This document provides an overview of basic SAS commands for inputting and analyzing data. It describes the SAS data step for inputting and manipulating data to create SAS datasets. It then summarizes commonly used SAS statistical procedures like ANOVA, CHART, CORR, and REG for analyzing SAS dataset. It includes syntax examples and explanations of options for these procedures.
Understanding SAS Data Step Processingguest2160992
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
The document summarizes the three phases of the SAS data step process: compilation, execution, and output. During compilation, SAS checks syntax and identifies variable types. It creates an input buffer and program data vector (PDV). In execution, SAS reads each observation into the PDV and executes code. For output, it writes observations from the PDV to the output data set. The document provides examples to illustrate the processing and highlights what occurs in each phase.
Data set options allow features during dataset processing and control variables, observations, security, and attributes. They are specified in parentheses after a SAS data set name and include options like DROP, KEEP, RENAME, FIRSTOBS, and LABEL. Data set options apply to input datasets before programming statements and to output datasets after statements are processed.
- PROC TABULATE creates customized tables that display descriptive statistics from SAS data. It allows users to classify and analyze variables to produce one, two, or three-dimensional tables.
- The procedure uses CLASS, VAR, and TABLE statements to specify how variables should be classified, analyzed, and presented in the table. Common statistics like frequency, mean, sum can be calculated and displayed.
- Two-way and multi-dimensional tables can be created to examine relationships between multiple variables. Formatting and summary options provide flexibility in customizing table output.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learn SAS programming, SAS slides, SAS tutorials, SAS certification, SAS Sample Code, SAS Macro examples,SAS video tutorials, SAS ebooks, SAS tutorials, SAS tips and Techniques, Base SAS and Advanced SAS certification, SAS interview Questions and answers, Proc SQL, SAS syntax, Advanced SAS, Quick links, SAS Documentation, SAS Addin to Microsoft office, Oracle, ODS HTML, ODS< Clinical trials, Financial Industry, Q & A, SAS Resumes, SAS Blogs, http://sastechies.blogspot.com, http://www.sastechies.com
A Step-By-Step Introduction to SAS Report ProcedureYesAnalytics
The presentation of data is an essential part of every analytics project and there are number of tools within SAS that allows to create a large variety of charts, reports, and data summaries.
PROC REPORT is a particularly powerful and valuable procedure that can be used in this process. It can be used to both summarize and display data, and is highly customizable and highly flexible. It combines features of the PRINT, MEANS, and TABULATE procedures with features of the DATA step.
Here is a step by step introduction to Report Procedure which walks through the PROC REPORT statement and a few of its key options.
This document provides a summary of SAS language elements, procedures, functions, formats, and the macro language. It includes brief descriptions of commonly used statements, such as DATA, SET, IF/THEN, FORMAT, and PROC, as well as summaries of various procedures like FREQ, MEANS, REPORT and SORT. It also outlines important macro language elements such as %DO, %LET, and macro quoting functions.
Understanding SAS Data Step Processingguest2160992
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
The document summarizes the three phases of the SAS data step process: compilation, execution, and output. During compilation, SAS checks syntax and identifies variable types. It creates an input buffer and program data vector (PDV). In execution, SAS reads each observation into the PDV and executes code. For output, it writes observations from the PDV to the output data set. The document provides examples to illustrate the processing and highlights what occurs in each phase.
Data set options allow features during dataset processing and control variables, observations, security, and attributes. They are specified in parentheses after a SAS data set name and include options like DROP, KEEP, RENAME, FIRSTOBS, and LABEL. Data set options apply to input datasets before programming statements and to output datasets after statements are processed.
- PROC TABULATE creates customized tables that display descriptive statistics from SAS data. It allows users to classify and analyze variables to produce one, two, or three-dimensional tables.
- The procedure uses CLASS, VAR, and TABLE statements to specify how variables should be classified, analyzed, and presented in the table. Common statistics like frequency, mean, sum can be calculated and displayed.
- Two-way and multi-dimensional tables can be created to examine relationships between multiple variables. Formatting and summary options provide flexibility in customizing table output.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
Learn SAS programming, SAS slides, SAS tutorials, SAS certification, SAS Sample Code, SAS Macro examples,SAS video tutorials, SAS ebooks, SAS tutorials, SAS tips and Techniques, Base SAS and Advanced SAS certification, SAS interview Questions and answers, Proc SQL, SAS syntax, Advanced SAS, Quick links, SAS Documentation, SAS Addin to Microsoft office, Oracle, ODS HTML, ODS< Clinical trials, Financial Industry, Q & A, SAS Resumes, SAS Blogs, http://sastechies.blogspot.com, http://www.sastechies.com
A Step-By-Step Introduction to SAS Report ProcedureYesAnalytics
The presentation of data is an essential part of every analytics project and there are number of tools within SAS that allows to create a large variety of charts, reports, and data summaries.
PROC REPORT is a particularly powerful and valuable procedure that can be used in this process. It can be used to both summarize and display data, and is highly customizable and highly flexible. It combines features of the PRINT, MEANS, and TABULATE procedures with features of the DATA step.
Here is a step by step introduction to Report Procedure which walks through the PROC REPORT statement and a few of its key options.
This document provides a summary of SAS language elements, procedures, functions, formats, and the macro language. It includes brief descriptions of commonly used statements, such as DATA, SET, IF/THEN, FORMAT, and PROC, as well as summaries of various procedures like FREQ, MEANS, REPORT and SORT. It also outlines important macro language elements such as %DO, %LET, and macro quoting functions.
This document provides an overview of SAS programming concepts and techniques for working with data. It discusses reading raw data using DATA steps and PROC IMPORT, selecting and transforming data, merging datasets, handling missing values, and working with dates. Functions for character manipulation, arithmetic, ranking and summarizing data are also explained. Overall, the document serves as a helpful cheat sheet for common SAS programming tasks.
This document provides an overview of using PROC SQL in SAS Enterprise Guide 4.3. It discusses the basics of SAS Enterprise Guide 4.3, the typical SQL statement structure including common clauses, best practices for order of operations and joins, and how to use macro variables with PROC SQL. The purpose is to provide guidance for both beginners and advanced users on effectively working with PROC SQL.
This document discusses three SAS statements - SET, MERGE, and UPDATE - that are used to combine existing SAS datasets. The SET statement reads observations from one or more SAS datasets and writes them to a new dataset. The MERGE statement brings two SAS datasets together. The UPDATE statement is used to change an existing SAS dataset. The document also covers using the LIBNAME statement to define SAS data libraries and the BY statement to control the order of data integration. The goal is to provide information to allow attendees to permanently store and combine their SAS data.
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1. The document provides information on database concepts like the system development life cycle, data modeling, relational database management systems, and creating and managing database tables in Oracle.
2. It discusses how to create tables, add, modify and delete columns, add comments, define constraints, create views, and perform data manipulation operations like insert, update, delete in Oracle.
3. Examples are provided for SQL statements like CREATE TABLE, ALTER TABLE, DROP TABLE, CREATE VIEW, INSERT, UPDATE, DELETE.
The document discusses data manipulation language (DML) statements in Oracle such as INSERT, UPDATE, DELETE, and MERGE. It provides examples of using each statement to add, modify, remove, and merge rows in database tables. It also covers transactions, locking, and maintaining data integrity with constraints when using DML statements.
The document discusses how to use Oracle's Data Definition Language (DDL) to define database objects like tables, views, indexes, and sequences. It provides the syntax for creating these objects using commands like CREATE, ALTER, and DROP. Examples are given for creating a table with various constraints, altering a table, creating views with subqueries, and using sequences to generate primary keys. The key DDL commands, data types, naming conventions, constraints, and how to populate and modify tables are summarized.
This document discusses how to create, manage, and modify database tables in Oracle. The key points covered include how to create tables with column definitions and datatypes, alter tables by adding, modifying or dropping columns, rename and truncate tables, and add comments to tables. Operations like create, alter, drop, and truncate allow managing the structure of tables, while comments provide descriptive information.
This document provides instructions on using Data Definition Language (DDL) and Data Manipulation Language (DML) commands in a relational database management system (RDBMS). It describes the syntax and use of common DDL commands like CREATE, ALTER, DROP, and TRUNCATE to define and modify database tables. It also explains DML commands like INSERT, UPDATE, DELETE, and SELECT to manipulate data by adding, changing, removing, and querying records. Examples are given of creating tables, adding/removing columns, inserting data, updating records, deleting rows, and running queries using these SQL commands.
This document provides an introduction to using Structured Query Language (SQL) with Teradata databases. It describes SQL and its three categories of statements: Data Definition Language (DDL), Data Manipulation Language (DML), and Data Control Language (DCL). It also introduces the Basic Teradata Query (BTEQ) tool for submitting SQL statements to a Teradata database interactively and covers setting session parameters in BTEQ like transaction semantics and the SQL Flagger. Key topics include the SELECT statement, ORDER BY and DISTINCT clauses, naming conventions for database objects, and setting the default database in a session.
1) SQL is a standard language for accessing and manipulating databases. It allows users to define, manipulate, and control access to data in a database.
2) SQL was first introduced in a 1970 research paper and later developed into a language. It became a standard and is now supported by most major databases.
3) SQL allows users to define tables, perform data manipulation like inserts and updates, run queries to retrieve data, and control transactions in the database. It provides powerful and easy to use commands to work with relational databases.
This document discusses a database management systems (DBMS) practical assignment on data manipulation language (DML) commands. It defines DML commands as SQL statements like select, insert, update, and delete. It provides the syntax and examples of each DML command, including selecting data from a table, inserting new rows, updating column values, and deleting rows. The conclusion states that DML commands were successfully executed.
SQL is a standard language for storing, manipulating and retrieving data in databases. It allows users to access and manipulate data in databases. Some key functions of SQL include executing queries against a database, retrieving data from a database, inserting, updating and deleting records in a database, and creating, altering and dropping database objects like tables.
This VB tool can extract titles/footnotes from Table Shell and save as a TXT file which is friendly to editing. What you see is what you get.
The user will automatically generate a report of the TFL production/validation status.
Also it can select the package you want and convert LST file to Word format.
The document describes various data definition language (DDL) and data manipulation language (DML) commands in MySQL. Some key commands include using CREATE to add new databases, tables, indexes, and constraints. ALTER is used to modify existing database objects. DROP removes databases, tables, columns or indexes. DML commands like SELECT are used to query data, WHERE filters rows, JOIN combines tables, and INSERT, UPDATE, DELETE modify data. COUNT, SUM, DISTINCT and other functions can be used to aggregate or transform result sets.
This document provides an overview of MySQL and relational databases. It defines key concepts like databases, tables, rows, columns, primary keys, foreign keys and indexes. It explains how to set up a MySQL database using scripts or a GUI tool. It also covers basic SQL commands like SELECT, INSERT, UPDATE and DELETE and provides examples of their usage. Finally, it introduces the Squirrel SQL Client, an open source Java GUI for interacting with MySQL databases.
This document provides an overview of SQLite, including:
- SQLite is a C library that implements a SQL database engine that can be embedded into an application rather than running as a separate process.
- It is widely used as the database engine in browsers, operating systems, and other embedded systems due to its small size and simplicity.
- The document discusses SQLite's design, syntax, built-in functions like COUNT, MAX, MIN, and SUM, and SQL statements like CREATE TABLE, INSERT, SELECT, UPDATE, DELETE, and VACUUM.
The document describes the characteristics of taiga forests including cold climates with low precipitation rates and widely spaced coniferous trees. Animals that inhabit taiga forests include Arctic fox, Arctic wolf, bald eagles, Canada geese, mosquitoes, and ants. Taiga forests are located in certain regions of North America, Europe, and Asia such as northern California, parts of Germany, and southern Korea.
The document summarizes key components of a coniferous forest biome. It describes the abiotic components like redwood and sequoia trees and biotic components such as bears, wolves, and birds. It outlines producers like mushrooms and fir trees. Primary consumers are identified as mice, birds, and deer, with secondary consumers including foxes, owls, and skunks. Bears are listed as predators. Threatened species mentioned are deer and eagles, while endangered species named are grizzly bears, rabbits, and spotted owls. The document also notes some extinct species and interesting facts about the coniferous forest biome.
This document provides an overview of SAS programming concepts and techniques for working with data. It discusses reading raw data using DATA steps and PROC IMPORT, selecting and transforming data, merging datasets, handling missing values, and working with dates. Functions for character manipulation, arithmetic, ranking and summarizing data are also explained. Overall, the document serves as a helpful cheat sheet for common SAS programming tasks.
This document provides an overview of using PROC SQL in SAS Enterprise Guide 4.3. It discusses the basics of SAS Enterprise Guide 4.3, the typical SQL statement structure including common clauses, best practices for order of operations and joins, and how to use macro variables with PROC SQL. The purpose is to provide guidance for both beginners and advanced users on effectively working with PROC SQL.
This document discusses three SAS statements - SET, MERGE, and UPDATE - that are used to combine existing SAS datasets. The SET statement reads observations from one or more SAS datasets and writes them to a new dataset. The MERGE statement brings two SAS datasets together. The UPDATE statement is used to change an existing SAS dataset. The document also covers using the LIBNAME statement to define SAS data libraries and the BY statement to control the order of data integration. The goal is to provide information to allow attendees to permanently store and combine their SAS data.
Sas classes in mumbai
best Sas classes in mumbai with job assistance.
our features are:
expert guidance by it industry professionals
lowest fees of 5000
practical exposure to handle projects
well equiped lab
after course resume writing guidance
1. The document provides information on database concepts like the system development life cycle, data modeling, relational database management systems, and creating and managing database tables in Oracle.
2. It discusses how to create tables, add, modify and delete columns, add comments, define constraints, create views, and perform data manipulation operations like insert, update, delete in Oracle.
3. Examples are provided for SQL statements like CREATE TABLE, ALTER TABLE, DROP TABLE, CREATE VIEW, INSERT, UPDATE, DELETE.
The document discusses data manipulation language (DML) statements in Oracle such as INSERT, UPDATE, DELETE, and MERGE. It provides examples of using each statement to add, modify, remove, and merge rows in database tables. It also covers transactions, locking, and maintaining data integrity with constraints when using DML statements.
The document discusses how to use Oracle's Data Definition Language (DDL) to define database objects like tables, views, indexes, and sequences. It provides the syntax for creating these objects using commands like CREATE, ALTER, and DROP. Examples are given for creating a table with various constraints, altering a table, creating views with subqueries, and using sequences to generate primary keys. The key DDL commands, data types, naming conventions, constraints, and how to populate and modify tables are summarized.
This document discusses how to create, manage, and modify database tables in Oracle. The key points covered include how to create tables with column definitions and datatypes, alter tables by adding, modifying or dropping columns, rename and truncate tables, and add comments to tables. Operations like create, alter, drop, and truncate allow managing the structure of tables, while comments provide descriptive information.
This document provides instructions on using Data Definition Language (DDL) and Data Manipulation Language (DML) commands in a relational database management system (RDBMS). It describes the syntax and use of common DDL commands like CREATE, ALTER, DROP, and TRUNCATE to define and modify database tables. It also explains DML commands like INSERT, UPDATE, DELETE, and SELECT to manipulate data by adding, changing, removing, and querying records. Examples are given of creating tables, adding/removing columns, inserting data, updating records, deleting rows, and running queries using these SQL commands.
This document provides an introduction to using Structured Query Language (SQL) with Teradata databases. It describes SQL and its three categories of statements: Data Definition Language (DDL), Data Manipulation Language (DML), and Data Control Language (DCL). It also introduces the Basic Teradata Query (BTEQ) tool for submitting SQL statements to a Teradata database interactively and covers setting session parameters in BTEQ like transaction semantics and the SQL Flagger. Key topics include the SELECT statement, ORDER BY and DISTINCT clauses, naming conventions for database objects, and setting the default database in a session.
1) SQL is a standard language for accessing and manipulating databases. It allows users to define, manipulate, and control access to data in a database.
2) SQL was first introduced in a 1970 research paper and later developed into a language. It became a standard and is now supported by most major databases.
3) SQL allows users to define tables, perform data manipulation like inserts and updates, run queries to retrieve data, and control transactions in the database. It provides powerful and easy to use commands to work with relational databases.
This document discusses a database management systems (DBMS) practical assignment on data manipulation language (DML) commands. It defines DML commands as SQL statements like select, insert, update, and delete. It provides the syntax and examples of each DML command, including selecting data from a table, inserting new rows, updating column values, and deleting rows. The conclusion states that DML commands were successfully executed.
SQL is a standard language for storing, manipulating and retrieving data in databases. It allows users to access and manipulate data in databases. Some key functions of SQL include executing queries against a database, retrieving data from a database, inserting, updating and deleting records in a database, and creating, altering and dropping database objects like tables.
This VB tool can extract titles/footnotes from Table Shell and save as a TXT file which is friendly to editing. What you see is what you get.
The user will automatically generate a report of the TFL production/validation status.
Also it can select the package you want and convert LST file to Word format.
The document describes various data definition language (DDL) and data manipulation language (DML) commands in MySQL. Some key commands include using CREATE to add new databases, tables, indexes, and constraints. ALTER is used to modify existing database objects. DROP removes databases, tables, columns or indexes. DML commands like SELECT are used to query data, WHERE filters rows, JOIN combines tables, and INSERT, UPDATE, DELETE modify data. COUNT, SUM, DISTINCT and other functions can be used to aggregate or transform result sets.
This document provides an overview of MySQL and relational databases. It defines key concepts like databases, tables, rows, columns, primary keys, foreign keys and indexes. It explains how to set up a MySQL database using scripts or a GUI tool. It also covers basic SQL commands like SELECT, INSERT, UPDATE and DELETE and provides examples of their usage. Finally, it introduces the Squirrel SQL Client, an open source Java GUI for interacting with MySQL databases.
This document provides an overview of SQLite, including:
- SQLite is a C library that implements a SQL database engine that can be embedded into an application rather than running as a separate process.
- It is widely used as the database engine in browsers, operating systems, and other embedded systems due to its small size and simplicity.
- The document discusses SQLite's design, syntax, built-in functions like COUNT, MAX, MIN, and SUM, and SQL statements like CREATE TABLE, INSERT, SELECT, UPDATE, DELETE, and VACUUM.
The document describes the characteristics of taiga forests including cold climates with low precipitation rates and widely spaced coniferous trees. Animals that inhabit taiga forests include Arctic fox, Arctic wolf, bald eagles, Canada geese, mosquitoes, and ants. Taiga forests are located in certain regions of North America, Europe, and Asia such as northern California, parts of Germany, and southern Korea.
The document summarizes key components of a coniferous forest biome. It describes the abiotic components like redwood and sequoia trees and biotic components such as bears, wolves, and birds. It outlines producers like mushrooms and fir trees. Primary consumers are identified as mice, birds, and deer, with secondary consumers including foxes, owls, and skunks. Bears are listed as predators. Threatened species mentioned are deer and eagles, while endangered species named are grizzly bears, rabbits, and spotted owls. The document also notes some extinct species and interesting facts about the coniferous forest biome.
Coniferous forests are characterized by long, cold winters with abundant snowfall. Coniferous trees such as pines, spruces, and firs predominate in these forests. Common mammal inhabitants include lynx, moose, deer, and bears. Coniferous forests stretch across northern regions of Europe, Asia, and North America between temperate grasslands and the polar tundra.
The Canadian coniferous forest biome has average temperatures between -40 to 68 degrees Fahrenheit, with 5-6 months of cold, humid winters. Precipitation is 300-900 mm annually. Abiotic factors include mountains, ponds, dirt and snow, while biotic factors are the same plus coniferous trees. The biome faces problems of deforestation from human development and pollution from cabin emissions degrading air quality.
Science Serving Society: Hot Topics in Urban Forestry ResearchArbor Day Foundation
The document summarizes hot topics in urban forestry research from the U.S. Forest Service's National Conference on Partners in Community Forestry. The Forest Service conducts research on urban forestry and environmental issues through scientists located around the U.S. who study topics like urban tree canopy tools, sustainable cities, urban resilience, ecosystem services and disservices, and environmental justice. The program emphasizes long-term, place-based, and collaborative research to inform science-based decision-making around livable and sustainable cities.
This document discusses biodiversity in coniferous forests. It defines biodiversity as the variety of species and ecosystems in a particular area. Coniferous forests have diverse plant species like spruce, hemlock, pine and fir trees that are adapted to cold climates. They also support a variety of animal species, though some migrate seasonally. Logging and deforestation threaten biodiversity in coniferous forests by destroying habitats and forcing animals out of their homes, endangering species like the Northern Spotted Owl. The document stresses the importance of protecting coniferous forest biodiversity.
The coniferous forest biome is located in the Northern Hemisphere, stretching across North America and Eurasia. It has long, cold winters averaging below -15°C and short summers with a growing season of only 3 months. Trees are adapted to the climate with needle-like leaves, tall narrow forms, and thick bark. Logging and deforestation are major threats, destroying habitats and contributing to climate change.
The taiga biome has harsh winters that last up to six months with sub-freezing temperatures and moderate precipitation. Plant life is limited mostly to lichens, mosses, and evergreen coniferous trees that grow closely together for protection. Animals have adapted to the cold through migration, hibernation, or other means to cope with food scarcity. Global warming threatens the taiga biome by potentially shifting climatic zones hundreds of kilometers north and reducing the biome's coverage by 50-90% over the next 50 years.
Coniferous trees dominate coniferous forests. The wildlife is diverse and adapts to long, harsh winters and short, mild summers. Coniferous forests in Mexico are located in mountainous regions in the north and center of the country. Primary producers include mosses, grasses, ferns and coniferous trees, which support a variety of herbivores, omnivores, carnivores, and decomposers in the forest's food chain.
Coniferous forests are dominated by cone-bearing evergreen trees adapted to cold climates. They have reduced leaf surfaces and remain green year-round to maximize growth during short northern seasons. Species like pines, spruces, and firs are common. Porcupines, squirrels, and birds consume their seeds. Owls and weasels control rodent populations. Moose are the largest browsing herbivores. Fires, though once suppressed, play a natural role in renewing coniferous forests.
This document provides an overview of stresses affecting deciduous trees and discusses specific issues for common genera including Acer, Betula, Fraxinus, Malus, Prunus, Quercus, Tilia, and Ulmus. It defines different types of stress as acute vs chronic and biotic vs abiotic. For each genus it lists example insect pests and diseases as well as notes on species selection and issues. The document emphasizes developing integrated pest management strategies to reduce plant stresses.
1) Living things need energy, which they obtain through food webs. Producers like plants get energy from sunlight, consumers eat producers or other animals, and decomposers break down dead organisms.
2) Food webs show the feeding relationships between organisms in an ecosystem, with energy transferring from one level to the next.
3) Energy pyramids illustrate how the amount of available energy decreases at each trophic level, as more is used for life processes and less is stored. Restoring gray wolves to Yellowstone helped balance populations by controlling elk numbers.
The document discusses tree identification through leaf observation. It defines key terms used in tree taxonomy and morphology. It explains how to distinguish between conifers and deciduous trees based on their leaf characteristics such as type, arrangement, edge and texture. The document also outlines other tree features useful for identification like bark, twigs, flowers, fruits and cones.
Coniferous trees, also known as conifers, are trees that produce cones and have long, needle-like leaves. Conifers grow upward in a triangular shape adapted for cold climates. The cones are important because they produce and shelter the tree's seeds. Most conifers have both male and female cones, with the female cones containing eggs that develop into seeds when pollinated. The needles are an adaptation that help the tree retain water in cold, snowy climates. Conifers are ecologically important as they provide shelter, lumber, and produce oxygen.
Forests provide oxygen and natural beauty, but some people illegally cut down trees, threatening forests. While controlled burns and deforestation can sometimes benefit forests, irresponsible activities like throwing cigarettes and illegal logging reduce forests and disrupt habitats. To protect forests for future generations, people must obtain permission before cutting trees, plant new trees, and prevent fires started by littering.
This document provides information about different types of forests and reforestation. It discusses 7 main types of forests: 1) Temperate needleleaf, 2) Temperate broadleaf and mixed, 3) Tropical moist, 4) Tropical dry, 5) Sparse trees and parkland, 6) Tropical forest types. It then provides more details on each forest type. The document also discusses reforestation, including management techniques, using reforestation for timber harvesting, mitigating climate change, incentives for reforestation, and examples of reforestation projects.
This document introduces the concepts of ecology, ecosystems, biotic and abiotic factors. It defines ecology as the study of relationships between living and non-living things in environments. An ecosystem includes all biotic factors such as plants, animals and microbes as well as abiotic factors like air, water and soil. Biotic factors interact with each other and abiotic factors in complex ways. The document also discusses biomes as large regional communities defined by climate and plant life, and provides examples of biomes and ecosystems.
Introduces the elementary student to some more of the basic aspects of the geography and climate of the Coniferous forests and to plant and animal adaptions needed to survive there.
This document provides a summary of the SAS programming language and various SAS procedures. It describes the basic structure of a SAS job, SAS language elements like statements, comments, and variables. It also summarizes how to work with SAS data sets, the DATA and PROC steps for data manipulation and analysis, and some common statistical and graphical procedures.
Learning
Base SAS,
Advanced SAS,
Proc SQl,
ODS,
SAS in financial industry,
Clinical trials,
SAS Macros,
SAS BI,
SAS on Unix,
SAS on Mainframe,
SAS interview Questions and Answers,
SAS Tips and Techniques,
SAS Resources,
SAS Certification questions...
visit http://sastechies.blogspot.com
8323 Stats - Lesson 1 - 03 Introduction To Sas 2008untellectualism
This document provides an introduction to SAS, a statistical software used for business intelligence. It discusses the main programming windows in SAS including the editor, log, and output windows. It also describes how to access and manage SAS datasets by assigning libraries, and how SAS programs are made up of data and proc steps to import data, create and analyze SAS datasets, and produce outputs.
This document provides an overview and introduction to the SAS statistical software system. It discusses the origins and development of SAS, describes some of the main SAS products and their uses, and provides resources for further learning including introductory books and online materials. The document also outlines the basic structure of SAS programs including data steps, procedure steps, and accessing SAS on UNIX systems. It provides explanations of key concepts such as the structure of the data step and using the INPUT statement.
htttps://www.smartprogram.in/sas
Learn SAS programming, SAS slides, SAS tutorials, SAS certification, SAS Sample Code, SAS Macro examples,SAS video tutorials, SAS ebooks, SAS tutorials, SAS tips and Techniques, Base SAS and Advanced SAS certification, SAS interview Questions and answers, Proc SQL, SAS syntax, Advanced SAS
This document provides an overview of the Statistical Analysis System (SAS) software. It discusses what SAS is used for, including data management, statistical analysis, reporting, and more. It also covers SAS components and their usage, how to install and use the SAS studio interface, basic SAS syntax like variables and data sets, and common statistical procedures in SAS like PROC MEANS, PROC FREQ, and PROC UNIVARIATE to produce measures, frequencies and graphs.
This document provides an introduction and overview of the SAS statistical software system. It discusses that SAS was originally developed in the 1970s for agricultural research, but is now widely used statistical software. It also summarizes the main SAS product lines, resources for learning SAS including introductory books and online documentation, and provides a basic overview of the SAS programming language including data steps, procedure steps, and accessing SAS.
I need help with Applied Statistics and the SAS Programming Language.pdfMadansilks
I need help with Applied Statistics and the SAS Programming Language
Solution
Introduction :
All SAS jobs are a sequence of SAS steps, which are
made up of instructions, which are called SAS
statements. There are only two kinds of SAS steps:
DATA steps are used to read, edit, and transform data
(raw data or SAS data files), to prepare SAS data sets,
PROC steps are ready-to-use procedures which
analyze or process SAS data sets. In general, data
must be in a SAS data file before they can be
processed by SAS procedures.
Without going into the details at this time, here is a
skeletal example of a SAS job:
DATA STUDENTS;
INPUT NAME $ 1-14 SEX $ 15
SECTION $ 17-19 GRADE;
DATALINES;
. . . data lines . . .
;
PROC SORT DATA=STUDENTS;
BY SECTION DESCENDING GRADE;
PROC PRINT DATA=STUDENTS;
BY SECTION;
RUN
There are two kinds of SAS data sets: SAS data files (or tables), and SAS data views. A SAS
data file contains: the descriptor portion, which provides SAS procedures and some DATA step
statements with descriptive information (data set attributes and variable attributes) about the data
, and the data portion, a rectangular structure containing the data values, with rows (customarily
called observations), and columns (customarily called variables); and which is passed to most
procedures, observation by observation. A SAS catalog is a type of SAS file which stores many
different types of information used by the SAS System. All SAS files reside in a SAS data
library. The SAS System processes the program in two steps: (1) it compiles the program, and
(2) it executes the program. When the program is compiled, a program data vector (PDV) is
constructed for each DATA step. It is an area of memory which includes all variables which are
referenced either explicitly or implicitly in the DATA step. At execution time, the PDV is the
location where the current working values are stored as they are processed by the DATA step.
Variables are added to the PDV sequentially as they are encountered during parsing and
interpretation of SAS source statements. Each step (DATA or PROC) is compiled and executed
separately, in sequence. And at execution time within each DATA step, each observation is
processed iteratively through all of the SAS programming statements of the DATA step. SAS
procedures (PROCs) are programs that are designed to perform specific data processing and
analysis tasks on SAS data sets. Base/SAS procedures fall into the following categories: SAS
Utilities -- APPEND, CATALOG, CIMPORT, COMPARE, CONTENTS, COPY, CPORT,
DATASETS, DBCSTAB, DISPLAY, EXPLODE, EXPORT, FORMAT, FSLIST, IMPORT,
OPTIONS, PMENU, PRINTTO, RANK, REGISTRY, SORT, SQL, STANDARD,
TRANSPOSE, TRANTAB; Descriptive Statistics -- CORR, FREQ, MEANS, SQL,
SUMMARY, TABULATE, UNIVARIATE; Reporting -- CALENDAR, CHART, FORMS,
MEANS, PLOT, PRINT, REPORT, SQL, SUMMARY, TABULATE, TIMEPLOT.
Creating SAS Data Files Since SAS procedures can operate only on SAS data sets, then the first
step in processing any .
This document provides an overview of the statistical software program Stata. It begins with an introduction to Stata and its capabilities for data management, visualization, and statistical analysis. It then covers the basic Stata interface and commands for importing data, exploring data, creating graphs and summaries, and managing data. Key points include how to load .dta and Excel files into Stata, use commands like codebook, summarize, tabulate, and graphs to explore data, and how to create and manage variables using commands like generate. The document is intended as a quick introduction and reference for learning basic Stata functions.
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This document provides an overview of introductory topics for getting started with data analysis in Stata. It covers first steps like setting the working directory, creating log files, and allocating memory. It also reviews opening and saving data files, finding variables, subsetting data, and understanding Stata's color-coding system for variable types. The document serves as an introduction to basic functions and commands in Stata.
This document provides an overview of SAS data sets and SAS programming. It discusses key concepts such as the two main parts of SAS programs (DATA and PROC steps), characteristics of SAS data sets such as variables and observations, and SAS libraries which are used to store SAS data sets. The document also provides examples of basic SAS code.
This document provides guidelines for developing databases and writing SQL code. It includes recommendations for naming conventions, variables, select statements, cursors, wildcard characters, joins, batches, stored procedures, views, data types, indexes and more. The guidelines suggest using more efficient techniques like derived tables, ANSI joins, avoiding cursors and wildcards at the beginning of strings. It also recommends measuring performance and optimizing for queries over updates.
This document provides an overview of SAS (Statistical Analysis Software). It describes how SAS can handle large datasets with millions or billions of records. It also lists some common SAS modules and provides examples of DATA and PROC steps to create and process SAS datasets. Finally, it discusses the SAS programming environment and how to submit and run SAS programs.
This document provides tips for importing data into SAS and reading data files. It discusses three main ways to get data into SAS: using proc import, the infile command, and cards/datalines. For proc import, it outlines the step-by-step process and equivalent SAS code. For infile, it demonstrates how to specify the delimiter for tab-delimited and other files. Cards/datalines allows directly entering small datasets into SAS code. It also shows how to read data from a URL rather than saving a local file.
The document provides an overview of the SAS system and its components. It describes the four main data-driven tasks of data access, data management, data analysis, and data presentation. It also outlines the structure of SAS programs and data sets, and how to run and submit SAS programs. Key concepts covered include DATA and PROC steps, the SAS log and output, browsing descriptor and data portions of SAS data sets, and SAS syntax rules.
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This document provides an introduction and overview of SAS programming, including descriptions of SAS datasets, variables, syntax, windows, and common procedures. It discusses the structure of SAS datasets which have a descriptor section and data section, and describes attributes of variables like name, type, and format. It also summarizes SAS syntax rules, comments, libraries, and how to use procedures like REG and UNIVARIATE.
1. The DATA step is processed in two phases - compile and execution. During compile, syntax is checked and the program data vector (PDV) is initialized. During execution, statements are executed in order and observations are written to the output dataset.
2. The PDV spans both phases and contains the current values of all variables. It is where SAS builds each observation before writing to the output dataset.
3. Understanding how the PDV is populated and changes during execution is important for effective DATA step programming and variable manipulation.
06-20-2024-AI Camp Meetup-Unstructured Data and Vector DatabasesTimothy Spann
Tech Talk: Unstructured Data and Vector Databases
Speaker: Tim Spann (Zilliz)
Abstract: In this session, I will discuss the unstructured data and the world of vector databases, we will see how they different from traditional databases. In which cases you need one and in which you probably don’t. I will also go over Similarity Search, where do you get vectors from and an example of a Vector Database Architecture. Wrapping up with an overview of Milvus.
Introduction
Unstructured data, vector databases, traditional databases, similarity search
Vectors
Where, What, How, Why Vectors? We’ll cover a Vector Database Architecture
Introducing Milvus
What drives Milvus' Emergence as the most widely adopted vector database
Hi Unstructured Data Friends!
I hope this video had all the unstructured data processing, AI and Vector Database demo you needed for now. If not, there’s a ton more linked below.
My source code is available here
https://github.com/tspannhw/
Let me know in the comments if you liked what you saw, how I can improve and what should I show next? Thanks, hope to see you soon at a Meetup in Princeton, Philadelphia, New York City or here in the Youtube Matrix.
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/141-10June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
https://www.meetup.com/unstructured-data-meetup-new-york/events/301383476/?slug=unstructured-data-meetup-new-york&eventId=301383476
https://www.aicamp.ai/event/eventdetails/W2024062014
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
PyData London 2024: Mistakes were made (Dr. Rebecca Bilbro)Rebecca Bilbro
To honor ten years of PyData London, join Dr. Rebecca Bilbro as she takes us back in time to reflect on a little over ten years working as a data scientist. One of the many renegade PhDs who joined the fledgling field of data science of the 2010's, Rebecca will share lessons learned the hard way, often from watching data science projects go sideways and learning to fix broken things. Through the lens of these canon events, she'll identify some of the anti-patterns and red flags she's learned to steer around.
06-18-2024-Princeton Meetup-Introduction to MilvusTimothy Spann
06-18-2024-Princeton Meetup-Introduction to Milvus
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://github.com/milvus-io/milvus
Get Milvused!
https://milvus.io/
Read my Newsletter every week!
https://github.com/tspannhw/FLiPStackWeekly/blob/main/142-17June2024.md
For more cool Unstructured Data, AI and Vector Database videos check out the Milvus vector database videos here
https://www.youtube.com/@MilvusVectorDatabase/videos
Unstructured Data Meetups -
https://www.meetup.com/unstructured-data-meetup-new-york/
https://lu.ma/calendar/manage/cal-VNT79trvj0jS8S7
https://www.meetup.com/pro/unstructureddata/
https://zilliz.com/community/unstructured-data-meetup
https://zilliz.com/event
Twitter/X: https://x.com/milvusio https://x.com/paasdev
LinkedIn: https://www.linkedin.com/company/zilliz/ https://www.linkedin.com/in/timothyspann/
GitHub: https://github.com/milvus-io/milvus https://github.com/tspannhw
Invitation to join Discord: https://discord.com/invite/FjCMmaJng6
Blogs: https://milvusio.medium.com/ https://www.opensourcevectordb.cloud/ https://medium.com/@tspann
Expand LLMs' knowledge by incorporating external data sources into LLMs and your AI applications.
4. Basic SAS Commands
2
LOG: Log Window.
This window contains messages describing the processing of the commands. The
messages include error messages, indicate whether data was input or output correctly and
provide the dataset names. It should be frequently reviewed to ensure proper analyses.
OUTPUT: Output Window.
All the analyses are provided in this window. Note that the results cannot be edited in
this window. For this reason you may wish to save it (to file.lst) and edit it in an editor of
your choice. Unwanted output may first be cleared by choosing Clear Text in the Edit
menu (or Ctrl-E).
HELP: Help Menu.
The Help menu contains all the basic information for executing SAS commands. Select
Help-SAS System Main Menu. Information on the SAS DATA Step, for example, is
found under SAS Language. All the basic procedures for analyzing data are found under
Modeling and Analysis Tools.
The rest of this section provides a few basic preliminaries about the SAS language .
PROCEDURES: SAS uses a number of procedures (identified with proc) to analyze
data. In addition, the datastep (which begin with data) is used to input and manipulate
data. The datastep and the procedures are discussed below in Sections 2 and 3,
respectively.
VARIABLE and DATASET NAMES: The names of variables and of SAS datasets must
consist of 8 or fewer alphanumeric characters; underscored blanks (_) also are allowed.
SAS does not distinguish between lower and upper case letters except in the value of a
character variable, a label or a title. In the examples below, SAS commands will be
given in lower case (e.g., proc reg). The names of variables and datasets will be in
upper case (e.g., X, DATASET1).
SEPARATING COMMANDS: Every command ends with a semicolon (;). First-time
users often find that a missing or misplaced semicolon is a cause of many errors. SAS
treats multiple spaces and blank lines as a single space so you may space commands to
suit you. Also, you may put more than one command (with semicolons) on the same line.
COMMENTS: If an asterisk (*) follows a semicolon (;) then everything between the
asterisk and the next semicolon is treated as commentary and is not interpreted by SAS.
Comments will be used to explain commands in the examples below.
5. Basic SAS Commands
3
2. The Datastep
DATA STATEMENT: A datastep is used to input, create, modify or combine SAS
datasets. Since most SAS procedures require a SAS dataset, this is usually the first step
other than specifying options or titles. Datasteps may also appear later in the commands.
A datastep usually contains three primary commands: data (to name the dataset and
signal SAS that a datastep begins), input (to input the values of variables from the
user's dataset) and output (to output an observation to the SAS dataset). The output
statement is often implicit and therefore optional. Between the input and output
commands, new variables can be created using ordinary mathematical expressions.
Looping can also be used. Other commands (set and merge) can be used for
modifying existing SAS datasets, but they will not be discussed here.
SAS DATASETS: SAS datasets are created using SAS. They consist of a sequence of
observations. Each observation contains the values of one or more variables named by
the user. The internal variable _N_ is the observation number, starting with _N_ = 1 for
the first observation.
USER'S DATASET: In the simplest case, the user inputs a dataset in which each line
corresponds to one observation of the SAS dataset and contains all the information
required to create that observation. More general scenarios are possible, however, as the
examples below illustrate. The user's dataset can follow the datastep or it can be in a
separate file. In the former case, the command cards must follow the datastep and the
data must begin on the line immediately after the cards command. If the data are in a
separate file, say file.dat, then an infile command is required. For example, use
data DATASET;
infile 'file.dat'; * Input data file;
INPUT STATEMENT: The input command is used to input data. It is usually easiest
to input in free format, one observation per line. However, other approaches are often
useful. See the examples below. Note that a $ must follow a character variable name.
EXPRESSIONS in the DATASTEP: Many ordinary mathematical expressions can be
used to compute new quantities. Logical expressions are indicated with parentheses (e.g.,
(X>=25)) and can be treated as ordinary numbers with values 0 (false) and 1 (true).
Also, conditional (if) statements and looping (do) statements may be used. The
examples below illustrate some uses of expressions. Section 5 lists common operators
and functions.
EXAMPLE DATASTEPS: These examples are just a small selection of the capabilities
of the datastep in SAS. Indeed, some users use SAS solely for data manipulation. The
first example is of the simplest form where each line of the user's dataset corresponds to
one observation of the SAS dataset. SAS executes the input command once for each
observation and in such a case the output command is implicit and not needed.
6. Basic SAS Commands
4
data DATASET1; * Create a dataset named DATASET1;
input NAME $15. SEX $ WT HT; * NAME & SEX have character values;
* NAME must have 15 characters;
cards; * The data begins on the next line;
J. Adams M 151 69
M. B. Finster F 104 63
K. C. Lodge M 208 73
M. J. Newsome M 185 69
Q. Prevoli F 132 66
;;; * Indicate end of data;
The semicolons at the end of the data are optional, but are useful both as visual markers
and to ensure SAS does not misinterpret any lines to follow.
The next example illustrates a formatted input where the columns for each variable are
identified. This is especially useful if the user's dataset contains more variables per
observation than are needed, but it requires the dataset to be strictly formatted. In this
example, the variables are input from a data file and are given labels. The if statement
is used to select only the observations where the patient is male.
data DATASET2; * Create a dataset named DATASET2;
infile 'patients.dat'; * Input data file;
input NAME $ 1-15 SEX $ 20 WT 30-35 HT 60-65;
if SEX='M'; * Keep only male patients;
label NAME='Name of Patient'
WT='Weight at Admission'
HT='Height at Admission'; * Label variables for plots;
Our third example shows how more than one observation can be read from the same line
of data. We also show the calculation of new variables.
data DATASET3; * Create a dataset named DATASET3;
input WIDTH HEIGHT LENGTH @@; * Continue reading on same line;
VOL=WIDTH*HEIGHT*LENGTH;
LOGVOL=log10(VOL);
LARGE=((VOL>20) or (LENGTH>5)); * LARGE is binary (0 or 1);
cards; * The data begins on the next line;
3.1 2.0 4.0 1.1 1.8 1.8 1.5 1.5 2.0
1.2 1.0 3.0 1.2 1.5 5.8 2.5 2.5 3.0
2.4 2.0 2.8 1.2 1.6 1.8 1.0 1.2 2.0
;;; * Indicate end of data (9 obs.);
The fourth example uses an array to input multiple observations per line which have
common values for some variables. In this case the first two variables (TEMP and
HUMIDITY) are common for five values of the third variable (STRENGTH). Missing
values are indicated with a . (period). Note that the output command is explicitly
given.
data DATASET4;
array X(I) X1-X5; * Define array X with length 5;
input TEMP HUMIDITY X1-X5;
do over X; * Begin a loop. I=1,5 is implicit;
7. Basic SAS Commands
5
RUN=I; STRENGTH=X; * Obtain RUN and STRENGTH;
output; * Output one observation each loop;
end; * End the loop;
drop I X1-X5; * Drop variables not needed;
cards; * 30 obs., including 2 missing;
75 70 14.3 15.9 13.8 15.7 15.2
75 80 13.6 15.3 14.8 13.2 14.6
75 90 12.3 14.7 . 15.1 13.5
85 70 14.8 15.6 14.8 15.9 16.0
85 80 14.3 13.9 15.5 15.6 15.3
85 90 13.3 . 13.4 12.7 12.5
;;;
The final example demonstrates a more explicit form of looping in order to simulate a
random sample of 50 normal data.
data DATASET5;
input MEAN STDDEV; * Input parameters;
do I=1,50; * Begin loop;
X=MEAN+STDDEV*normal(0); * normal(0) is a std. normal r.v.;
output; end; * Output X and end loop;
drop MEAN STDDEV;
cards;
40 15
;;;
3. Procedures
The most commonly used statistical procedures are given below with the required syntax
and primary options. This is meant to provide basic useful information and is not at all
comprehensive. Consult the manuals for more options and examples.
ANOVA: Analysis of Variance.
For balanced experimental designs, ANOVA is the most efficient analysis, but it can not
produce residuals (see instead GLM, or INSIGHT in Section 6). The syntax is
proc anova data=dataset; class variables;
model response variable = factors and interactions;
means effects/options;
For example, if DATASET4 shown above did not have missing data the following could
be used.
proc anova data=DATASET4;
class TEMP HUMIDITY;
model STRENGTH=TEMP|HUMIDITY; * Factorial model with interaction;
means TEMP/duncan; * Duncan's test on TEMP means;
All of the factors in the model statement must appear in the class statement. The
interactions can be given explicitly or implicitly. Examples include:
8. Basic SAS Commands
6
model Y=A B; * Factors A and B, no interaction;
model Y=A B A*B; * Factors A and B, and interaction A*B;
model Y=A|B; * Factors A and B, and interaction A*B;
model Y=A|B C; * Factors A, B and C, interaction A*B;
model Y=A B(C); * Factors A and B, and C within B;
The effects in the means statement can be any effect included in the model statement.
Means and standard errors are given for each main effect level and for each crossed effect
level. Multiple comparisons are performed for the main effects according to the option
requested. Options for the means statement include:
bon Bonferroni test
duncan Duncan's multiple range test
lsd Fisher's least significance difference test (default)
tukey Tukey's studentized range test
CHART: Frequency Bar Charts.
This provides a quick histogram-like chart, with more flexibility than the stem-leaf plots
of UNIVARIATE. (See also INSIGHT in Section 6.) For eight or more intervals, the
horizontal plots are usually better. The syntax is
proc chart data=dataset; hbar variables/options;
For example,
proc chart data=DATASET4;
hbar STRENGTH/type=pct midpoints=12 to 20 by 2;
Use vbar instead of hbar for a chart with vertical bars. Options to determine how the
scale of the bars is determined include
axis=value maximum value of the bar axis
type=freq bar length represents the frequency (default)
type=cfreq bar length represents the cumulative frequency
type=pct bar length represents the relative frequency
type=cpct bar length represents the cumulative relative frequency
By default SAS will choose the categories or the intervals for the chart. To specify them
instead use the option
midpoints=values
If values are numerical then the interval endpoints are chosen halfway between the
specified values. Equally spaced values result in a chart with equal length intervals
(except possibly the end intervals). The values can also be nominal values of a
categorical variable. Examples are
9. Basic SAS Commands
7
midpoints = 1 3 5 7 9; intervals are [min.,2), [2,4), [4,6), [8,max.)
midpoints = 1 to 9 by 2; same as above
midpoints = 1 3 7 15; intervals are [min.,2), [2,5), [5,11), [11,max.)
midpoints = A B C; categories are A, B and C
Side-by-side charts can be given for each subgroup defined by a group variable by using
the option
group=variable
CORR: Correlation Matrix.
For multivariate data, the sample correlations are provided as well as the sample means
and standard deviations of each variable. The syntax is
proc corr data=dataset options; var variables;
Options include
pearson Pearson or usual correlation (default)
spearman Spearman rank correlation
FREQ: Frequency Table.
This tabulates frequencies and/or relative frequencies according to categorical variables
given by the user. It is especially useful for cross-tabulation (when there are two or more
categorical variables) and it can do the chi-square contingency or homogeneity test. The
command is given by
proc freq data=dataset; weight frequency variable;
var variables;
The weight or frequency variable is used when the dataset consists of counts for each of
the subclasses, rather than the individual observations. To do the contingency test or the
homogeneity test use
tables class variable*class variable/options;
For example,
proc freq data=DATASET4; var TEMP HUMIDITY;
tables TEMP*HUMIDITY/expected chisq;
The options include
cellchi2 chi-square term for each cell
chisq Pearson's chi-square test
expected expected frequency for each cell under null hypothesis
10. Basic SAS Commands
8
GLM: General Linear Models.
This conducts the analysis of variance for general linear models, including those for
experimental designs and models with covariates. GLM should be used instead of
ANOVA when the experimental design is not balanced. GLM can also be used for
multiple regression but usually REG is the better procedure. Residuals can be obtained
for a residuals analysis. (INSIGHT can perform the basic analyses of GLM interactively,
but not the multiple comparisons or special tests.) The syntax for GLM is
proc glm data=dataset; class classification variables;
model response variable = predictor variables/options;
output out=new dataset keyword = name;
The predictor variables can be either numerical or class variables, but if any are the latter
they must be identified in the class statement. Interactions may be included as in
ANOVA (see ANOVA above). Additionally, quadratic and interactive terms with
numerical variables may be included as, e.g., X*X, A*X and X*Y.
Additional statements which may used in GLM, but which must appear after the model
statement, include the means, lsmeans, estimate and contrast statements.
These are discussed in more detail below.
We provide two examples. The first involves an experimental design with unbalanced
data (i.e., unequal sample sizes).
proc glm data=DATASET4; class TEMP HUMIDITY;
model STRENGTH=TEMP|HUMIDITY; * Factorial model with interaction;
lsmeans TEMP|HUMIDITY/stderr tdiff;
* Compare all cell means;
The second example is a model with both categorical and numerical variables. This
situation is sometimes called covariate analysis or regression with unequal slopes.
proc glm data=DATASET1; class SEX;
model WT=SEX HT SEX*HT/ss3 solution;
* The SEX*HT term allows for the slope to depend on SEX;
output out=DATANEW r=RESIDUAL p=PREDICTD;
estimate 'Avg Male Wt at 70 In' SEX HT SEX*HT 0 1 70 0 70;
* The coefficients for SEX are 0 (Female) and 1 (Male);
estimate 'Diff Avg Wt at 70 In' SEX HT SEX*HT 1 -1 70 70 -70;
* The coefficients for SEX are 1 (Female) and -1 (Male);
Options for the model statement include
i prints the inverse of the crossproducts matrix, X'X-1
noint suppresses the intercept term (default is to include it)
solution prints parameter estimates
ss1 prints the sequential sums of squares, according to the sequence of
predictors
11. Basic SAS Commands
9
ss3 prints the partial sums of squares for each predictor
xpx prints the crossproducts matrix, X'X
The options ss2 and ss4 are for certain exceptional situations and generally should be
avoided.
Certain values may be produced by the output statement and appended to the dataset
(thus forming a new dataset). These are indicated by keyword above and include
p values predicted by the fitted model
r residuals from the fitted model
For experimental designs the population means are estimated with the means and/or
lsmeans statements. Their syntax is
lsmeans effects/options;
means effects/options;
The options for the means statement are the same as those for ANOVA (see ANOVA).
Generally, if the experimental design is not balanced then the lsmeans statement should
be used. The options for lsmeans include
pdiff p-values for all pairwise comparisons
stderr standard errors of the estimates
tdiff t-statistics and p-values for all pairwise comparisons
Specific hypothesis tests and estimates are obtained with the contrast and estimate
statements, respectively. The syntax is
contrast 'label' effects values;
estimate 'label' effects values;
In these statements the label should be no more than 20 characters. For a class variables
(i.e., a factor) the values are a list of coefficients, one for each level of the factor, in
ascending order of the levels. For numerical variables the values are usually particular
values for those variables. The rules are somewhat complicated for handling effects
which are not included in the statement so it is often best just to include them all, with
zeros as needed. (See the second example above.)
LOGISTIC: Logistic Regression.
This procedure fits logistic regression models to a categorical response variable. Logistic
regression can be quite sophisticated, depending on the assumptions. We just discuss the
bare bones here. Note that the response is assumed to be from ordered categories. The
model fits the cumulative probabilities for those categories. If the category labels are not
in alphabetic or numeric order then the data should be grouped by category and ordered
appropriately. The syntax for LOGISTIC is as follows
12. Basic SAS Commands
10
proc logistic data=dataset options;
model response variable = predictor variables/options;
output out=new dataset keyword = name;
In the proc logistic statement options include
descending use if the categories are in reverse order (recommended if the
response is binary)
notsorted use when response categories are not in alphanumeric order and
SAS should not sort them
simple prints simple statistics for each predictor variable
Options in the model statement can include
cl provide confidence limits for parameters
corrb print correlation matrix of parameter estimates
covb print covariance matrix of parameter estimates
ctable classification table if the response is binary
noint suppress intercept in the model
risklimits provide confidence limits for odds ratios
rsquare provide R-square of model fit
Keywords allowed in the output statement include
p predicted cumulative probability for the response category
reschi residual based on Pearson's chi
resdev deviance residual
There are many other options available.
MEANS: Summary Statistics.
This procedure calculates summary statistics, such as sample mean and variance. It
differs from UNIVARIATE in that the resulting statistics may be output to a new dataset.
The syntax is
proc means data=dataset keyword list; var variables;
class classification variable;
output out=dataset keyword(s)=name(s);
The keywords in keyword list are names for various summary statistics such N, MEAN,
STD, MIN, MAX and VAR. These same keywords are used in the output statement to
assign names to the variables in the output dataset. For example,
proc means data=DATASET4 n mean std; var STRENGTH;
class TEMP HUMIDITY; out=DATANEW n=N mean=MEAN std=STD;
13. Basic SAS Commands
11
NPAR1WAY: Kruskal-Wallis Test.
Distribution free procedures for the one-way classification model such as the Kruskal-
Wallis test are obtained by
proc npar1way options data=dataset;
var response variable; class classification variable;
The Kruskal-Wallis test is also called the Wilcoxon test when there are two groups. To
use this test to compare weights for the two sexes in DATASET1,
proc npar1way wilcoxon data=DATASET1; var WT; class SEX;
Options include
anova the usual one-way analysis of variance (F-test)
wilcoxon the Kruskal-Wallis test
Other options may be used, depending on assumptions.
PLOT: Character Plot.
Although the resolution is not high, reasonable scatterplots can be obtained. (See also
INSIGHT in Section 6.) The syntax for PLOT is
proc plot data=dataset;
plot Y variables * X variables = plot variable/options;
The plot variable is optional. The default will be a letter indicating the number of data
plotted at that location (A for one observation, B for two observations, etc.). The plot
variable can be the value of a variable in the dataset or it can be a single character in
quotes, e.g. '*'. Options for PLOT include
overlay to overlay two or more plots
haxis = values identify values for the horizontal (X) axis
href = value identify where to draw the vertical (Y) axis
vaxis = values identify values for the vertical (Y) axis
vref = value identify where to draw the horizontal (X) axis
Some examples are
proc plot data=DATASET1;
plot WT*HT/haxis=60 to 75 by 5; * plot of WT vs. HT;
proc plot data=DATASET1;
plot WT*HT='*'; * plots with character *;
proc plot data=DATASET1;
plot WT*HT=SEX; * plot character is the value of SEX;
proc plot data=DATASET4;
plot STRENGTH*(TEMP HUMIDITY);
* two plots with different X variables;
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proc plot data=DATASET;
plot Y1*X='*' Y2*X='+'/overlay; * overlays two plots;
PRINT: Listing of Data.
This lists each observation with its values for the variables specified. NOTE: This
procedure does not send output to the printer. The syntax is
proc print data=dataset; var variables;
If no variables are provided then all the variables are output by default.
RANK: Data Ranking.
The data ranked in order of value (alphabetic order for character data). One option will
produce the corresponding quantiles of the normal distribution. SAS creates a new
dataset with all the old variables plus the rank values of each ranked variable. To rank
the data use
proc rank data=dataset out=new dataset;
var variables; ranks ranked variables;
For example
proc rank data=DATASET1 out=DATANEW;
var HT WT; ranks HT_RANK WT_RANK;
To obtain normal quantiles (called N_QUANT here) for the variable X and then to plot X
in a normal quantile plot use
proc rank data=DATASET out=DATANEW normal=blom;
var X; ranks N_QUANT;
proc plot data=DATANEW; plot X*N_QUANT;
REG: Regression Analysis.
Estimates of the coefficients of a multiple regression and their standard errors are
provided. It also provides an analysis of variance. Diagnostics for residuals can be
obtained. (INSIGHT will do regression interactively, except for the model selection.)
The syntax is
proc reg data=dataset;
model response variable = predictor variables/options;
output out=new dataset keyword(s)=name(s);
For example,
proc reg data=DATASET4;
model STRENGTH=TEMP HUMIDITY; * Regression with two predictors;
output out=DATANEW r=RESIDUAL p=PREDICTD;
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Each predictor variable must have been defined in a datastep. For example, X*X cannot
be used (as it can in GLM) to indicate a quadratic term. Options for the model
statement include
i prints the inverse of the crossproducts matrix, X'X-1
cli confidence limits for predicting individual values
clm confidence limits for estimating the regression curve (mean)
noint suppresses the intercept term (default is to include it)
r requests an analysis of the residuals
ss1 prints the sequential sums of squares, according to the sequence of
predictors
ss2 prints the partial sums of squares for each predictor
vif prints the variance inflation factor for each parameter estimate
xpx prints the crossproducts matrix, X'X
Certain values may be produced by the output statement and appended to the dataset
(thus forming a new dataset). These are indicated by keyword above and include
p values predicted by the fitted model
r residuals from the fitted model
Model selection criteria can also be obtained from proc reg. Note, however, that the
above options cannot be used in conjuction with the selection option. The syntax is
proc reg data=dataset;
model response variable = predictor variables/selection=rsquare options;
Options include
adjrsq adjusted R-squared criterion
aic Akaike's infomation criterion
b provide parameter estimates
best=number list only the best (by R-square) models for each given number of
parameters
cp Mallow's Cp criterion
include=number consider only models containing at least the first number of
predictors
rmse root mean squared error criterion
sse sum of squared errors criterion
RSQUARE: Regression Model Fits.
This procedure calculates model fit for full and reduced models according to various
specified criteria. It is essentially the same as the selection procedure in REG. Use
proc rsquare data=dataset;
model response variable = predictor variables/options;
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For example
proc rsquare data=DATASET4;
model STRENGTH=TEMP HUMIDITY/adjrsq cp rmse;
The model statement is the same as for proc reg (see REG). The options are also the
same, except for best.
SORT: Data Sorting.
The data are sorted alphabetically according to or by order of value for one or more
variables. This procedure is required before using the by statement in other procedures
to ensure the data are properly sorted. Use
proc sort data=dataset; by option variable … option variable;
If more than one variable is specified the dataset is ordered by the first variable first then
the second, etc. The default is to sort in ascending order but any variable name may be
proceeded by the option
descending to sort in descending order
TTEST: Student's Two Sample t-test.
This does the usual t-test to compare means from two independent samples. The
command is given by
proc ttest data=dataset; class classification variable;
var variables;
The classification variable is required and must consist of exactly two levels. To
compare the average weight of males and females in DATASET1,
proc ttest data=DATSET1; class SEX; var WT;
UNIVARIATE: Summary Statistics.
Summary statistics are calculated, including moments and percentiles. The information
is strictly univariate and no multiple variable analysis is provided, except that the analysis
can be broken down for categories by using a by statement. The syntax is
proc univariate data=dataset options; var variables;
Options include
normal provides a test of normality
plot provides a stem-leaf plot, a box-plot and a normal plot
def=5 the best definition for sample percentiles
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The plots provided by UNIVARIATE are not particularly good because they are quite
small. But if a by statement is used then side-by-side box-plots will be provided on a full
page. Larger histograms can be obtained with CHART and normal plots can be produced
using normal quantiles obtained with RANK (see RANK).
VARCOMP: Variance Components Analysis.
This procedure analyzes models with random effects (i.e., with factors having random
values for levels).
proc varcomp data=dataset method=type1;
class variables;
model response variable = effects/option;
The effects are factors and interactions comprised from the class variables. There is
only one option.
fixed=number the first number effects are fixed effects, the remainder are random
4. Other Commands
BY: Subgroup Analysis.
Most SAS procedures can be performed on subgroups of the dataset if the subgroups are
defined by a classification variable and if the dataset is first sorted by that variable. Use
the statement
by option variable … option variable;
The class variables must appear in the same order as in the most recent sort procedure
and the option descending must be used or not used as in the sort procedure. For
example to get two scatterplots, according to SEX,
proc sort data=DATASET1; by SEX;
proc plot data=DATASET1; by SEX; plot WT*HT='*';
Or to obtain summary statistics according to TEMP and HUMIDITY and side-by-side
box-plots,
proc sort data=DATASET4; by TEMP descending HUMIDITY;
proc univariate data=DATASET4 plot; var STRENGTH;
by TEMP descending HUMIDITY;
LABEL: Variable Labels.
Variables may be labeled with a character string (in quotes) at any time. If the labeling
occurs in a datastep the labels will apply henceforth; otherwise they apply only to the
current procedure. The syntax is
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label variable = 'character string' … variable = 'character string';
For example,
label NAME='Name of Patient' WT='Weight at Admission'
HT='Height at Admission';
OPTION: Page Options.
The statement to set up page options is
option options;
and possible options include
center center title, if any
date include job execution date on first line
ls=number line length is number characters
nocenter do not center title, if any
nodate do not include job execution date on first line
ps=number page length is number lines
RUN: Run Statement.
The run statement tells SAS that all commands for a particular procedure have been
provided and is required at the end of a sequence of commands submitted interactively.
The syntax is
run;
TITLE: Page Titles.
Page titles may be provided and changed at any time. They may have multiple lines. For
the first line of a title (given in quotes) use
title 'title';
and for additional lines use
title# 'title';
where # is the line number on which the title is to appear.
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5. Operators, Expressions and Functions
OPERATORS: The usual mathematical operators +, -, *, / and ** (for exponentiation)
are available as well as the comparison operators =, >, <, >=, <=. Also, not, and and
or may be used in logical expressions.
EXPRESSIONS: Expressions are constructed according to the usual rules of syntax in
most programming languages such as C or Fortran. Logical expressions are given the
numerical values 0 (false) or 1 (true) and may be used as such.
FUNCTIONS: Standard functions are available as well as many statistical functions.
The standard functions include abs, max, min, sqrt, exp, log (natural logarithm),
and log10 (logarithm base 10). Trigonometric and hyperbolic functions include sin,
cos, tan, arsin, arcos, atan, sinh, cosh and tanh.
Function that compute sample statistics either take the form function(list) where list is a
list of values and variables (e.g., sum(1,2,X,Y,Z)) or take the form function(OF list)
where list is a list only of variables, separated by spaces, and may be less explicit (e.g.,
sum(OF X1-X10 Y1-Y5)). These functions include the following.
n no. of nonmissing arguments nmiss no. of missing arguments
sum sum mean average
min smallest value max largest value
std standard deviation var variance
skewness skewness kurtosis kurtosis
cv coefficient of variation
Among the statistical functions are those which give the cumulative probability function
for various distributions:
probnorm(Z) standard normal probt(T,DF Student's T
probchi(X,DF) chi-square probf(F,NDF,DDF) Fisher's F
probgam(X,ALPHA) gamma
The inverses to these functions (which compute percentiles) include:
probit(P) standard normal tinv(P,DF) Student's T
cinv(P,DF) chi-square finv(P,NDF,DDF) Fisher's F
gaminv(P,ALPHA) gamma
There are also functions to generate pseudo-random variables: normal(SEED),
uniform(SEED) and rangam(SEED,ALPHA). The argument SEED must be a
constant, either 0 or a 7-digit odd integer and is used only the first time the function is
evaluated. If SEED is 0 then a function of the current time is used for a seed.
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6. Interactive Data Analysis (INSIGHT)
Most of the basic model fitting and testing (as in REG or GLM) can be done interactively
with SAS using INSIGHT, which is windows oriented and menu driven. It is also the
best and easiest way to get decent graphs. Before it can be used you first must create the
desired data (with a datastep or as output from a procedure).
To start INSIGHT select Analyze-Interactive Data Analysis from the Globals menu. This
brings up a menu of data libraries. Choose WORK and then the dataset desired. SAS
then provides a spreadsheet of the data. (It is also possible to enter data into the
spreadsheet.) By double clicking on a variable in the spreadsheet, you bring up a dialog
box for that variable. You can then change or assign labels, variable type and other
attributes.
Now choose the desired analysis from the Analyze menu. To select the variables for the
analysis, highlight the variable names and then click on Y or X as desired. In addition,
each analysis has a number of options concerning the type of plots to be produced. Both
DISTRIBUTION and FIT analyses include options for "smooth" fitting of the data. You
can also obtain residuals from the FIT analysis and perform residuals diagnostics.
Note that the output is graphical. Tables and graphs should be saved separately as
follows. To save tables, hold the Ctrl key down while highlighting each table you want
to save. Select File-Save-Tables, which copies the tables to the OUTPUT window. Select
the OUTPUT window, then select File-SaveAs to save the output as a text file (e.g.,
file.txt) which may be edited later. You cannot edit in the OUTPUT window. To save a
graph, first highlight it. Then select File-Save-GraphicsFile and check GIF and grey
scale (unless you really want color). Do not save it as a bitmap file because the file will
be 10-100 times larger with no improvement in quality. Give a filename (e.g., file.gif)
and click OK. The graphics file may be inserted as a picture with a suitable word
processor. Repeat the above for each graph you want to save. (Except when graphs are
presented adjacent to each other, it is recommended you save each graph separately.)
For more information, see the Help menu while in an INSIGHT window. Specifically,
Help-References and Help-Techniques have information about the analyses available in
INSIGHT.