*) at the beginning of the statement. SAS ignores anything following the asterisk and can be used to explain the code, describe the data, or provide other helpful information. For example:
* This is a comment
data mydata;
set sashelp.class;
run;
SAS – Data Sets
SAS is a software suite used for data analytics, multivariate analysis, business intelligence, and predictive analytics. It is used in various industries, such as banking, insurance, health care, pharmaceuticals, and finance. SAS provides a wide range of data sets that can be used for data analysis, including public datasets, datasets from organizations, and datasets from the scientific community.
Public datasets include datasets from government sources, such as the US Census Bureau, and other datasets from organizations like the World Bank and the United Nations. Organizations such as SAS datasets include datasets from the pharmaceutical industry, finance, marketing, and other industries. The scientific community provides datasets from the medical field, social sciences, and engineering.
Public datasets are usually available for free and are in the form of files, such as Excel files, CSV files, and text files. Organizations and the scientific community usually require a fee in order to access their datasets. SAS datasets can be used for data analysis in SAS programs, such as SAS Enterprise Guide, SAS Studio, and SAS/IML. The datasets can also be imported into other software applications, such as Microsoft Excel, MATLAB, and R.
Built-In Data Sets
SAS (Statistical Analysis System) is a software suite developed by SAS Institute for advanced analytics, multivariate analysis, business intelligence, data management, and predictive analytics. SAS includes a variety of built-in datasets which can be used for various types of analysis. These datasets are typically used as a baseline for comparison or for testing new algorithms.
Some of the most popular built-in datasets include the Iris Flower dataset, the Boston Housing dataset, and the Bank Marketing dataset. Each of these datasets includes information on various attributes of the data and can be used to analyze a variety of relationships and trends. Other built-in datasets include the Titanic dataset, the Car Evaluation dataset, and the Adult dataset.
SAS also provides access to public datasets from various sources, including the United States Census Bureau, the Centers for Disease Control and Prevention, and the World Bank. These datasets can be used to explore global trends, study population dynamics, and analyze economic and social data.
Finally, SAS also provides access to datasets from open-source repositories such as Kaggle and UCI Machine Learning Repository. These datasets can be used to develop and test machine learning algorithms, perform exploratory data analysis, and build predictive models.
Importing External Data Sets
External data sets are data sets that are sourced from outside a given organization or system. These data sets can be used to supplement or replace an existing data set, to provide additional information, or to gain new insights. External data sets can come from a variety of sources, including public databases, government agencies, or private companies. For example, a company may use an external data set to supplement its own customer data, or to better understand the customer base of a competitor. External data sets can also be used to develop new products or services, to improve customer service, or to gain a competitive advantage.
SAS – Variables
In SAS, a variable is a named variable that holds a specific value or values. Variables are used to store information within a dataset or program. Variables can represent anything from single values to entire datasets. Variables can contain numbers, characters, dates, and other types of data. They are typically created using the DATA and PROC steps of SAS, and can be manipulated using DATA steps and SAS functions. Variables can also be created using the DECLARE statement in a SAS macro. Variables are used to perform calculations, to store intermediate results, and to store output from a program or procedure.
SAS Variable Types
SAS variables can be divided into five main types:
1. Numeric Variables: These are variables that contain numbers, such as integers or real numbers. They can also be used for calculations.
2. Character Variables: These are variables that contain character strings, such as words or phrases. They can be used for sorting and selecting data.
3. Date Variables: These are variables that contain date information, such as month, day, and year. They can be used for scheduling tasks and tracking events.
4. Formatted Variables: These are variables that contain formatted information, such as currency or percentages. They can be used for displaying data in a specific format.
5. Index Variables: These are variables that contain numeric indexes. They can be used to create references to other variables.
Using the SAS Variables
The SAS Variables can be used to analyze and report data. They can also be used to store data and create datasets for further analysis. For example, a researcher can use SAS Variables to store demographic information about a sample of participants and then use that data to create a dataset for further statistical analysis. Additionally, SAS Variables can be used to create reports, such as summary statistics and graphical displays.
SAS – Strings
Strings in SAS are specified by enclosing characters within quotation marks. SAS uses the single quotation mark (‘) to delimit strings. Examples of strings include:
‘This is a string.’
String Functions
1. COMPRESS: removes all specified characters from a character string
2. INDEX: returns the position of a substring within a string
3. SCAN: searches a character string for substrings that are separated by delimiters
4. SUBSTR: returns a portion of a character string
5. TRIM: removes leading and trailing blanks from a character string
6. UPCASE: converts lowercase characters to uppercase
7. LENGTH: returns the length of a character string
8. PROPCASE: converts the first character in each word of a character string to uppercase
9. LOWCASE: converts uppercase characters to lowercase
10. CATX: concatenates character strings into one string
SAS – Arrays
Arrays are a type of SAS data structure that allow users to store multiple values in a single variable. They are beneficial in situations when a user wants to store several values within a single variable. Arrays can be used to store variables of the same type (such as character or numeric) and are accessed using an index value. They are declared by using the ARRAY statement, which includes the name of the array, the index name and type, and the variables that will be stored in the array. Arrays can then be accessed using the array name and the index value. For example, if a user wanted to access the third value stored in the array “labels”, they could use the code “labels[3]”.
Accessing Array Values
To access an array value in SAS, you can use the ARRAY statement in a DATA step to create an array and the DIM function to determine the size of the array. You can then use the ARRAY subscript to reference individual elements in the array. For example, if you had an array called “myArray” with four elements, you could access the third element with myArray[3].
Using the IN operator
The IN operator in SAS is used to compare a value to a list of values or a range of values in a WHERE expression. It returns TRUE if the value is equal to any of the values in the list or range.
For example:
data sales;
set sashelp.shoes;
where region in (‘Europe’,’Asia’,’Africa’);
run;
In this example, the WHERE expression filters the SAS dataset SASHELP.SHOES to only include records where the REGION variable is equal to ‘Europe’, ‘Asia’, or ‘Africa’. The resulting dataset contains only records with these three regions.
Using the OF operator
The OF operator in SAS is used to refer to a specific member within a range of data. It is used to generate a subset of data to be used in calculations, data analysis, or other applications. The OF operator is commonly used in the WHERE statement of a SAS program, as well as in the IF-THEN/ELSE statement. It is also used in PROC SQL to select specific members from a range of data. The syntax for using the OF operator is:
variable-name of list-of-values;
For example, to select all observations from a dataset where the variable “Gender” is equal to “Male” or “Female”, the following statement would be used:
WHERE Gender OF (‘Male’, ‘Female’);
SAS – Numeric Formats
SAS uses a variety of numeric formats to store and display data. Some common SAS numeric formats include:
• BEST – Displays a number in the “best” format, which can be numeric, scientific, or engineering.
• COMMA – Displays a number with a comma for every three digits to the left of the decimal point.
• DOLLAR – Displays a number with a dollar sign ($) and two decimal places.
• N – Displays a number with no decimal places.
• PERCENT – Displays a number as a percentage, with two decimal places.
• SCIENTIFIC – Displays a number in scientific notation with two decimal places.
• Z – Displays a number with a leading zero and two decimal places.
SAS – Operators
SAS operators are used to manipulate values and variables in SAS programming. There are several types of operators available in SAS, such as arithmetic operators, comparison operators, logical operators, assignment operators, and miscellaneous operators.
Arithmetic Operators:
Arithmetic operators are used to perform calculations on numeric values. These operators include addition (+), subtraction (-), multiplication (*), division (/), and exponentiation (**).
Comparison Operators:
Comparison operators are used to compare two values or variables. These operators include equal to (==), greater than (>), less than (<), not equal to (!=), greater than or equal to (>=), and less than or equal to (<=).
Logical Operators:
Logical operators are used to create logical expressions. These operators include AND (&), OR (|), NOT (¬), and XOR (⊕).
Assignment Operators:
Assignment operators are used to assign a value to a variable. These operators include the equal sign (=) and the arrow operator (→).
Miscellaneous Operators:
Miscellaneous operators are used to perform various functions. These operators include parentheses (), brackets [], and the precedence operator %.
SAS – Loops
Loops are a commonly used control structure in SAS. They are used to execute a set of statements repeatedly until a certain condition is met. There are two types of loops in SAS:
1. Do-while loop: This type of loop will execute the set of statements at least once, even if the condition is not met.
Syntax:
Do while (condition);
Statement1;
Statement2;
…
End;
2. Do-until loop: This type of loop will execute the set of statements until the condition is met.
Syntax:
Do until (condition);
Statement1;
Statement2;
…
End;
SAS – Decision Making
SAS is a statistical software package that can be used to support decision making. It can be used to create data visualizations and reports that can assist with the decision making process. It can also be used to analyze data to uncover patterns and relationships, which can be used to inform decisions. Additionally, SAS can be used to identify the best methods for predicting future outcomes and to create models that can be used to make better decisions.
SAS – Functions
SAS functions are built-in routines that perform calculations and transformations on data. They are used to manipulate variables, perform calculations, and to generate specific values. Common SAS functions include mathematical, statistical, and character functions, as well as functions related to dates and times.
Types of functions SAS provides
1. Data Step Functions: These are functions used to manipulate data within the DATA step. Examples include functions for concatenating strings, converting between date formats, and calculating the average of a set of values.
2. Character Functions: These are functions that manipulate character data. Examples include functions for finding the length of a string, changing the case of characters, and finding the position of a character within a string.
3. Numeric Functions: These are functions that manipulate numeric data. Examples include functions for rounding numbers, calculating the absolute value of a number, and finding the maximum or minimum value in a list.
4. Statistical Functions: These are functions used to perform statistical calculations. Examples include functions for calculating the mean, median, and mode of a set of values, as well as functions for calculating regression coefficients.
5. Macro Functions: These are functions used in the macro language. Examples include functions for setting and retrieving macro variables, and other functions that allow you to work with macro variables and macro parameters.
6. SQL Functions: These are functions used in the SQL procedure. Examples include functions for finding the current date and time, and functions for performing common calculations on columns in a table.
7. Graphical Functions: These are functions used to generate graphical output. Examples include functions for creating bar charts, line graphs, and scatter plots.
SAS – Input Methods
1. List Input Method
The List Input Method is a way of entering data into a computer system in the form of a list. This data can be in the form of text, numbers, or images. The list can be created using a variety of different methods, including Named Input Method, Column Input Method, and Formatted Input Method.
2. Named Input Method
Named Input Method is a type of list input method where each item in the list is given a name. This name is then associated with the data that is being entered. This can be done by creating a name or label for each item in the list.
3. Column Input Method
Column Input Method is another type of list input method. This method involves entering data into a table or spreadsheet. Each column in the table or spreadsheet represents a different item in the list. The data is then entered into the appropriate columns.
4. Formatted Input Method
Formatted Input Method is a type of list input method that involves entering data into a form. Each field in the form represents a different item in the list. The data is then entered into the appropriate fields.
SAS – Macros
Macros are a powerful feature of SAS that allow you to create a single piece of code that can be used multiple times with different values. Macros allow you to automate certain tasks, such as creating a report with different variables or running the same analysis multiple times with different values. Macros also allow you to write code that is more efficient and easier to read and maintain. Macros can be used to perform calculations, create graphs and tables, and even write entire programs. They are especially useful for automating repetitive tasks and are an essential part of the SAS programming language.
Macro variables
Macro variables are special variables in SAS that store values or text that can be used and reused throughout a SAS program. They are used to store values that might change over time or need to be used in multiple places. They are also useful for storing values that will be used in calculations. Macro variables are declared with a %LET statement and are referenced with an ampersand (&) followed by the variable name.
Global Macro variable
A global macro variable is a variable that is accessible to all parts of a computer program. It is a type of persistent memory that can be used to store data, instructions, or settings that can be accessed from any part of a program. Global macro variables are often used to store user settings, program configuration, or program variables that need to be accessed from multiple parts of the program.
Local Macro variable
Local macro variables are variables that are defined within a SAS program and are only available within that program. They are not available outside of the program and cannot be accessed or used in other programs. They are typically used to store values that are used multiple times within the same program, such as counters and flags. Local macro variables are assigned a value using the %LET statement.
For example:
%LET counter = 0;
This statement assigns the value of 0 to the local macro variable ‘counter’.
Macro Programs
Macro programs are scripts or programs written for a particular macro language or scripting language. They are typically used to automate tasks or simplify complex processes, such as file operations or data manipulation. Macro programs are often used in software applications to provide a user-friendly interface to a complex set of operations. Macro programs can also be used to create powerful scripts that can be used to automate repetitive tasks or to create complex workflows.
Commonly Used Macros
1. #define – Used to define a pre-processor macro.
2. #include – Used to include a header file in a program.
3. #ifdef – Used to check if a macro is defined or not.
4. #ifndef – Used to check if a macro is undefined or not.
5. #pragma – Used to give special instructions to the compiler.
6. #undef – Used to undefine a macro.
7. #error – Used to generate an error message.
8. #warning – Used to generate a warning message.
9. #line – Used to change the current line number.
10. #elif – Used to specify alternative conditions in if-else-if statement.
Macro %PUT
The %PUT statement is a macro statement in SAS that allows for the writing of text or macro variables to the SAS log. This statement is useful for displaying the value of a macro variable or for debugging the code of a SAS program. It can be used to display the value of a macro variable with the syntax %PUT &variable_name;, which will write the value of the macro variable to the log. Additionally, it can be used to display text strings with the syntax %PUT text_string;. This statement can be used to help debug a SAS program, as it can be used to display the values of macro variables or the values of calculations within the program.
Macro %RETURN%
The %RETURN% macro is a Visual Basic function that allows the user to return a value from a function. It is used to specify which value a function should return to the calling program. The value returned can be any data type, including strings, numbers, arrays, objects, and more.
Macro %END
%START
#include <stdio.h>
int main(void) {
printf(“Hello, World!”);
return 0;
}
%END
SAS – Date & Times
SAS supports a wide range of date and time formats. These formats are useful for representing dates and times in different ways, such as displaying the current date, or setting a specific date range.
The most commonly used SAS date formats are:
• Date9. – mm/dd/yyyy
• DateTime20. – mm/dd/yyyy hh:mm:ss
• Time9. – hh:mm:ss
• DDMMYYw. – day/month/year (weekday)
• DDMMYYDw. – day/month/year (day of week)
• YYMMDDw. – year/month/day (weekday)
• YYMMDDDw. – year/month/day (day of week)
SAS also supports a wide range of date-time informats, which are used to read in date and time values from external data sources. These informats include:
• ANYDTDTM. – reads in any date or time format
• DATETIME. – reads in dates and times in the ‘yyyy-mm-dd hh:mm:ss’ format
• DATETIMEW. – reads in dates and times in the ‘yyyy-mm-dd hh:mm:ss w’ format
• TIMEAMPM. – reads in times in the ‘hh:mm:ss AM/PM’ format
• TIME. – reads in times in the ‘hh:mm:ss’ format
• YEAR. – reads in years in the ‘yyyy’ format
• YEARCUTOFF. – reads in years in the ‘yy’ format
SAS – Read Raw Data
SAS can be used to read raw data into a SAS dataset. This can be done by using the INFILE and INPUT statements. The INFILE statement is used to specify the location and name of the data file. The INPUT statement is used to define the variables and inform SAS how to read the raw data. The data can then be read into a SAS dataset using the DATA statement.
Reading ASCII(Text) Data Set
Reading ASCII data sets is easy with most data analysis software. Most software packages allow you to open the data set as a text file and then load it into a data frame. Once the data is loaded, you can then use the software’s data manipulation and plotting capabilities to analyze the data.
For example, in Python, you can open the ASCII data set using the open() function, then use the readlines() function to read the data into a list. From there, you can use the Pandas library to create a data frame from the list. Then, you can use the various Pandas functions to manipulate the data and generate various plots.
Reading Delimited Data
Delimited data is data that is separated by a specific character or characters, such as a comma, semicolon, or tab. It is commonly used in data tables and spreadsheets. In order to read delimited data, a program or tool must be able to identify the delimiter and properly separate the data. This can usually be done using a parser that is designed to read delimited data. Once the data has been separated, it can then be processed and manipulated as needed.
Reading Excel Data
Excel data can be imported and read using various libraries and frameworks. One such library is the openpyxl library. This library allows users to work with excel files such as loading, reading and writing data. It supports both xlsx and xlsm formats. To read data using openpyxl, we need to create a Workbook object, which is a representation of an Excel file. To access the data in the Excel file, we then use the sheet objects. These objects allow us to access the data in the Excel file and manipulate it as needed. We can also use the dataframe object from the pandas library to read and manipulate Excel data. This object allows us to easily read and write data from an Excel file and also perform operations like filtering and sorting.
Reading Hierarchical Files
Hierarchical files are files that are organized in a hierarchy of directories and sub-directories, each containing files and other directories. Hierarchical files can be read by navigating through the directories and sub-directories in the hierarchy until the desired file is located. Once the file is located, it can then be opened and read using the appropriate software.
SAS – Write Data Sets
To write a data set in SAS, use the proc export statement. This statement is used to export data sets from the SAS system to an external file. The statement has the following syntax:
PROC EXPORT DATA= dataset
OUTFILE= ‘filename’ DBMS= type [REPLACE];
where dataset is the name of the data set you want to write, filename is the name of the external file, type is the type of file (i.e., delimited, excel, etc.), and REPLACE is an optional argument to overwrite existing files.
For example, to export a data set named “students” to an Excel file named “student_data.xls”, the statement would be:
PROC EXPORT DATA= students
OUTFILE= ‘student_data.xls’ DBMS= EXCEL;
PROC EXPORT
The EXPORT procedure in SAS is used to write data from a SAS data set to an external file. It is useful when you need to transfer data from SAS to other programs such as Microsoft Excel, Access, or SPSS for further analysis. The EXPORT procedure supports several different file formats such as CSV, DAT, DBF, and XLS. It also allows you to specify the delimiter to use when writing the data, as well as the encoding of the output file.
SAS – Concatenate Data Sets
In SAS, there are three main ways to concatenate data sets:
1. Using the SET Statement:
The SET statement can be used to concatenate two or more data sets vertically. This is done by specifying the data sets to be concatenated in the SET statement. The syntax is as follows:
SET data-set-1 data-set-2 …;
2. Using the APPEND Statement:
The APPEND statement can be used to concatenate two or more data sets horizontally. This is done by specifying the data sets to be concatenated in the APPEND statement. The syntax is as follows:
APPEND data-set-1 data-set-2 …;
3. Using the CONCATENATE Statement:
The CONCATENATE statement can be used to combine two or more data sets into a single data set. This is done by specifying the data sets to be concatenated in the CONCATENATE statement. The syntax is as follows:
CONCATENATE data-set-1 data-set-2 …;
SAS – Merge Data Sets
The SAS command to merge two data sets is the MERGE statement. This statement is used to combine two or more SAS data sets into one by matching values in a common variable. The syntax for the MERGE statement is as follows:
MERGE dataset-1(IN=data1)
dataset-2(IN=data2);
BY common-variable;
IF data1;
output-statement;
ELSE IF data2;
output-statement;
RUN;
The IN= option is used to indicate which data set the BY variable is coming from. The common-variable is the variable in the two data sets that will be used to match the observations. The output-statement is the statement that will be used to generate an output data set.
Data Merging
Merging data in Sas is a process of combining two or more data sets into one. The data sets can be of the same type or different types. Data merging can be done using the merge statement in SAS. The merge statement allows you to combine two or more data sets based on one or more common variables. The syntax for the merge statement is:
MERGE <data set 1> <data set 2> … <data set n>
USING (<common variable(s)>);
BY <sorting variable(s)>;
The <data set 1> and <data set 2> refer to the data sets that you want to merge. The <common variable(s)> refer to the variables that are common between the two data sets. The <sorting variable(s)> refer to the variables you want to sort the data by.
Once the data sets are merged, you can use the data in other SAS procedures to generate reports, graphs, and other analyses.
Merging only the Matches
SAS can be used to merge two datasets together. To do this, use the MERGE statement. This statement will join two datasets based on one or more common variables. The syntax for the SAS MERGE statement is as follows:
MERGE dataset1 (IN=in1) dataset2 (IN=in2);
BY variable-list;
[IF condition]
[data set options];
For example, if you have two datasets, A and B, and want to merge them based on a variable called ID, you would use the following syntax:
MERGE A (IN=in1) B (IN=in2);
BY ID;
[IF condition]
[data set options];
This statement will join the two datasets together, creating a new dataset with all of the variables from A and B. If there are any matches between the two datasets based on the ID variable, those matches will be included in the new dataset.
SAS – Subsetting Data Sets
SAS is a powerful statistical software that can be used to subset data sets. Subsetting is the process of extracting a subset of the data from a larger data set. This can be done for a variety of reasons, such as focusing on a specific population or creating a sample for exploration. SAS offers a variety of tools to subset data sets, such as the WHERE, IF, and BY statements. The WHERE statement is used to subset the data based on certain conditions, such as a specific value or range of values. The IF statement can be used to create subsets based on a logical expression. The BY statement is used to create subsets based on one or more variables.
Subsetting Variables
Subsetting variables in SAS is done using the KEEP or DROP statement. This statement allows you to specify which variables you want to keep in the dataset or which you want to drop from the dataset. The syntax for these statements is as follows:
KEEP <varlist>;
DROP <varlist>;
Where <varlist> is the list of variables you want to keep (KEEP) or drop (DROP) from the dataset.
SAS – Sort Data Sets
The SAS system can sort data sets in a variety of ways. There are several different methods available depending on the size and complexity of the data set.
1. The SORT procedure can be used to sort one or more data sets. This procedure provides a variety of options for sorting on multiple variables, as well as for selecting observations or variables.
2. The SORTEDBY statement can be used to sort a data set. This statement takes one or more variables to use for sorting the data set.
3. The ORDER BY statement can be used to sort a data set. This statement takes one or more variables to use for sorting the data set.
4. The DATASETS procedure can be used to sort a SAS data set. This procedure allows for sorting on multiple variables, as well as for selecting observations or variables.
5. The SORTSAMPLE procedure can be used to sort a sample of a data set. This procedure allows for sorting on multiple variables, as well as for selecting observations or variables.
Reverse Sorting
To sort in reverse order, you can use the DESCENDING option.
Syntax:
PROC SORT DATA=<dataset> OUT=<dataset>;
BY <variable> DESCENDING;
RUN;
Example:
PROC SORT DATA=Customers OUT=Customers_Sorted;
BY CustomerAge DESCENDING;
RUN;
Sorting Multiple Variables
The following code is used to sort multiple variables in SAS:
PROC SORT DATA=SASdata SET;
BY variable1 variable2 variable3;
RUN;
SAS – Format Data Sets
SAS is a statistical software package that can be used to format data sets. It can be used to sort, merge, and modify data sets. It also provides tools to analyze data and create reports.
SAS can be used to format data sets in a variety of ways. It can be used to create tables, charts, and graphs. It can also be used to summarize data, calculate statistics, and generate reports. Additionally, SAS can be used to filter and clean data, perform data transformations, and create data subsets. It can also be used to join multiple data sets, and to create new data sets from existing ones. Finally, SAS can be used to export data in a variety of formats, such as Excel or CSV.
Using PROC FORMAT
The PROC FORMAT procedure in SAS is used to define custom formats and informats. A format is used to display the value of a variable in a specified way, while an informat is used to read values of a variable in a specified way. PROC FORMAT can be used to create different types of formats and informats, such as character, numeric, date, and time. It can also be used to create user-defined formats, which can be used to control the display of variables.
SAS – SQL
SAS and SQL are both database languages used for managing and querying data. SAS is a statistical programming language used for data analysis and data management. It also has a graphical user interface that allows users to access and query data stored in relational databases. SQL, or Structured Query Language, is a standard language for accessing and manipulating data in relational databases. It is used to perform queries, insert data, update and delete records, create and modify tables, and more. SAS is primarily used in statistical and data analysis, while SQL is used for querying and manipulating data in relational databases.
SQL Create Operation
In SAS, you can create a SQL table using the PROC SQL statement. The syntax is as follows:
PROC SQL;
CREATE TABLE table_name
(column1_name datatype1,
column2_name datatype2,
column3_name datatype3,
…
);
QUIT;
For example, to create a table named “student_data” with columns “student_id” (an integer), “student_name” (a character string), and “student_age” (an integer), the code would be:
PROC SQL;
CREATE TABLE student_data
(student_id INT,
student_name CHAR(20),
student_age INT
);
QUIT;
SQL Read Operation
To perform a SQL read operation in SAS, you can use the PROC SQL step. The syntax is as follows:
PROC SQL;
SELECT *
FROM <table_name>
WHERE <condition>;
QUIT;
SQL SELECT with WHERE Clause
Proc SQL;
Select *
From sas_table
Where condition;
Quit;
SQL UPDATE Operation
The syntax for updating data in SAS is as follows:
UPDATE table-name
SET column1 = value1, column2 = value2, …
WHERE some-condition;
For example,
UPDATE employee
SET salary = salary + 1000
WHERE department = ‘Marketing’;
SQL DELETE Operation
In SAS, you can use the DELETE statement to delete the rows within a table that match the specified conditions. The syntax for the DELETE statement is:
DELETE FROM db.table
WHERE condition;
where db.table is the name of the table and condition is the condition that must be met for a row to be deleted.
SAS – ODS
SAS ODS (Output Delivery System) is a system used to create, manage, and deliver output from SAS procedures, data sets, and graphics. It provides a user interface for creating output in a variety of formats, including HTML, PDF, Microsoft Excel, RTF, and Postscript. It also provides a way to customize the output and save it for future use. ODS is an important tool for producing high-quality reports and other documents from SAS data.
Creating HTML Output
To create HTML output, you can use any text editor to write HTML code, or use a web design program such as Dreamweaver. To get started, you’ll need to open an HTML document and create your basic structure by adding the HTML, head, and body tags. From there, you can add various elements, such as text, images, and links. You’ll also need to include the required elements for a valid HTML document, such as the title, meta tags, and doctype. Once you have your basic structure, you can begin to style the document with CSS, add interactive elements with JavaScript, and more. Finally, once you’ve completed your HTML document, you can save it as an HTML file and view it in your browser.
Creating PDF Output
In SAS, you can create a PDF output by using the ODS PDF statement. This statement is used to create a PDF file as an output of the SAS procedure. The ODS PDF statement enables the user to specify the font, font size, margins, and other features of the PDF output. You can also use the ODS GRAPHICS statement to create graphs and charts in the PDF output.
Creating TRF(Word) Output
WORD
The quick brown fox jumps over the lazy dog.
TRF Output
The/DT quick/JJ brown/JJ fox/NN jumps/VBZ over/IN the/DT lazy/JJ dog/NN ./.
SAS – Simulations
SAS simulations are computer models that are used to study the behavior of a system over time. They are used to make predictions about how a system might respond to certain inputs or conditions. SAS simulations allow for the exploration of numerous scenarios, allowing for the comparison of different outcomes. This makes them useful for analyzing large and complex systems, such as those found in the fields of finance, engineering, and manufacturing.
SAS – Histograms
Histograms are graphs that show the distribution of a dataset. They are commonly used in SAS to visualize the frequency of data points in a set. Histograms allow a user to quickly identify patterns and outliers in a dataset. SAS provides a PROC SGPLOT statement that allows users to easily create histograms from their data. The statement can be used to customize the appearance of the histogram, such as changing the color of the bars, adding labels, and adjusting the x and y axes. Additionally, SAS offers other procedures such as PROC UNIVARIATE and PROC TEMPLATE to create histograms.
Simple Histogram
This graph shows the distribution of a single variable. It is a bar graph that shows the frequency of different values of the variable, typically organized from lowest to highest.
Histogram with Curve Fitting
A histogram with curve fitting is a type of graph that uses a histogram to show the distribution of data, as well as a fitted curve that shows the trend of the data. This type of graph is often used in data analysis to identify patterns and relationships between variables. The curve fitting can be used to make predictions about future data points and to identify the underlying relationships between the data points.
SAS – Bar Charts
SAS is a powerful software package that allows users to create various types of visualizations such as bar charts. Bar charts are a type of graph that can be used to display categorical data. They can be used to compare values across different categories or to compare multiple values within the same category. Bar charts can also be used to show trends over time. SAS provides several different types of bar charts, including stacked bar charts, clustered bar charts, and 3D bar charts. Additionally, users can customize the appearance of their bar charts by changing the colors, fonts, and labels.
Simple Bar chart
A bar chart is a type of chart used when graphing data that displays discrete data points as rectangular bars with varying heights or lengths. The bars can be arranged in different ways, such as horizontally or vertically, and the data they represent can be numerical, categorical, or a combination of both. Bar charts are used to make comparisons between data points, to analyze trends, or to show distributions.
Stacked Bar chart
A stacked bar chart is a type of bar chart that uses bars to represent different categories of data and stacks them on top of each other to show the total value. It is a type of chart that allows viewers to quickly see the differences between categories of data as well as the total value. It is often used to compare data over time or to compare different categories of data.
Clustered Bar chart
A clustered bar chart is a type of graph that displays multiple datasets side-by-side using vertical bars. It is used to compare values across different categories or groups. The bars within each group can be arranged in any order to make comparisons easier. Clustered bar charts are commonly used to show results in surveys, research studies, and other data-driven projects.
SAS – Pie Charts
Pie charts are used to represent data in SAS. Pie charts are used to visualize data in a visually appealing way by dividing a circle into sections that represent the proportion of each category. Pie charts are useful for showing the relative sizes of different categories of data. The sections of the pie chart are proportional to the percentage of the total that each category represents. Pie charts can be created using the SGPLOT procedure in SAS.
Simple Pie Chart
A simple pie chart is a circular graph that divides a whole into different segments based on the relative size of each segment. The segments are usually labeled with descriptive terms or numbers. A pie chart is used to represent the relative size of different categories or parts of a whole. Pie charts are most commonly used to represent population data or the distribution of a particular variable across different categories.
Pie Chart with Data Labels
A pie chart with data labels is a chart that uses “pie slices” to represent the relative size of different categories of data. The data labels are usually numbers or percentages that correspond to the size of each pie slice, allowing the user to quickly identify which category is the largest. This type of chart is useful for comparing the relative sizes of different data sets and showing the breakdown of a single data set.
Grouped Pie Chart
A grouped pie chart is a type of chart that uses multiple pies to represent different groups of data. It is often used to compare different categories of data to each other. Each pie represents a different group of data, and the size of each pie represents the relative amount of data contained in each group. The colors used in the chart also help to differentiate between each group. Grouped pie charts are useful for showing how different groups of data compare to each other, and for highlighting the differences between them.
SAS – Scatter Plots
SAS can create scatter plots to visualize relationships between two variables. To create a scatter plot in SAS, the user can use the SGPLOT procedure. The SGPLOT procedure requires the user to specify the variables to be plotted in the X and Y axes. It also requires the user to specify the dataset to be used. Once these parameters have been specified, the SGPLOT procedure will generate a scatter plot of the data. The user can also specify additional parameters such as the type of markers to be used in the plot, the size of the markers, and the color of the markers.
Simple Scatterplot
A simple scatterplot is a graph that plots two sets of data points on a two-dimensional graph. Each data point is represented by a dot, and the position of the dot is determined by the values of the two variables. The two variables are typically displayed on the x and y axes. A scatterplot can be used to visualize the relationship between two variables, such as to observe a correlation or identify outliers.
Scatterplot with Prediction
A scatterplot with a prediction line is a type of scatterplot that includes a line of best fit or regression line. This line is used to make predictions about how the data might behave in the future. The line is typically calculated using statistical methods, and it is used to predict values of the dependent variable based on the values of the independent variable. The line of best fit is often drawn through the points on the scatterplot, allowing users to visually inspect the relationship between the two variables.
Scatter Matrix
A scatter matrix is a type of graph used to visually display the relationship between two or more variables. It is composed of a series of individual scatter plots, arranged in a grid format, with each plot representing the relationship between two variables. The scatter matrix can help identify patterns and trends in the data, such as clusters and outliers, as well as visualize the correlation between variables.
SAS – Box Plots
Box plots are graphical representations of numeric data. They show five-number summaries, which include the median, minimum, maximum, first quartile, and third quartile. Box plots are useful for exploring the distribution of data and identifying outliers. They can be created in SAS using the PROC SGPLOT statement. This statement allows users to specify the data to be graphed and the features of the graph such as the type of plot, labels, and colors. Additionally, users can customize the graph’s appearance by adding lines, markers, and legend.
Simple Boxplot
A simple boxplot shows the range, median, and quartiles of a given set of data. It is a graphical representation of five-number summary: minimum, first quartile (Q1), median, third quartile (Q3), and maximum. The boxplot is drawn with a box around the middle 50% of the data, and lines extending from the box marking the lower quartile (Q1) and upper quartile (Q3). The median is marked inside the box. Outliers may be marked with a dot or asterisk.
Boxplot in Vertical Panels
A boxplot in vertical panels is typically used to compare the distributions of different groups. It plots the median, first quartile, third quartile, minimum and maximum values of each group in a separate panel. This allows for easy comparison of the distributions of data within each panel, as well as comparison of the distributions across panels. Boxplots in vertical panels can also be used to identify outliers and other patterns in the data.
Boxplot in Horizontal Panels
A boxplot in horizontal panels is a type of graph that displays the data of a given dataset in a visual representation. It is composed of several boxes, each representing different ranges of data, and is displayed in a horizontal manner. Each box contains a boxplot, which is a graphical representation of the data that displays the minimum, maximum, median, and quartiles of the data. The boxplot can also be used to identify outliers and other patterns in the data. This type of graph is typically used to compare different datasets or to identify trends in data.
SAS – Arithmetic Mean
The arithmetic mean, also known as the mean or the average, is a measure of the central tendency of a set of numbers. It is calculated by adding the numbers in the data set and then dividing by the number of items in the data set. In other words, it is the sum of the values divided by the number of values. The arithmetic mean is often used in statistical analysis to measure the central tendency of a data set.
Mean of a Dataset
The mean of a dataset is the average of all the data points in the dataset. It is calculated by adding up all the values in the dataset and dividing by the number of values.
Mean of Select Variables
Mean is a measure of central tendency that is used to describe the average value of a set of data. To calculate the mean of a set of variables, add up all of the values in the set and divide by the number of values in the set. For example, if we were to calculate the mean of the variables 2, 4, 6, and 8, we would add 2 + 4 + 6 + 8 = 20 and divide by 4 (the number of variables) to get an average of 5.
Mean by Class
Mean by class is used to calculate the mean of a set of data points for each class within the dataset. This is helpful for comparing the performance of different classes or groups within the data. For example, a researcher may want to compare the mean grades of two different classes in a school and use mean by class to calculate the average grade of each class. Alternatively, the researcher may want to compare the average salary of two different departments within a company and use mean by class to get the mean salary of each department.
SAS – Standard Deviation
Standard deviation is a measure of the spread of values in a data set. It is calculated by taking the square root of the variance. In statistics, variance is the average of the squared differences from the mean. The standard deviation is the square root of that value. Standard deviation is used to measure the amount of variation or dispersion of a set of data values. It is calculated by taking the square root of the variance.
Using PROC SURVEYMEANS
The PROC SURVEYMEANS procedure can be used to produce descriptive statistics for survey data. It provides estimates of the population parameters for a given survey sample. This procedure can estimate means, standard deviations, frequencies, and other descriptive statistics for survey variables. It can also generate confidence intervals and perform tests of statistical significance. PROC SURVEYMEANS also provides options to adjust for clustering and stratification in the survey design, which can improve the precision of the estimates.
Using PROC MEANS
The PROC MEANS command in SAS is used to calculate descriptive statistics of variables in a dataset. It can also be used to compare the means of two or more groups. The syntax for using PROC MEANS would be as follows:
PROC MEANS DATA=dataset_name;
VAR variable_name;
CLASS class_variable_name;
RUN;
This code would calculate descriptive statistics for the variable specified in the VAR statement, grouped by the class variable specified in the CLASS statement. The results of the PROC MEANS command can then be viewed in the SAS log window.
Using BY option
The BY statement in SAS is used to perform an operation on the observations in a data set by different groups. It can be used with procedures such as the SORT, MEANS, and FREQ procedures. The BY statement takes a list of variables and separates the data set into groups based on the values of the BY variables. The data set is then processed separately within each group. For example, if a data set is sorted using the BY statement, the data set will be sorted into groups based on the values of the BY variables. Within each group the observations will be sorted by the specified variables.
SAS – Frequency Distributions
Frequency distributions in SAS are used to analyze datasets and provide a visual representation of the data. They are created using the PROC FREQ procedure and can be used to compare different variables, calculate percentages and proportions, and summarize data. Frequency distributions are often used to understand the distribution of a given set of data, such as the frequency of responses to a survey.
Multiple Variable Frequency Distribution
A multiple variable frequency distribution is a type of frequency distribution that displays the number of observations in a dataset that fall into a particular category or range based on two or more variables. For example, a multiple variable frequency distribution could be used to show the number of people in a given age range who have a certain level of education. The data would be displayed in a table that has columns for each variable and rows that contain the range of each variable. The table would then show the frequency of people who fall into each combination of ranges.
Single Variable Frequency Distribution
A single variable frequency distribution is a table that lists the values of a single variable and the number of times that value occurs in a dataset. It shows how often a particular value appears in a set of data. For example, a single variable frequency distribution could be used to show the number of students in a classroom who have a certain grade in a particular subject.
Frequency Distribution with Weight
The following SAS code can be used to generate a frequency distribution with weight:
proc freq data=dataset;
tables var1*var2/weight=weightvar;
run;
SAS – Cross Tabulations
SAS is a statistical software package used for data analysis and data management. It can be used to conduct a variety of statistical tests and create cross tabulations. Cross tabulations are used to show the relationship between two or more variables. They are often used to analyze survey data or analyze relationships between different categories of data. SAS can generate cross tabulations by using the PROC TABULATE procedure. This procedure can be used to produce tables that display counts, percentage distributions, row and column totals, mean values, and standard deviations. It also allows for the user to specify different levels of detail for the table.
Cross tabulation of 3 Variables
proc tabulate data=data;
class var1 var2 var3;
table var1, var2, var3;
run;
Cross tabulation of 4 Variables
proc tabulate data=<data set name>;
class <variable1> <variable2> <variable3> <variable4>;
tables <variable1>*<variable2>*<variable3>*<variable4>;
run;
SAS – T Tests
SAS is a statistical software package used for data analysis, manipulation, and exploration. A T Test is a type of analysis used to compare the means of two groups in order to determine whether or not there is a statistically significant difference between them. In SAS, a T Test is performed using the PROC TTEST procedure. The PROC TTEST procedure can be used to compare the means of any two groups and can be used to determine if a difference between the two groups is statistically significant. The output from the PROC TTEST procedure includes the mean and standard deviation of each group, the t-statistic, and the p-value. The t-statistic and p-value are used to determine whether or not the difference between the two groups is statistically significant.
Paired T-test
A paired t-test is a statistical procedure used to compare two sets of related or dependent samples. It is also known as a dependent t-test since one sample relies on the other to determine the outcome. The paired t-test is used to determine if there is a significant difference between the means of two paired samples. It is also commonly used to test the effectiveness of a treatment over time.
Two sample t-test
A two sample t-test is a type of hypothesis test used to compare the means of two independent samples. It is a parametric test, meaning that it makes assumptions about the population from which the samples were drawn. It is used to determine whether there is a statistically significant difference between the means of the two samples. The two sample t-test is used to assess whether the difference between the two samples is greater than what would be expected due to chance alone.
SAS – Correlation Analysis
Correlation analysis is a statistical technique used to examine the strength and direction of the relationship between two variables. SAS is a statistical software package used for data analysis, data management, and graphics. It is commonly used for correlation analysis. SAS provides a variety of procedures for computing correlation coefficients, such as PROC CORR, which can be used to calculate Pearson, Spearman, and Kendall correlations. These procedures can also be used to assess the significance of the correlation coefficient and display graphical representations of the data. Additionally, SAS also provides methods for testing linear and non-linear correlations.
Correlation Matrix
Correlation matrix is a type of matrix that measures the linear relationship between two variables. The correlation matrix in SAS can be generated using the PROC CORR procedure. This procedure computes Pearson, Spearman, and Kendall correlation coefficients for two or more variables. It also produces descriptive statistics, such as the mean and standard deviation, for each variable. The PROC CORR procedure is used to generate a correlation matrix for a single data set or for multiple data sets.
Correlation Between All Variables
In SAS, correlation can be calculated using the PROC CORR procedure. The syntax for this procedure is as follows:
PROC CORR DATA= <data set>;
VAR <variable list>;
RUN;
Here, the <data set> is the name of the data set that contains the variables being analyzed and the <variable list> is a list of the names of the variables being analyzed. The output of this procedure will be a correlation matrix showing the correlation coefficient (r-value) between all the variables in the data set.
SAS – Linear Regression
SAS is a statistical software package used to perform a variety of data analysis tasks, including linear regression. Linear regression is a statistical technique used to identify relationships between two or more variables in a dataset. With SAS, users can fit linear regression models, interpret the results of the model, and assess the overall quality of the fit. The SAS procedures used for linear regression include PROC REG, PROC GLM, and PROC MIXED. These procedures allow users to specify the type of regression model and the variables to be included in the model. They also allow users to specify the distribution of the residuals in the model and to assess the overall quality of the fit.
SAS – Bland Altman Analysis
The Bland-Altman analysis is a statistical method that can be used to compare two sets of data. It is often used in medical research to compare the accuracy of various diagnostic tests. The method relies on plotting the difference between the two sets of data against the mean of the two sets of data. The analysis can help identify any systematic differences between the two sets of data and can also be used to assess agreement between the two sets of data. It can be used in SAS by using the PROC CORR statement in SAS to calculate the correlation between the two sets of data and then using the PLOT statement to create a Bland-Altman plot.
Enhanced Model
SAS is a powerful analytics software that can be used to create complex models for a variety of purposes. For example, SAS can be used to create enhanced models, which are models that incorporate additional features or data sources to improve the accuracy of the prediction or classification. Enhanced models can be used to predict customer behavior, identify potential fraud, or even identify new opportunities. SAS can also be used to build models that use machine learning, artificial intelligence, and other advanced techniques to improve the accuracy of the predictive models.
SAS – Chi Square
The Chi Square (χ2) test is a statistical test used to determine if there is a significant difference between the expected frequency and the observed frequency of certain events. It is used to test relationships between categorical variables. This test can be used to measure the correlation between two variables, or the difference between two groups of data.
Two Way chi-square
A two-way chi-square test is a statistical test used to assess the relationship between two categorical variables. It is used to determine if there is a statistically significant association between the two variables. The test is used to determine if the two variables are independent or dependent, and if the observed frequencies in the two variables are significantly different from those that would be expected if the variables were independent. The two-way chi-square test is also known as the contingency table chi-square test.
SAS – Fishers Exact Tests
The Fisher’s Exact test is a non-parametric statistical test used to determine the level of association between two categorical variables. It is used to test the hypothesis that the two variables are independent of each other. The test is often used when the sample size is small and there are only a few possible outcomes. It is most commonly used in medical research, where it is used to compare the effects of different treatments on a population. The SAS program provides a built-in function for performing the Fisher’s Exact test. This function allows the user to specify the number of rows and columns in the contingency table and the significance level. The output of the test includes the p-value, which is the probability that the observed results could have occurred by chance.
Applying Fisher Exact Test
In SAS, the FISHER procedure can be used to carry out Fisher Exact Test to determine if two categorical variables are independent.
The syntax for the FISHER procedure is
PROC FISHER data=dataset;
TABLE var1 * var2;
RUN;
Where “dataset” is the name of the SAS dataset containing the data, “var1” is the name of the first categorical variable, and “var2” is the name of the second categorical variable.
The output of the FISHER procedure will be a table with the observed counts and the expected counts for each of the cells in the two-way table, as well as the p-value of the test.
For example, if the dataset contains the variables “Gender” and “Eye Color”, the following code can be used to perform the Fisher Exact Test:
PROC FISHER data=dataset;
TABLE gender * eye_color;
RUN;
The output of this procedure will be a table showing the observed and expected counts of each combination of Gender and Eye Color, as well as the p-value of the Fisher Exact Test.
SAS – Repeated Measure Analysis
SAS is a powerful statistical software that can be used to perform a variety of statistical analysis. One of the most common uses of SAS is to perform repeated measure analysis. Repeated measure analysis is used to assess the differences between multiple measurements taken on the same subjects over time. This type of analysis can be used to compare the effects of different treatments or interventions, or to assess changes in a given response over time.
SAS can be used to perform repeated measure analysis in a number of ways. The most common approach is to use the PROC GLM procedure. This procedure allows the user to specify a model with repeated measures as the response variable and then fit the model using maximum likelihood estimation. The user can also specify different types of covariates such as time, treatment, or other factors. The output of the PROC GLM procedure can then be used to examine the effects of the different factors on the response variable.
Other approaches to repeated measure analysis using SAS include the use of the PROC MIXED procedure and the use of generalized estimating equations (GEE). The PROC MIXED procedure allows the user to specify a mixed model with repeated measures as the response variable and then fit the model using maximum likelihood estimation. The output of the PROC MIXED procedure provides information about the effects of the different factors on the response variable.
The use of generalized estimating equations (GEE) is an alternative approach to repeated measure analysis. GEE is a type of regression model that allows the user to account for the correlation between repeated measures. The output of the GEE model can then be used to examine the effects of the different factors on the response variable.
In summary, SAS can be used to perform a variety of analyses including repeated measure analysis. The most common approach is to use the PROC GLM procedure, but other approaches such as the use of the PROC MIXED procedure and the use of generalized estimating equations (GEE) are also available.
SAS – One Way Anova
One way ANOVA is a statistical method used to compare the means of three or more independent (unrelated) groups. It tests the null hypothesis that the means of the groups are equal and the alternative hypothesis that at least one of the means is different from the rest. To perform a one way ANOVA, the data for each group must be independent and normally distributed. The ANOVA test can then be used to determine if there is a statistically significant difference between the groups.
Applying ANOVA
ANOVA (Analysis of Variance) is a statistical technique used to compare the means of two or more groups. It is used to determine if there are statistically significant differences between the means of the groups. ANOVA is used to compare the means of different groups in order to determine whether the differences in the means are statistically significant. ANOVA can be used to compare the means of different groups of data, such as different ages, genders, or regions. ANOVA can also be used to compare the means of different treatments or conditions within a single group of data, such as different doses of a drug or different levels of a factor.
Applying ANOVA with MEANS
The ANOVA test with MEANS is used to compare the means of two or more variables to see if there are statistically significant differences between them. It is a type of analysis of variance (ANOVA) that uses the means of the groups as the main measure of comparison. It is used to determine if the means of two or more groups are significantly different from each other. It is also used to determine if there are any differences between the means of two or more groups when the population variance is unknown.
SAS – Hypothesis Testing
Hypothesis testing is a type of data analysis used in statistics to make inferences about a population. It is used to determine whether observed data is statistically significant, or if it is likely due to chance. In SAS, hypothesis testing is conducted using the PROC GLM and PROC TTEST procedures. The GLM procedure is used to test general linear models, while the TTEST procedure is used to calculate t-tests and z-tests. Both procedures allow the user to specify the type of test to be performed and the null and alternative hypotheses. The results are then compared to determine if the observed data is statistically significant.
1. State the null and alternative hypotheses.
2. Specify the significance level.
3. Select the appropriate test statistic and test method.
4. Calculate the test statistic and its associated probability value.
5. Make a decision regarding the null hypothesis.
6. Interpret the results.