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Ann Arbor ASA Up and Running Series: SAS Sponsored by the Ann Arbor Chapter of the American Statistical Association and the Department of Statistics of the University of Michigan

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Contents Starting SAS User Interface Libraries Syntax Getting Data into SAS Examining Data Manipulating Data Descriptive Statistics Graphing Data Statistics in SAS Up and Running Series: SAS 2

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Starting SAS Start SAS 9.3 (English) Up and Running Series: SAS 3

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User Interface Log Comments, warnings, etc. Program Editor: Write and submit commands Output (not seen) Explorer/ Results Up and Running Series: SAS 4

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Libraries SAS requires the creation of Library folders to save the data –Libraries are accessed through LIBNAME command Four Libraries are defined by default, at the start of SAS –Maps –SASHELP: holds help info and sample datasets –SASUSER: holds settings, etc. –WORK: default temporary Library for each session All data stored in this folder will be deleted at the end of each SAS session It is recommended the creation of permanent files/Libraries Up and Running Series: SAS 5

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Libraries Create a folder called ‘my_files’ on your desktop. Run this command in SAS: LIBNAME a "C:\Users\ uniquename \Desktop\ my_files "; Refer to datasets in that folder by with the prefix ‘a.datasetname’. TIP: Use memorable names for libraries, rather than ‘a’ (e.g., ‘raw’, ‘final’, ‘time1’, etc) Up and Running Series: SAS 6

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Syntax SAS divides commands into two groups –DATA step create/alter datasets –PROC (Procedures) perform statistical analyses or generate reports. Some exceptions to the rule: –DATA step can be used to generate reports –PROC IMPORT creates a data set –PROC SORT alters data sets (without telling you!) Up and Running Series: SAS 7

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PROC IMPORT –Allows the reading of standard file types –Allows the reading of plain text, with user-specified delimiters (i.e., the characters which separate the data) –WARNING – SAS changed PROC IMPORT for Excel and Access files, in 64-bit SAS DATA step –Allows the reading of non-standard file types, complex file structures, and unusual delimiters. Getting data into SAS Up and Running Series: SAS 8

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PROC IMPORT Editor PROC IMPORT OUT= a.class2 DATAFILE="C:\Users\ uniquename \Desktop\class2.xlsx" DBMS=xlsx REPLACE; GETNAMES=YES; RUN; Log 1 PROC IMPORT OUT= a.class2 2 DATAFILE="C:\Users\ uniquename \Desktop\class2.xlsx" 3 DBMS=xlsx REPLACE; 4 GETNAMES=YES; 5 RUN; NOTE: The import data set has 19 observations and 5 variables. NOTE:.a.class2 data set was successfully created. NOTE: PROCEDURE IMPORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds Up and Running Series: SAS 9

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DATA step SAS syntax can be used to read in raw data files (.txt,.csv files), specifying which variables to read in, which ones are text/numeric, combining multiple rows into one case, etc. However, this is a more advanced topic. –Follow up with an Intro class from CSCAR, or by going through examples from the literature (e.g., ‘The Little SAS Book’). Up and Running Series: SAS 10

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Examining Data VIEWTABLE Window –Select dataset icon in Explorer PROC CONTENTS –Produces a listing of data set information, including the variables and their properties PROC PRINT –Prints a subset of variables or cases to the output window Up and Running Series: SAS 11

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VIEWTABLE Window Up and Running Series: SAS 12

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PROC CONTENTS In the Editor window, type: PROC CONTENTS data=a.class2; run; Highlight the syntax Submit for processing –Click on icon of ‘running-man’ –Right click on selected syntax Submit Selection Up and Running Series: SAS 13

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PROC CONTENTS Up and Running Series: SAS 14

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PROC PRINT In the Editor window, type: PROC PRINT data=a.class2; run; Submit for processing Up and Running Series: SAS 15

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PROC PRINT Up and Running Series: SAS 16

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Manipulating Data Usually done within a data step –Match data sets using a shared key variable –Create new variables, or drop/rename existing variables –Take one or more subsets of the data –Sort the data by specific variable(s). Overwrite existing or create new dataset s –PROC SORT –Adding/Removing variables –Merging Datasets Up and Running Series: SAS 17

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PROC SORT In the Editor window, type: PROC SORT data=a.class2 out=a.class2sorted; by age descending weight height; run; Submit for processing WARNING: PROC SORT alters data –Store in a new dataset out=‘newdatasetname’; Up and Running Series: SAS 18

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PROC SORT Up and Running Series: SAS 19

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Adding/Removing variables Create new data set, compute new variables, remove unwanted variables DATA a.class2metric (drop=weight height sex age); set a.class2; height_cm=height*2.54; weight_kg=weight/2.2; labelheight_cm=‘Height in CM’ weight_kg=‘Weight in Kilograms’; run; PROC PRINT data=a.class2metric; run ; Submit for processing Up and Running Series: SAS 20

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Adding/Removing variables Up and Running Series: SAS 21

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Merging Datasets Data sets must be sorted by the same key variable(s) proc sort data=a.class2; by name; proc sort data=a.class2metric; by name; data classmerged; merge a.class2 a.class2metric; by name; run; Submit for processing Up and Running Series: SAS 22

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Merging Datasets Up and Running Series: SAS 23

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Merging Datasets Up and Running Series: SAS 24

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Descriptive Statistics PROC FREQ –Produces a table of counts and percentages –For cross-tabulations, statistical tests can also be performed; e.g., independence testing PROC MEANS –Produces descriptive statistics such as mean, standard deviation, minimum, maximum Up and Running Series: SAS 25

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PROC FREQ In the Editor window, type proc freq data=a.class2; tables age*sex; run; Submit for processing Up and Running Series: SAS 26

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PROC FREQ Up and Running Series: SAS 27

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PROC MEANS In the Editor window, type proc means data=a.class2; var age weight height; run; Submit for processing Up and Running Series: SAS 28

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PROC MEANS Up and Running Series: SAS 29

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Graphing Data PROC GPLOT Simple bivariate scatterplot Separate lines Multiple variables scatterplot Options Up and Running Series: SAS 30

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PROC GPLOT Simple bivariate scatterplot: proc gplot data=a.class2; symbol1 value=dot interpol=rl; plot weight*height; run; Submit for processing Up and Running Series: SAS 31

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PROC GPLOT - Log Up and Running Series: SAS 32

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PROC GPLOT Up and Running Series: SAS 33

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To graph separate lines for each level of a categorical variable, type: proc gplot data=a.class2; symbol1 value=dot interpol=rl; plot weight*height = sex; run; Submit for processing PROC GPLOT Up and Running Series: SAS 34

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PROC GPLOT Up and Running Series: SAS 35

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Multiple variables on the same graph: proc gplot data=a.class2; symbol1 value=dot interpol=rl color=blue; symbol2 value=dot interpol=rl color=red; plot weight * age; plot2 height * age; run; quit; Submit for processing PROC GPLOT Up and Running Series: SAS 36

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PROC GPLOT Up and Running Series: SAS 37

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value=___ Any character enclosed in single quotes Special characters –dot –plus sign –star –square –...and many others interpol=___ RL / RQ / RC –linear –quadratic –cubic –regression curves JOIN –connects consecutive points (line graph) BOX PROC GPLOT Up and Running Series: SAS 38

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Statistics in SAS PROC CORR –Correlational analyses PROC REG –Statistical Regression PROC UNIVARIATE –To assess normality of regression residuals Up and Running Series: SAS 39

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PROC CORR Compute bivariate correlation coefficients proc corr data = a.class2; var age; with height weight; run; Up and Running Series: SAS 40

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PROC CORR Up and Running Series: SAS 41

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PROC REG Run a regression on merged ‘class’ dataset –Save residuals and predicted values in an output dataset –Request residual plot proc reg data=a.classmerged; model height_cm=age weight / partial; output out=reg_data p=predict r=resid rstudent=rstudent; plot rstudent. * height_cm; run; quit; Notes – the quit command terminates the regression procedure; otherwise it keeps running; the output data set will be in the work library, since no library was specified. Up and Running Series: SAS 42

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PROC REG Up and Running Series: SAS 43

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PROC REG Up and Running Series: SAS 44

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PROC REG Up and Running Series: SAS 45

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PROC REG Up and Running Series: SAS 46

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PROC UNIVARIATE Assess normality of regression residuals stored in the output dataset from PROC REG: proc univariate data=reg_data; var rstudent; histogram; qqplot / normal (mu=est sigma=est); run; quit; Up and Running Series: SAS 47

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PROC UNIVARIATE Up and Running Series: SAS 48

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PROC UNIVARIATE Up and Running Series: SAS 49

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PROC UNIVARIATE Up and Running Series: SAS 50

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QUESTIONS Up and Running Series: SAS 51

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Winter 2013 Training from CSCAR http://cscar.research.umich.edu/workshops/ Introduction to SAS® - January 28,30, February 1,4,6,8, 2013 Intermediate Topics in SPSS: Data Management and Macros - February 5,7, 2013 Intermediate Topics in SPSS: Advanced Statistical Models - February 12,14, 2013 Intermediate SAS® - February 25,27, March 1, 2013 Regression Analysis - March 11,13,15, 2013 Applications of Hierarchical Linear Models - March 18,20,22, 2013 Statistical Analysis with R - March 19,21, 2013 Introduction to NVivo - April 3, 2013 Applied Structural Equation Modeling - April 10,11,12, 2013 Up and Running Series: SAS 52

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Further Resources The Little SAS Book: A Primer UCLA site –software tutorials, classes and lectures on statistical methods – an incredible site! http://www.ats.ucla.edu/stat/ http://www.ats.ucla.edu/stat/ SAS Documentation: http://support.sas.com/documentation/ http://support.sas.com/documentation/ Documentation also found in ‘SAS help’ files. Up and Running Series: SAS 53

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54 Other Winter 2013 Workshops from Ann Arbor ASA R - January 31, 1-3 PM Angell Hall Computing Classroom B (also known as MH444-B) For more information go to: http://community.amstat.org/annarbor/home http://community.amstat.org/annarbor/home Up and Running Series: SAS

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PLACE Starbucks State & Liberty, lower level TIME 6:00pm – 6:45pm, DATE TOPIC 24-JAN Business Meeting 1 -APR Business Meeting and Election of Officers For more information go to: http://community.amstat.org/annarbor/home http://community.amstat.org/annarbor/home Chapter Meetings open to all Up and Running Series: SAS 55

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