Base Sas Certification Exercise

Session 1 [edit]

exercise 1 Fill in the blank: data test; _______ x 1-2 y 3-6 z 7-9; cards; 12 5 34 6 5 80 8 ; run;

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exercise 2 Fill in the blanks: data cat; ______ 'C:\Documents and Settings\Yuan-Ting Wang\UserData\cat.txt'; ______ ID $ 1-4 AGE 6-7 SEX $ 8; run;

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exercise 3 Write a program, similar to the previous exercise that will create a temporary dataset called Students (who are all cool cats). There are perhaps two (or more) ways to do this. One way uses the raw data file directly. Or you may assume that a temporary dataset (called cats, work.cats, or 'cats.sas7bdat'(in some folder) already exists. What changes should be made so that the DATA step reads only the first 15 observations? [edit]

exercise 4 Look at Example 1 in Session 1 : data test; input x y; cards; 12 34 5 80 ; run;

How many times does SAS execute the INPUT statement when the program is submitted? [edit]

exercise 5 In example 6:

data scores; infile datalines _______; input score1-score5; datalines; 90 98 98 80 100 98 70 78 20 50 90 30 60 ; run; proc print data=scores; run;

If you know there are five tests in the semester, the first person missed the last two tests and she got 90 98 98 in the first three tests. The second person got 80 100 98 70 78, and the third person got 20 50 90 30 60. Then which options should you use to present the correct data set. [edit]

exercise 6 In Example 5: data test; input x y z; cards; 12 4 34 7 5 80 3 6 20 2 9 30 1 ; run; proc print data=test (firstobs=2); run;

Which changes should be made if you only want to read the first three observations? And which changes should be made if you want to read the second and the third observations? [edit]

Session 2

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exercise 1 In example 3.2

data sealife; input name $ family $ length ; datalines; beluga whale 15 whale shark 40 basking shark 30 gray whale 50 mako shark 12 sperm whale 60 dwarf shark .5 whale shark 40 humpback . 50 blue whale 100 killer whale 30 ; run;

Write a program to create a new data set called newsealife, and set a new variable-newlength which presents the length in two decimal points. And print out the new data set with only the new variable-newlength. Hint: the output should look like below Obs 1 2 3 4 5 6 7 8 9 10 11

newlength 15.00 40.00 30.00 50.00 12.00 60.00 0.50 40.00 50.00 100.00 30.00

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exercise 2 In example 3.4 Name ClassRm Month Day Year Candy Quantity Adriana 21 3 2 2000 MP 7 Nathan 14 2 28 2000 CD 19 Matthew 14 3 1 2000 CD 14 Claire 14 3 3 2000 CD 11 Caitlin 21 2 24 2000 CD 9 Ian 21 3 3 2000 MP 18 Chris 14 2 18 2000 CD 6 Anthony 21 6 1 2000 MP 13

Stephen 14 3 25 2000 CD 10 Erika 21 3 25 2000 MP 17

Create a program which shows the min and the max of the quantity. Hint: The output should look like below: Analysis Variable : Quantity Minimum Maximum ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 6.0000000 19.0000000 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ [edit]

exercise 3 Follow the previous exercise. Group the candy into two groups. Call the Candy with quantity less than 12 "1", and call the other "2". And then print out the result. Hint: The output should look like below Obs 1 2 3 4 5 6 7 8 9 10

group 1 2 2 1 1 2 1 2 1 2

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exercise 4 Follow the previous exercise, sort the data by group. And calculate the mean quantity of each group. Hint: the output should look like below --------------------------------------------- group=1 The MEANS Procedure Analysis Variable : Quantity Mean -----------8.6000000 -------------------------------------------------------- group=2 Analysis Variable : Quantity Mean -----------16.2000000 ------------

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exercise 5 Instead of creating the seperate tables above, what changes are you going to make if you want a single table like below: Analysis Variable : Quantity N group Obs Mean ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ 1 5 8.6000000 2 5 16.2000000 ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

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Session 3 [edit]

Exercise 1 Use the data in example 3.4 to create a HTML page which has the title Candy Data , and shows the correlation between quantity of the candy and the classroom. Finally, save the HTML page in your computer: Hint: Use the html , title option in ods statement. And the Proc statement. [edit]

Exercise 2 Use the data in Session 4.4. Use the round function for SDAR, the substr function to choose the first five characters of the name variale, and sort the data by the first five characters of the name. [edit]

Exercise 3 Use the data in Example 4.5 to calculate the average minutes that students spend on the homework per day. Hint: using the mean function. [edit]

Exercise 4 Use the example 4.6. Set an array T to describe the Women's and men's Salary in thousands. Hint: T[i]=T[i]*1000 [edit]

Session 4 [edit]

Exercise 1 Use the data in example 2.1. Run the regression with the independent variables-weight and RestPulse and the dependent variable-age. Remove those observations with the absolute value of the residual larger than 8, and re-run the program again. Use the option outest to compare the results you get. Hint: use the option r in output statement to calculate the residual. [edit]

Exercise 2 Follow the previous example. Do the plot option in proc statment and see the relation between age and weight. [edit]

Exercise 3 Create the data called month. Use do loop to get the output which has 12 months. Merge the month data and the data below-the frequency of speeding per month: datalines; 2 3 2 1 3 6 8 9 10 13 2 12 ;

Print out the data you just merged, and see what it looks like. [edit]

Session 5 [edit]

exercise 1

Use the example below:

data phone; input City $11. @12 State $ Zip $ Phonenum $; cards; cary NC 27513 6224549 cary NC 27513 6223251 chapel-hill NC 27514 9974794 raleigh NC 27612 6970450 raleigh NC 27612 6791125 cary NC 27513 6224550 ; run; proc print data=phone; run;

Use substr function to limit the first three numbers in phone number. Use length function to see how many characters the city has. [edit]

exercise 2 Variable: year Y = Index of Real Compensation per Hour, 1982=100 X = Index of Output per Hour, 1982=100 1982 100.0000 100.0000 1983 100.5000 102.0000 1984 100.4000 104.6000 1985 101.3000 106.1000 1986 104.4000 108.3000 1987 104.3000 109.4000 1988 104.4000 110.4000 1989 103.0000 109.5000 1990 103.2000 109.7000 1991 103.9000 110.1000

Use the information above to run the regression with the dependent variable-Y and independent variable-X. See the relationship between the compensation and output per hour. [edit]

exercise 3 According to the previous data, you want to change the index base to 1 not 100. What change should you make? Will that affect the result of the regression? Hint:Apply the informat function in your codes or creat new variables. [edit]

exercise 4

Use the data below: Y 10 8 6 4 2 0 2

X2 1 2 3 4 5 6 7

X3 1 3 5 7 9 11 13

Treat Y is the dependent variable and X3 is independent variable. Take log on both sides, and compare the regression with log and without log. [edit]

exercise 5 Go to the US. census Bureau website, and findout the 2005 3rd quarter data in Federal Assistance Award Data System. Download the flat data file and try to import the data into your SAS program. Print out the first 15 observations for variables- COUNTY_NAME F_FUNDS T_FUNDS. Compare the output and the original excel data file, see if you get the correct print out. If you are interested in the data, you can read FAADS User Guide, and see how to use the data. If you can't find the flat data file, here is the link [1] [edit]

Session 6 Use the data "air" in sashelp. Format the variable date into julday. Also try to informat the variable date into numbers of day. And explain why you get negative value in date in this case. Hint: Since the "informat" must follow by the input statement, you have to put infile statement too. But never use infile to read sashelp data, because infile statement can only read text file. So what you may do is to save the sas data file into text file then use it in the infile statement. [edit]

Session 7 [edit]

exercise 1 Follow questions in /* */ and complete the codes we did in the course. Please fill out all the blank below: data airt; set sashelp.air;

day=day(date); mo=month(date); yr=year(date); decade=(int(yr/10)-190)*10; run; proc print data=airt; run; data airt; set airt; drop day date; run; /*define a new variable-mair which stands for the maximum value of air line travel in each decade*/ proc ____ data=airt; __ decade; var air; output out=maxair ___=mair; run; proc print data=maxair; run; data test; set maxair; drop _type_ _freq_; run; /*Save the test data file into the permanent sas file*/ data '_____________________'; set ____; run; data airmax; set 'c:\temp\airmax.sas7bdat'; run; /*Now you want to merge the data airmax and airt by decade. And define a new variable relative=((mair-air)/mair)*100. Fill out the blank below*/ proc sort data=airmax; ___ decade; run; proc print data=airmax; run; proc ____ data=airt; ___ decade; run; proc print data=airt; run; data final; ____ airt airmax; ___ decade; relative=((mair-air)/mair)*100; run;

proc print data=final; run;

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exercise 2 Go to the website of US. Department of Labor: [2] Find the data file describes the population-employment ratio for white male until 2002. And if now you have data in 2002 and 2003 looks like below: Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2003 74.4 74.4 74.2 74.2 74.1 74.0 74.0 73.8 73.9 73.7 73.3 73.3 2004 72.7 73.2 72.8 72.9 73.1 73.1 73.1 73.2 73.0 72.9 72.7 72.4

Try to combine those two data sets. [edit]

exercise 3 If the new data you get includes 2002-2004 instead of 2003-2004, then there will be a duplicate data for 2002 after you combine the old data and new data. How would you fix the problem? Which SAS options are you going to use? [edit]

Session 8 [edit]

exercise 1 Use the soup example introduced in the workshop. Try to count the soup which is made by chicken. Hint: You can use SUBSTR or INDEX to solve the problem. [edit]

Session 9 [edit]

exercise 1 Use the Example for Model Procedure in SAS Certification Examples(part 2). 1.Plot the original data

2.Apply the economic model: population = a / ( 1 + exp( b - c * (year1790) ) ) where a=Maximum Population. b=Location Parameter. c=Initial Growth Rate. Use Model Procedure to estimate a, b and c note:a should start from 1000, and b shoud start from 5.5 and c should start from 0.02 in SAS you can write: start=(a 1000 b 5.5 c .02) 3.plot the model you estimate.

Session 6 [edit]

Example 1 data test; file 'c:\Temp\myfile.txt'; x=10; y=20; put x; put y; output; x=3; y=36; put x; put y; put 'All done.'; run;

Try putting a * in front of the file statement. Run the code clip again and look what's in the LOG. After making a dataset in SAS, it becomes a temporary .sas7bdat file in a WORK library that is cleared when you close the SAS session. data test; x=10; y=20; output; run;

Before you close SAS you can simple read it by using a set statement. Here's an example of reading a plain text file data testds; infile 'c:\Temp\myfile.txt'; input x; run; proc print data=testds; run;

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Example 2 Strings data strings1; infile 'c:\Temp\strings.txt'; input id month $ grade $1. correct total; run; proc print data=strings1; run;

The $1. is an INformat. An informat always has a . (dot) in it. Without an informat specification on the INPUT statement SAS may have some difficulty determining what length to make the placeholder for the textual information in a column. data strings1; infile 'c:\Temp\strings.txt'; input id month $ grade $ correct total; run; proc print data=strings1; run;

To assist SAS with correctly jumping to a column where we expect certain data to reside on each record/row we use pointer control (using the @ sign on the INPUT statement). data justyr; infile 'c:\Temp\strings.txt'; input @10 yr @13 grade $1. @15 correct total; run; proc print data=justyr; run;

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Example 3 Using Informats to do more. data strings2; input id month $ grade $upcase1. correct total major $20.; cards; 101 10-2006 A 12 14 Economics 101 11-2006 A 15 15 Econ 101 12-2006 B 15 20 economics 101 1-2007 a 15 15 econ 102 10-2006 c 10 14 Psychology 102 12-2006 C 12 20 Psych 102 01-2007 B 13 15 PSYCH 103 10-2006 a 14 14 Econ 103 11-2006 B 12 15 Econ 103 12-2006 B 16 20 Econ 104 01-2007 A 15 15 Decker School of Nursing ;

run; proc print data=strings2; run;

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Example 4 Using common string functions. data str3; set strings2; m=substr(month,1,2); maj=substr(major,1,4); l=length(major); lenm=length(month); run; data str4; set strings2; maj=upcase(substr(major,1,4)); run;

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Example 4a A short practical use of converting everything to upper case and using a 'subsetting IF' statement. data econ; set str4; if (maj='ECON'); run;

proc means data=econ; var correct; run; proc print data=econ; run;

Other functions (acting on character strings) /* index anylower anyupper if 'a'='A' then do; end; anyspace if upcase('a')=upcase('A') then do; end; */

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Example 5 INformats for numbers.

data numbers; input n 4.1 @6 n2 ; cards; 012345678910 8912827384 999999 6666 103 ; run; proc print data=numbers; run;

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Closing examples data stringok; input @21 majorcap $upcase4.; cards; 101 10-2006 A 12 14 Economics 101 11-2006 A 15 15 Econ 101 12-2006 B 15 20 economics 101 1-2007 a 15 15 econ 102 10-2006 c 10 14 Psychology 102 12-2006 C 12 20 Psych 102 01-2007 B 13 15 PSYCH 103 10-2006 a 14 14 Econ 103 11-2006 B 12 15 Econ 103 12-2006 B 16 20 Econ 104 01-2007 A 15 15 Decker School of Nursing ; run; proc print data=stringok; run; data stringweird; input @21 majorcap $revers4.; cards; 101 10-2006 A 12 14 Economics 101 11-2006 A 15 15 Econ 101 12-2006 B 15 20 economics 101 1-2007 a 15 15 econ 102 10-2006 c 10 14 Psychology 102 12-2006 C 12 20 Psych 102 01-2007 B 13 15 PSYCH 103 10-2006 a 14 14 Econ 103 11-2006 B 12 15 Econ 103 12-2006 B 16 20 Econ 104 01-2007 A 15 15 Decker School of Nursing ; run; proc print data=stringweird;

run;

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Session 7 No data for today. We'll use datasets in the SASHELP library, or files that we explicitly create in class. Be sure to look at the references (listed at 'Essential Readings') for functions and functions by category (to find those listed for SAS Dates and Times). We'll be speaking about date/time related Formats, and Informats today as well. To look at what's possible with SAS graphics the SAS/Graph overview is an excellent place to start. [edit]

In class data test; x=today(); x1=today()+1; x2='3

SAS Certification Examples From BingWiki Jump to: navigation, search Back to SAS Certification Examples and Exercises (Part 1)

Contents [hide] •

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1 Session 1 o 1.1 Example 1 o 1.2 Example 2 2 Session 2 o 2.1 Example 1 o 2.2 Example 2 o 2.3 Example 3 o 2.4 Example 4 o 2.5 Example 5 o 2.6 Example 6

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o 2.7 Example 7 3 Session 3 o 3.1 Example 1 o 3.2 Example 2 o 3.3 Example 3 o 3.4 Example 4 4 Session 4 o 4.1 Examples (part 1) o 4.2 Example 2 (skipped) o 4.3 Example 3 (actually just a part 2) o 4.4 Example 4 o 4.5 Example 5 o 4.6 Example 6 5 Session 5 o 5.1 Example 1 o 5.2 Example 2 o 5.3 Example 2a o 5.4 Example 2b o 5.5 Example 3 o 5.6 Example 4 o 5.7 Example 4a o 5.8 Example 5

6 SAS_Certification_Examples(part_2) [edit]

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Session 1 You can select and copy each one of the following examples and paste them directly into your SAS Editor window. They should each run without errors. [edit]

Example 1 data test; input x y; cards; 12 34 5 80 ; run; •

Notice that a dataset is created, but no output is produced. What's missing from the program?

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The dataset has 3 observations and 2 variables.

After the line that says 5 80 add a new line that says 6.

Run the whole program again. Notice that SAS does not produce an error message. [edit]

Example 2 Taken from the SAS Online Documentation, Example from the PROC REG procedure. data fitness; input Age Weight Oxygen RunTime RestPulse RunPulse MaxPulse @@; datalines; 44 89.47 44.609 11.37 62 178 182 40 75.07 45.313 10.07 62 185 185 44 85.84 54.297 8.65 45 156 168 42 68.15 59.571 8.17 40 166 172 38 89.02 49.874 9.22 55 178 180 47 77.45 44.811 11.63 58 176 176 40 75.98 45.681 11.95 70 176 180 43 81.19 49.091 10.85 64 162 170 44 81.42 39.442 13.08 63 174 176 38 81.87 60.055 8.63 48 170 186 44 73.03 50.541 10.13 45 168 168 45 87.66 37.388 14.03 56 186 192 45 66.45 44.754 11.12 51 176 176 47 79.15 47.273 10.60 47 162 164 54 83.12 51.855 10.33 50 166 170 49 81.42 49.156 8.95 44 180 185 51 69.63 40.836 10.95 57 168 172 51 77.91 46.672 10.00 48 162 168 48 91.63 46.774 10.25 48 162 164 49 73.37 50.388 10.08 67 168 168 57 73.37 39.407 12.63 58 174 176 54 79.38 46.080 11.17 62 156 165 52 76.32 45.441 9.63 48 164 166 50 70.87 54.625 8.92 48 146 155 51 67.25 45.118 11.08 48 172 172 54 91.63 39.203 12.88 44 168 172 51 73.71 45.790 10.47 59 186 188 57 59.08 50.545 9.93 49 148 155 49 76.32 48.673 9.40 56 186 188 48 61.24 47.920 11.50 52 170 176 52 82.78 47.467 10.50 53 170 172 ; run; proc reg data=fitness; model Oxygen=RunTime; run;

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Session 2 To understand the differences in the SAS Statments-INPUT and INFILE, See the following examples. [edit]

Example 1 The INFILE statement is to identify an external file. In SAS, it will be like: FILENAME CAT 'C:\USERS\CAT.DAT'; DATA PETS;

INFILE CAT; INPUT ID $ 1-4 AGE 6-7 SEX $ 8; RUN;

Notice that the CAT is the name of your external file. Note: DATA statement is to NAME a SAS data set. [edit]

Example 2 The INPUT statment is to describe your data. In SAS, it will be like: FILENAME CAT 'C:\USERS\CAT.DAT'; DATA PETS; INFILE CAT; INPUT ID $ 1-4 AGE 6-7 SEX $ 8; RUN; Notice that the dollar sign idenfities the variable type as character. Since it's meaningless to run the regression if you treat the ID as numeric variable. And the INPUT statement here assigns the character variable ID to the data in columns 1-4, the numeric variable AGE to the data in columns 6-7, the character variable SEX to the data in column 8.

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Example 3 There is a useful statement-OBS which can be used in the INFILE statement. Situation: When you have 1000000 observations in your data set, and you want to take a look at it without reading the entire data file. You can add OBS=n to the INFILE satement, so that you can process only records 1 through n. FILENAME CAT 'C:\USERS\CAT.DAT'; DATA PETS; INFILE CAT OBS=10; INPUT ID $ 1-4 AGE 6-7 SEX $ 8; RUN;

Notice that you only run the first ten observations here. [edit]

Example 4 The data statements of this example make use of a new feature called direct referencing (version 9.1). By using this new feature we avoid the added step of using a FILENAME statement. Note: this example won't run without errors since in class we used File Import in the SAS menu to create a WORK.Class1 dataset.

Here is the raw text that belongs in a file called class1.txt in the C:\Temp folder (from an Excel spreadsheet). id

age name 1 19George 2 20Mary 3 21Xena 4 21Juan proc print data=class1 (firstobs=3 obs=4); var id age; run; proc means data=class1; run;

data class2; infile 'c:\Temp\class1.txt' firstobs=2 truncover; input id 1-8 age 9-16 name $ 17-24; run; proc print data=class2; run; data 'c:\Temp\plato.sas7bdat'; set class1; run; data test; input x y; cards; 12 34 5 80 6 16000 ; run; data 'c:\Temp\test.sas7bdat'; set test; run;

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Example 5 FIRSTOBS: To specify which observation SAS processes first. data test; input x y z; cards; 12 4 34 7 5 80 3 6 20 2 9 30 1

; run; proc print data=test (firstobs=2); run;

Run the whole example in your computer and notice that you only print out the last four observations in output. [edit]

Example 6 MISSOVER: To prevent an INPUT statement from reading a new input data record if it does not find values in the current input line for all the variables in the statement. --from SDLEo data scores; infile datalines missover; input score1-score5; datalines; 90 98 98 80 100 98 70 78 20 50 90 30 60 ; run; proc print data=scores; run;

Run the above program and notice the missing value in the ouput window. [edit]

Example 7 Change the option-MISSOVER to FLOWOVER and TRUNCOVER in the previous example, and see what's the difference. [edit]

Session 3 [edit]

Example 1 data test; input f1; cards; 1 2 3000 0.0004 0.0005

6.6 70 365 366 367 ; run; data test2; set test; f2=f1; f3=f1; f4=f1; f5=f1; format f2 8.2 ; format f3 date9. ; format f4 dollar8.2 ; format f5 8.0 ; run; proc print data=test2; run;

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Example 2 Great white sharks (Wikipedia article) are killing machines that have not needed to evolve for millions of years. An extended example. The data records the average length in feet of selected whales and sharks. data sealife; input name $ family $ length ; datalines; beluga whale 15 whale shark 40 basking shark 30 gray whale 50 mako shark 12 sperm whale 60 dwarf shark .5 whale shark 40 humpback . 50 blue whale 100 killer whale 30 ; run; proc means data=sealife mean; var length; run; proc means data=sealife min max; class family;

var length; run; proc sort data=sealife out=sortedlife; by descending length ; run; proc print data=sortedlife; var name family length; run; proc sort data=sealife out=sealife2; by family descending length ; run; proc print data=sealife2; var name family length; run; proc means data=sealife noprint nway; class family; var length; output out=ds35; run; proc sort data=sealife out=sl2; by family; run; proc means data=sl2; var length; by family; run;

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Example 3 Using the fitness example from the earlier session we looked at a common usage for PROC SORT data fit2; set fitness; * Here's a simple calculation that will give us two age groups; agecat=1; /* The 'young' age category */ if (age>50) then agecat=2; /* 'old' */ run; proc print data=fit2; var age agecat Oxygen RestPulse; run; proc sort data=fit2 out=jerry; by agecat; run; proc corr data=jerry;

var Oxygen RestPulse; by agecat; run;

A separate Correlation report is produced for each Age category. [edit]

Example 4 Candy sales data, similar to the data mentioned in The Little SAS Book, Section 4.4 (light blue edition, page 107). Name ClassRm Month Day Year Candy Quantity Adriana 21 3 2 2000 MP 7 Nathan 14 2 28 2000 CD 19 Matthew 14 3 1 2000 CD 14 Claire 14 3 3 2000 CD 11 Caitlin 21 2 24 2000 CD 9 Ian 21 3 3 2000 MP 18 Chris 14 2 18 2000 CD 6 Anthony 21 6 1 2000 MP 13 Stephen 14 3 25 2000 CD 10 Erika 21 3 25 2000 MP 17

Briefly editting the text above we wrote the program to illustrate two ways to use PROC FREQ. data candy; input Name $ ClassRm Month Day Year Candy $ Quantity ; cards; Adriana 21 3 2 2000 MP 7 Nathan 14 2 28 2000 CD 19 Matthew 14 3 1 2000 CD 14 Claire 14 3 3 2000 CD 11 Caitlin 21 2 24 2000 CD 9 Ian 21 3 3 2000 MP 18 Chris 14 2 18 2000 CD 6 Anthony 21 6 1 2000 MP 13 Stephen 14 3 25 2000 CD 10 Erika 21 3 25 2000 MP 17 ; run; proc freq data=candy; tables ClassRm Candy; run; proc freq data=candy; tables ClassRm*Candy /nopercent norow; run;

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Session 4

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Examples (part 1) Height/Weight data showing very basic ODS statement usage data htwt; input Name $ 1-10 Sex $ 12 Age 14-15 Height 17-18 Weight 20-22; datalines; ALFRED M 14 69 112 ALICE F 13 56 84 BARBARA F 14 62 102 BERNADETTE F 13 65 98 HENRY M 14 63 102 JAMES M 12 57 83 JANE F 12 59 84 JANET F 15 62 112 JEFFREY M 13 62 84 JOHN M 12 59 99 JOYCE F 11 51 50 JUDY F 14 64 90 LOUISE F 12 56 77 MARY F 15 66 112 PHILLIP M 16 72 150 ROBERT M 12 64 128 RONALD M 15 67 133 THOMAS M 11 57 85 WILLIAM M 15 66 112 ; run; proc corr data=htwt; var height weight; run; ods listing close; proc print data=htwt; var height weight; run;

Notice the error message that occurs when there is no active Output Destination (above). The following is a simple, very controlled way of directing where the output from a procedure should go ... and in what format you'd like to make the output. Other popular choices for output formats are: • •

PDF RTF (Rich Text Format)

ods html file='c:\Temp\corr.html'; title 'The relationship between heights and weights'; proc corr data=htwt; var height weight; run;

ods html close;

ODS and function examples using Tomato data (from the Little SAS Book, 3rd edition, Section 5.3) ods html file='c:\Temp\print2.html'; ods listing; /* Turns the listing output destination back on */ data tomatoes; input name $13. color $ Days Weight ; cards; Big Zac red 80 5 Delicious red 80 3 Dinner Plate red 90 2 Goliath red 85 1.5 Mega Tom red 80 2 Big Rainbow yellow 90 1.5 Pineapple yellow 85 2 ; run; proc print data=tomatoes; run; ods trace on; proc corr data=tomatoes; var Days Weight; run; ods trace off; * Examine the LOG to see what the names are for the various output components; ods trace on; proc corr data=tomatoes nosimp; /* This nosimp option reduces the number of tables produced */ var Days Weight; run; ods trace off; proc corr data=tomatoes; ods select Corr.PearsonCorr; var Days Weight; run;

Now, on to limiting the observations output by a datastep, functions and creating new variables. data tom2; set tomatoes; if (Days>80); *if (Days