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Sartenes. UCM-Universidad Complutense de Madrid. Portada » La Universidad Complutense » [...] » Personal docente e investigador contratado » Profesor Asociado Profesor Asociado Advertencia: La información contenida en estas páginas es meramente informativa y no originará derechos ni expectativas de derechos, de acuerdo con el artículo 14 del Decreto 21/2002 de 24 de enero (BOCM de 5 de febrero de 2002) que regula la atención al ciudadano en la Comunidad de Madrid para el ejercicio de sus derechos, cumplimiento de sus obligaciones y acceso a los servicios públicos.
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Introduction to merging in SAS — UNC Carolina Population Center. What is a match-merge?
A match-merge combines observations from two or more SAS data sets based on the values of specified common variables (one or more) creates a new data set (the merged data set) is done in a data step with the statements MERGE to name the input data sets BY to name the common variable(s) to be used for matching Prerequisites for a match-merge input data sets must have at least one common variable to merge oninput data sets must be sorted by the common variable(s) that will be used to merge on From now on when we use the term "merge" it will mean "match-merge. " Sasopedia/Language elements - sasCommunity. Cody's Collection of Popular SAS Programming Tasks and How to Tackle Them - Ron Cody. Lesson 14: Data Step Options. In this lesson, we explore the various DATA step options that are available in SAS to control the structure and contents of a SAS data set when the input is from a SAS data set.
For example, we might want to select only those observations in a SAS data set that meet a certain condition. Or, we might want to select only a subset of variables to keep in a working analysis data set. Options illustrated in this lesson include: FIRSTOBS= and OBS=, to reduce the number of observations in the dataset. DROP= and KEEP=, to reduce the number of variables in the dataset. Learning objectives & outcomes Upon completing this lesson, you should be able to do the following: Our "to do" list for this lesson. Lesson 15: Combining SAS Data Sets. In this lesson, we will learn how to combine data sets in four different ways — one-to-one reading, one-to-one merging, concatenating and interleaving.
Although one-to-one reading and one-to-one merging both involve placing one data set to "to the right" of other data sets to create a single "fat" data set, the results can differ slightly. Concatenating involves stacking one data set "below" other data sets to create a single "tall" data set. Lesson 16: Combining SAS Data Sets. In the last lesson, we learned different ways of combining SAS data sets — one-to-one reading, one-to-one merging, concatenating and interleaving.
In this lesson, we'll finish up our work in this arena by investigating the process of match-merging, in which we combine two or more SAS data sets based on the values of one or more common variables using MERGE and BY statements. This method deserves its own lesson, because it is far and away the most commonly used method of combining SAS data sets. Once we've learned the how to match-merge two or more SAS data sets, we'll also spend some time exploring how to use DATA step options at the same time that match-merge. Learning objectives & outcomes. Lesson 17: Using the OUTPUT and RETAIN statements. When processing any DATA step, SAS follows two default procedures: When SAS reads the DATA statement at the beginning of each iteration of the DATA step, SAS places missing values in the program data vector for variables that were assigned by either an INPUT statement or an assignment statement within the DATA step.
(SAS does not reset variables to missing if they were created by a SUM statement, or if the values came from a SAS data set via a SET or MERGE statement.) At the end of the DATA step after completing an iteration of the DATA step, SAS outputs the values of the variables in the program data vector to the SAS data set being created. In this lesson, we'll learn how to modify these default processes by using the OUTPUT and RETAIN statements: Lesson 18: Generating Data With Do Loops. When programming, you can find yourself needing to tell SAS to execute the same statements over and over again.
That's when a DO loop can come in and save your day. The actions of some DO loops are unconditional in that if you tell SAS to do something 20 times, SAS will do it 20 times regardless. Onlinecourses.science.psu. In this lesson, we'll learn about basic array processing in SAS.
In DATA step programming, you often need to perform the same action on more than one variable at a time. Although you can process the variables individually, it is typically easier to handle the variables as a group. Arrays offer you that option. For example, until now, if you wanted to take the square root of the 50 numeric variables in your SAS data set, you'd have to write 50 SAS assignment statements to accomplish the task.
Onlinecourses.science.psu. Introduction In this lesson, we'll focus on two special situations that you might encounter when trying to read an input raw data file into a SAS data set, namely: When you need to read across several records in the input raw data file in order to create one observation in a SAS data set.When you need to read from just one record in the input raw data file in order to create multiple observations in a SAS data set.
We'll learn how to use two different line pointer controls — the forward slash (/) line pointer control and the pound-n (#n) line pointer control — to accomplish the first task. And, we'll learn how to use two different line-hold specifiers — the double trailing at sign (@@) and the single trailing at sign (@) — to accomplish the second task. Learning objectives & outcomes Upon completing this lesson, you should be able to do the following:
Lesson 20: More on Importing Data. In Stat 480, we learned how to read only the most basic data files into a SAS data set.
In this lesson (and the next), we'll extend our knowledge in this area by learning how to read just about any data file into SAS — no matter how messy or unstructured the input data file. In most cases, the data files will be raw ascii data files that are obtained from exporting data from some other PC software. Learning objectives & outcomes Upon completing this lesson, you should be able to do the following: Our "to do" list for this lesson. Onlinecourses.science.psu. In Stat 480, we learned how to read only the most basic data files into a SAS data set.
In this lesson (and the next), we'll extend our knowledge in this area by learning how to read just about any data file into SAS — no matter how messy or unstructured the input data file. In most cases, the data files will be raw ascii data files that are obtained from exporting data from some other PC software. Learning objectives & outcomes Upon completing this lesson, you should be able to do the following: Onlinecourses.science.psu. In this lesson, we will investigate various aspects of processing dates and times within the SAS System.
Specifically, we will learn: how SAS defines numeric date and time valueshow to use informats to read dates and times into a SAS data sethow to use formats to display SAS dates and timeshow to use dates and times in calculationshow to compare a SAS date to some date constant, and how to compare a SAS time to some time constanthow to use several of the available SAS date and time functionshow to change the system options that pertain to processing date and times As always, you'll probably want to follow along in the lesson by downloading and running the provided SAS programs yourself. Learning objectives & outcomes Upon completing this lesson, you should be able to do the following: Onlinecourses.science.psu. Onlinecourses.science.psu. In this lesson, we'll investigate some of the functions available in SAS that can be applied only to character variables. For example, if you want to remove blanks from a character string, you might consider using the compress function.
Or, if you want to select a smaller substring, say a first name, from a larger string containing one's full name, you might want to take advantage of the substr function. Some of the functions that we will learn about are old standbys, such as: length, substr, compbl, compress, verify, input, put, tranwrd, scan, trim, upcase, lowcase, | | (concatenation), index, indexc, and spedis. And, some of the functions that we will learn about are new just to SAS Version 9. They include: anyalpha, anydigit, catx, cats, lengthc, propcase, strip, count, and countc. Onlinecourses.science.psu. This lesson introduces the most commonly used features of the SAS macro language. When you write a program that will be run over and over again, you might want seriously to consider using "macros" in your code, because: macros allow you to make a change in one location of your program, so that SAS can cascade the change throughout your program macros allow you to write a section of code once and use it over and over again, in the same program or even different programsmacros allow you to make programs data driven, letting SAS decide what to do based on actual data values.
Onlinecourses.science.psu. In this lesson, we investigate how to use the FREQ procedure to conduct various statistical analyses on categorical data that can be summarized in two-way frequency tables. G. Two-way Frequency Tables Page 89. The table on the bottom of page 89 is incorrect. The cells in the Dewey row are missing the last row of numbers corresponding to column percent. Onlinecourses.science.psu. In this lesson, we return to a number of topics that we already learned about in Part II of this course. Such topics include handling date variables, selecting first and/or last observations, and using the MEANS and SUMMARY procedures to calculate summary statistics. Here, however, our focus will be on using the techniques to analyze longitudinal data, that is, data that are collected over time. B. Processing Date Variables If you are pressed for time, and you feel really comfortable with handling dates in SAS, you might opt to skip reading this section.
Onlinecourses.science.psu. In this lesson, we investigate statistical analyses that are typically performed when dealing with two or more continuous numeric variables. Onlinecourses.science.psu. In this lesson, we learn how to use the TTEST procedure for comparing the population means of two groups when there is reason to believe that the sampling distribution of the means is at least approximately normally distributed. In the cases in which we wouldn't be able to sleep at night making that assumption, we learn how to use the NPAR1WAY procedure for comparing the medians of two groups. Onlinecourses.science.psu. In this lesson, we investigate two of the more common statistical analysis procedures. Specifically, we investigate:
Onlinecourses.science.psu. Onlinecourses.science.psu. In this lesson, we'll explore using the REG procedure for performing regression analyses with a continuous response variable and more than one predictor variable. We'll also explore using the LOGISTIC procedure for performing logistic regression analyses with a binary response variable and one or more predictor variables.
Onlinecourses.science.psu. Psychometrics concerns the study of educational and psychological measurement. Onlinecourses.science.psu. Onlinecourses.science.psu. Let's get started! Onlinecourses.science.psu. Onlinecourses.science.psu. Onlinecourses.science.psu. Onlinecourses.science.psu. Onlinecourses.science.psu.
Onlinecourses.science.psu. Onlinecourses.science.psu. Onlinecourses.science.psu. Macro SAS. Variables de un dataset en una macro variable » Análisis y decisión. Onlinecourses.science.psu. Onlinecourses.science.psu. SAS » Análisis y decisión. SAS » Análisis y decisión. Onlinecourses.science.psu. Fundamental Concepts for Using Base SAS Procedures: Language Concepts. Learning Path.
Recetas. SAS » Análisis y decisión. Sas. Creating and Using Macro Programs - 59 of 61. General Macros - Chris's SAS Macros. Garcya. 1.- TEMAS PDF. Uned. Diagramas de flujo. C++. Alemán. Curso básico de matemáticas para estudiantes de económicas y empresariales. Sice. Libros_cristina. Bancos_imágenes. Open office. Aiuhs. Cocina y trucos. Acertijos. Smart. Libros. Manguitos. Tipografías. Extensiones_joomla. Ink. HTML Videos. Vídeo en HTML5?
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