# Technology Short Courses: Spring 2010 Kentaka Aruga

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Technology Short Courses: Spring 2010 Kentaka Aruga
SAS　Statistics Technology Short Courses: Spring 2010 Kentaka Aruga

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) SAS can perform lots of statistical analysis such as simple descriptive statistics, ANOVA, ANCOVA, logistic regression, Multivariate analysis, and so on but this course will focus mainly on linear regression analysis.

Getting data and importing data
Section 1 Preparation Getting data and importing data

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

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;

Then highlight the command line and execute the command.
Either right click on where you highlighted and click “submit selection” or click the “submit” icon

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)

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

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; run; Execute this command ADDED

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

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.

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.

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.

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

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;

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”?

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

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”?

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

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;

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

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. Coefficient of determination = square of the correlation coefficient

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

Rho(XY)=COV(X,Y)/(VAR(X)*VAR(Y))^(1/2)
The low p-value indicates a strong negative linear relationship between weight and mpg. The heavier the car is the lower the mpg becomes.

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

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.” Expected value of each cell is based on the independence between row and column variables.

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.

run; Q2 proc freq data=vote; tables answers*gender /expected chisq;

Results

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). This is lower than 0.01

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.

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:

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?

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

The first table shows the basic statistics
See here High high p-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. For table 3, the test for the mpg variance equality yields an F value of 1.18 with a p-value of Thus the result indicates equal variance. Look for a T value based on the assumption of equal mpg variance. Then looking at table two the null hypothesis of equal mean is not rejected at even 10% significance level (P-value is ). T-test statistics = which is not significant at α=0.05, indicating no significant difference in the mpg values between the domestic and the foreign cars.

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

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 Proc Reg estimates the coefficients (beta’s)

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 none of 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.

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?

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

Proc reg Q3

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; 95% CI means that if the estimation process were repeated for many times 95% of all the calculated intervals would be expected to contain the true parameter value . 95 % PI means that about 95 % of the time, the next measurement you make will be inside this interval

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. No semicolon here

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

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

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"

Useful supports: other useful sites
Online SAS manuals This will automatically link you to Statbookstore: useful site for finding program examples

For further Questions: kentaka@mail.uri.edu