SAS Statistics

1. SASStatistics Technology Short Courses: Spring2010 KentakaAruga

2. Object of the course • Performing simple descriptive statistics (proc mean, proc freq, and proc corr) • Performing basic test statistics (Chi-square test, T-test, F-test) • Basic commands for regression analysis and how to export the result into a table (proc reg)

3. Section 1 Preparation Getting data and importing data

4. Getting data • Download the SAS command that will be used in this practice from http://www.uri.edu/its/research/sasstat.txt • Download the data file that will be used in this course from http://www.uri.edu/its/research/auto.xls http://www.uri.edu/its/research/vote.txt • Save the files under ‘C:/’ drive of your windows computer.

5. Importing Excel file to SAS • Open SAS program and copy and paste the following commands from the file you have just downloaded “sasstat.txt”: libname car ‘c:/’; proc import out= car.auto datafile=“c:/auto.xls” dbms=excel2000 replace; sheet=“auto”; getnames=yes; run;

7. Then highlight the command line and execute the command.

8. Proc import • Look at the ‘trunk’ column • Do you see an empty column? • SAS determines the data type based on the most common data type in the first 8 rows. ‘trunk’ column has mixed data.(since the first eight columns are all zero, the remaining columns become all zero)

10. ADDED Proc import • Add the following statement mixed = yes; • Now the command line should look like proc import out= car.auto datafile=“c:/auto.xls” dbms=excel2000 replace; sheet=“auto”; getnames=yes; mixed = yes; run; • Execute this command

12. Importing Excel file from the main menu bar • From the main menu click “File,” and then click “Import Data.”

13. Importing Excel file from the main menu bar • Under the “Import Wizard” specify the data source (in this example select MS Excel) and click next. • Under the “Connect to MS Excel” wizard, browse the Excel file you are importing.

14. Importing Excel file from the main menu bar • Under the “Select Table” wizard select the name of the “sheet” of your Excel file and click next. • Under the “Select library and member” wizard, specify the library where you want to import the Excel file. • Put in the name of the file in the “Member” box to name the file that will be imported to SAS.

15. Saving the syntax for importing Excel file • You can save the syntax for what we just did to import the Excel file using the main menu bar. • Browse and name the file in “Create SAS Statements” wizard. • Open the “sas” file you just saved to see the commands.

16. Section 2 Performing simple descriptive statistics (proc mean, proc freq, and proc corr)

17. How to perform simple descriptive statistics (Review from SAS basics course) • How would you see the number of obvs, mean, std, min, and max of all numeric variables in SAS? Ans. proc means data=car.auto; run; • How do you analyze frequency of the variables? Ans. proc freq data=car.auto; run;

18. Proc means • By default “proc means” provides the number of obvs, mean, std, min, and max of all numeric variables proc means data=car.auto; run; • Specifying a certain variable • var variable name ; Q. How would you execute the mean procedure for the variables “price”, “mpg,” and “weight” ? • Creating an output table • output out= file name Q. How would you get the output for the mean procedure for the variables “price”, “mpg,” and “weight”?

19. Proc means (Answers) procmeans data=car.auto; output out=car.means; var price mpg weight; run;

20. Proc freq • By default this procedure creates frequency tables for all variables proc freq data=car.auto; run; • Specifying a certain variable • tables variable name Q. How would you execute the FREQ procedure for the variable “foreign”? • Creating an output table • /out = file name Q. How would you get the output for the FREQ procedure for the variable “foreign”?

21. Proc freq (Answers) proc freq data=car.auto; tables foreign /out=car.frn; run;

22. Proc freq: Creating a two-way table • How would you create a two-way table using the FREQ procedure for the variables “rep78” and “foreign”? Ans. proc freq data=sasuser.auto; tables rep78*foreign; run;

23. Total % (= 8/13) Row % (= 8/9) Column % (= 8/10) Proc freq: two-way table

24. Proc corr • The CORR procedure generates ‘Simple Statistics’ based on non missing values, and ‘Pearson Correlation Coefficient’, an index that quantifies the linear relationship between a pair of variables • Insignificant p-value indicates the lack of linear relationship between the two variables.

25. Proc corr • Finding correlations between a pair of variables 1) All variables proc corr data=car.auto; run; 2) Three specific variables proc corr data=car.auto; var price mpg weight; run;

26. The low p-value indicates a strong negative linear relationship between weight and mpg. The heavier the car is the lower the mpg becomes.

27. Section 3 Performing basic test statistics (Chi-square test, T-test, F-test)

28. Chi-square test of independence • What is the Chi-square test of independence? Ans. It tests whether the variable in the row and column are independent or related • What is the null hypothesis? Ans. The variables in the row and column are independent: there is no relationship between row and column frequencies • The command for SAS to test this is provided in the option of “proc freq”. Simply use chisq. • To display the expected cell frequency for each cell use the option “expected.”

29. Chi-square test of independence: exercise There are 34 students in the classroom and there was a vote on whether they wanted to have a turtle in their classroom as a pet. The data file “vote.txt” contains the result of the vote (Yes=y, No=n), and gender of the students (male=m, female=f). • Q1 Import the file “vote.txt” into SAS and name the variables “answers” and “gender.” • Q2 Using the option “chisq,” test whether or not the answers to the vote and gender are associated with each other.

30. Answers Q1 data vote; infile 'c:/vote.txt'; input answers $ gender $; run; Q2 procfreq data=vote; tables answers*gender /expected chisq; run;

31. Results

32. This is lower than 0.01 What does the result tell you? • The null hypothesis that the two variables are independent is rejected at even 1% significance level. • The two variables “answers” and “gender” are associated with each other (They are dependent).

33. Proc ttest • This procedure is used to test the hypothesis of equality of means for two normal populations from which independent samples have been obtained. • Three cases in SAS • One-sample t-test • Computes the sample mean of the variable and compares it with a given number. • Two-sample t-test • Compares the mean of the first sample minus the mean of the second sample to a given number. • Pair observations t-test • Compares the mean of the differences in the observations to a given number.

34. Assumptions of “proc ttest” • The observations are random samples drawn from normally distributed populations. This can be tested using the UNIVARIATE procedure • If the normality assumptions are not satisfied: use NPAR1WAY procedure. • Two populations of a group comparison must be independent. • If not independent, you should question the validity of a paired comparison. • The default null hypothesis is set as equal to zero. To change this you can use H0=‘number’.” e.g. h0=10 • The default confidence level is 5%. To change this you can use alpha=‘confidence level’.” e.g. alpha=0.01 Source: http://www.okstate.edu/sas/v8/saspdf/stat/chap67.pdf

35. Proc ttest: exercise • How would you perform a t-test on mpg variable classified by foreign variable? Hint: use “class” and “var” statement • What will the null hypothesis be in this case?

36. Proc ttest (Cont’d) • The command proc ttest data=car.auto; class foreign; var mpg; run; • CLASS statement: contains a variable that distinguishes the groups being compared. • VAR statement: specifies the response variable to be used in calculations. • The null hypothesis • The alternative hypothesis

37. See here Highhighp-value • The first table shows the basic statistics • The second table is the t-test for equal mean. Before using this table you need to look at the third table to determine if the assumption of equal variances is reasonable • The third table is a test of equal variances • In this example the null hypothesis of equal variance is not rejected. • Thus you need to look at the “equal variance” in the second table. The second table suggests there is not a difference in means across domestic and foreign car.

38. Section 4 Basic commands for regression analysis and how to export the result into a table (proc reg)

39. Regression analysis • Regression analysis : finding a reasonable mathematical model of the relationship between a response variable (y) and a set of explanatory variables (x1, x2,…. xP) • General model

40. Proc reg • General command proc reg data = file name model DV = IV ; run; DV: dependent variable IV: independent variable • This procedure also does the following testing: • F-test: Tests the null hypothesis that noneof the independent variables has any effect • T-test Tests for each IV the null hypothesis that the independent variable has no effect toward the dependent variable.

41. Proc reg: exercise • Let ‘price’ be a response variable (dependent variable (DV)), and ‘mpg’ and ‘length’ be explanatory variables (independent variables (IV)) Q1 What will be the commands? Q2 What null hypotheses will be tested? Q3 Will the model be significant?

42. Proc reg: answers Q1 proc reg data = car.auto; model price = mpg length; run; Q2 F-test T-test

43. Proc reg Q3

44. Proc reg: Confidence and prediction interval • Constructing 95% confidence and prediction interval by adding two options, ‘clm’ and ‘cli’ • How would you add these options in the case of previous model? proc reg data=car.auto; model price = mpg length / clm cli; run;

45. No semicolon here Proc reg: creating an output table • Add “outest = file name” after the “proc reg” command proc reg data=car.auto outest=car.est1; model price = mpg length /clm cli; run; quit; • In order to see the output data file “car.est1” you need to add the statement “quit” in the end.

46. You can drop the categories you do not want to see by using the “keep” or “drop” statement e.g. data car.est2 (keep=intercept mpg length); set car.est1; run; data car.est3 (drop=price _model_ _depvar_ _type_ _RMSE_); set car.est1; run;

47. Click “Syntax” Proc reg: creating an output table • To see other outputs go to “Help” and type in “REG” and go into “The REG procedure.”

48. Click Here

50. Exporting the output data to Excel • General commands proc export data = Name of the SAS data file you are exporting outfile = “The name of the drive or the pass to the folder of your computer” dbms = excel2000 replace; run; • How would you export the file “car.est2” into an Excel file? Ans.proc export data = car.est2 outfile = “c:/est.xls" dbms = excel2000 replace; run;