SAS PROGRAMMING

1. SAS Training
Basic
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2. Agenda
2
Introduction to SAS Software Program
Data preparation & Tabulation
Test of Difference: T-test, and ANOVA
Test of Association: Correlation & Regression Analysis

3. 3

4. SAS
• From traditional statistical analysis of variance
and predictive modeling to exact methods and
statistical visualization techniques, SAS/STAT
software is designed for both specialized and
enterprise wide analytical needs. SAS/STAT
software provides a complete, comprehensive set
of tools that can meet the data analysis needs of
the entire organization.
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5. SAS Components
5
SAS Enterprise
Guide
SAS Enterprise
Guide
SAS 9.2SAS 9.2
Graphical user interface application
for some common basic data analysis
tasks.
Graphical user interface application
for some common basic data analysis
tasks.
Command-based application for a
wide variety of data analysis tasks.
Command-based application for a
wide variety of data analysis tasks.

6. SAS Enterprise Guide
• To open the statistical software package SAS
go to the Start Menu >>> All Programs >>>
SAS >>> SAS Enterprise Guide 4.3
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7. SAS 9.2
• To open the statistical software package SAS
go to the Start Menu >> All Program >> SAS
>> SAS 9.2 (English)
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8. What Is SAS Enterprise Guide?
What Is SAS Enterprise Guide? SAS
Enterprise Guide is an easy-to-use Windows
client application that provides these features:
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– access to much of the functionality of SAS
– an intuitive, visual, customizable interface
– transparent access to data
– ready-to-use tasks for analysis and
reporting
– easy ways to export data and results to
other applications
– scripting and automation
– a program editor with syntax completion
and built-in function help

9. Explore the Main Windows
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1
2
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10. Create a Project for This Tutorial
• If SAS Enterprise Guide is not open, start it now. In the
Welcome window, select New Project.
• If SAS Enterprise Guide is already open, select File >>
New Project. If you already had a project open in SAS
Enterprise Guide, you might be prompted to save the
project. Select the appropriate response.
• The new project opens with an empty Process Flow
window.
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11. 1. The Project Tree
• You can use the Project Tree window to manage
the objects in your project. You can delete,
rename, and reorder the items in the project.
You can also run a process flow or schedule a
process flow to run at a particular time.
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12. 2. Workspace and Process Flow Windows
You can have one or more
process flows in your project.
When you create a new project,
an empty Process Flow window
opens. As you add data, run
tasks, and generate output, an
icon for each object is added to
the process flow.
The process flow displays the
objects in a project, any
relationships that exist between
the objects, and the order in
which the objects will run when
you run the process flow.
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13. 3. The Task List
You can use tasks to do
everything from manipulating
data, to running specific
analytical procedures, to
creating reports.
Many tasks are also available as
wizards, which contain a
limited number of options and
can provide a quick and easy
way to use some of the tasks.
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14. Add SAS Data to the Project
• You can add SAS data files
and other types of files,
including OLAP cubes,
information maps, ODBC-
compliant data, and files
that are created by other
software packages, such as
Microsoft Word or
Microsoft Excel.
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15. • SAS Enterprise Guide requires all data that it
accesses to be in table format. A table is a
rectangular arrangement of rows (also called
observations) and columns (also called
variables).
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Name Gender Age Weight
Jones M 48 128.6
Laverne M 58 158.3
Jaffe F . 115.5
Wilson M 28 170.1

16. • a column's type is important because it affects how
the column can be used in a SAS Enterprise Guide
task. A column's type can be either character or
numeric.
• Character variables, such as Name and Gender in
the preceding data set, can contain any values.
Missing character values are represented by a blank.
• Numeric variables, such as Age and Weight in the
preceding data set, can contain only numeric values.
Currency, date, and time data is stored as numeric
variables. Missing numeric values are represented
by a period.
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Name Gender Age Weight
Jones M 48 128.6
Laverne M 58 158.3
Jaffe F . 115.5
Wilson M 28 170.1

17. Local and Remote Data
• When you open data in SAS Enterprise Guide,
you must select whether you want to look for the
data on your local computer, a SAS server, or in
a SAS folder.
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18. Local and Remote Data (Cont’)
• If you click My Computer, you can browse the
directory structure of your computer. You can
open any type of data file that SAS Enterprise
Guide can read.
• If you click Servers, you can look for your data
on a server. A server can either be a local server
if SAS software is installed on your own
computer, or it can be a remote server if SAS
software is installed on a different computer.
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19. Open Data from Server
• Within each server there are icons that you can select for
Libraries and Files. Libraries are shortcut names for
directory locations that SAS knows about. Some libraries
are defined by SAS, and some are defined by SAS
Enterprise Guide. Libraries contain only SAS data sets.
• The Files folder on a server enables you to access data
files in the directory structure on the computer where
the SAS server is running. For example, if you wanted to
open a Microsoft Excel file on a server that is defined in
your repository, you would use the Files node to locate
and open the file.
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20. Open Data from SAS Folders
If you click SAS Folders, you can browse the list
of SAS folders that you can access. SAS folders
are defined in the SAS Metadata Server and can
be used to provide a central location for your
stored processes, information maps, and projects
so that they can be shared with other SAS
applications. SAS folders can also contain content
that is not in the SAS Metadata Server, such as
data files.
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21. Add SAS Data from Your Local Computer
• Select File >> Open >> Data. In the Open Data
window, select My Computer.
• Open the SAS Enterprise Guide samples directory
and double-click Data. By default, the sample
programs, projects, and data are located in
C:Program FilesSASEnterpriseGuide4.3Sample.
By default, all file types are displayed in the
window. Files with the icon are SAS data sets.
Press CTRL and select Orders.sd2 and
Products.sas7bdat, and then click Open.
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22. Add SAS Data from Your Local Computer
(Cont’)
• Shortcuts to
the Products and Orders
tables are added to the
project, and the data sets
open in data grids.
• By default, the tables open in
read-only mode. In this
mode, you can browse, resize
column widths, hide and
hold columns and rows, and
copy columns and rows to a
new table.
• You cannot edit the data in
the table unless you change
to edit mode. Select Edit >>
Remove Protect Data
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23. View the Properties of a Data Set
• In the project tree, right-click Products and
select Properties from the pop-up menu. The
Properties for Products window opens. You can see
information about general properties such as the
physical location of the data and the date it was last
modified.
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24. View the Properties of a Data Set (Cont’)
• In the selection pane, click Columns. Here you
can view a list of columns in your data and the
column attributes.
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25. Add Data from a SAS Library
• Select File >> Open >> Data.
In the Open Data window,
select Servers.
• Double-click Libraries, and
then double-click SASHELP.
As you can see, only SAS data
sets are stored in libraries
• Scroll in the window and
double-click
the PRDSALE data set. A
shortcut to the data is added to
the project and the data opens
in the data grid.
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26. Save the Project
• Select File >> Save
Project As.
• The Save window opens
and prompts you to choose
whether to save the project
on your computer or on a
server. Select My
Computer.
• In the Save window, select a
location for the project. In
the File name box, type
‘your file name’. Project
files are saved with the
extension .egp.
• Click Save.
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27. 27

28. Data Input
• There are two main simple tasks for data input;
▫ Manually Input Data
▫ Import from an External File
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29. Manually Input Data
1. Create a SAS Library
2. Create a SAS Data Set
3. Input data
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30. What is a SAS Data Library?
• A SAS data library is a collection of one or more
SAS files that are recognized by SAS and can be
referenced and stored as a unit. Each file is a
member of the library. SAS data libraries help to
organize your work. For example, if a SAS
program uses more than one SAS file, then you
can keep all the files in the same library.
Organizing files in libraries makes it easier to
locate the files and reference them in a program.
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31. Telling SAS Where the SAS Data
Library Is Located
• directly specify the operating environment's
physical name for the location of the SAS data
library.
• assign a SAS libref (library reference), which is a
SAS name that is temporarily associated with the
physical location name of the SAS data library.
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32. Using Librefs for Temporary and
Permanent Libraries
• When you start a SAS session, SAS automatically
assigns the libref WORK to a special SAS data
library. Normally, the files in the WORK library
are temporary files.
• Files that are stored in any SAS data library
other than the WORK library are usually
permanent files; that is, they endure from one
SAS session to the next. Store SAS files in a
permanent library if you plan to use them in
multiple SAS sessions.
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33. Create a SAS Library
• Tools >> Assign Project Library
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34. Create a SAS Library – Step 1
• Specify name and server for the library
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35. Create a SAS Library – Step 2
• Specify the engine for the library
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36. Create a SAS Library – Step 3
• Specify options for the library
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37. Create a SAS Library – Step 4
• Click Test Library, checking it’s OK to create this library
• Press Finish to create the library
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38. Create a SAS Library
• Check created library at
Server List
• When a libref is assigned to
a SAS data library, you can
use the libref throughout
the SAS session to access
the SAS files that are stored
in that library or to create
new files.
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39. Create SAS Data Set
• File >> New >> Data
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40. Create SAS Data Set – Step 1
• Specify name ‘TEST’ and location ‘DEMO’
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41. Create SAS Data Set – Step 2
• Create columns and specify their properties
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Name Gender Age Weight
Jones M 48 128.6
Laverne M 58 158.3
Jaffe F . 115.5
Wilson M 28 170.1

42. Input Data
42

43. Import from an External File
• The Import Data wizard enables you to create
SAS data sets from text, HTML, or PC-based
database files (including Microsoft Excel,
Microsoft Access, and other popular formats).
When you use the Import Data wizard, you can
specify import options for each file that you
import.
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44. Import Data
• File >> Import Data
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45. Import Data (Cont’)
• Desktop >> SAS Training >> Data Advising
Survey.xls
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46. Import Data (Cont’)
• Specify Data
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47. Import Data (Cont’)
• Select Data Source
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48. Import Data (Cont’)
• Define Field Attributes
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49. Import Data (Cont’)
• Advanced Options
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50. Import Data Result
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51. Import SPSS file
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52. Import SPSS file – Step 1
• Select an SPSS file to import
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53. Import SPSS file – Step 2
• Specify a name for the imported table
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54. Import SPSS file Result
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55. Create Format
• Tasks >> Data >> Create Format
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56. Create Format (Cont’)
• Set Format Name ‘GENDER’
• Select Library - SASUSER
• Select Format Type ‘Character’
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57. Define Formats
• Click New Label and type a name of a label
• Click New Range and select type of values and
type a value according to the specified label
• Repeat the steps
• Click Run
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58. Applying User-Defined Formats
• Open a SAS Data Set
• Unprotect Data: Edit >> Unprotect Data
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59. Applying User-Defined Formats (Cont’)
• Right-click the column
• Select Properties
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60. Applying User-Defined Formats (Cont’)
• In the left pane, select Formats
• In Categories box, select User Defined
• In Formats box, select the desired Formats
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61. Applying Formats in Tasks
• Custom formats can be applied in the same
places that formats defined in SAS can be used.
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62. SAS Tasks
• After you have data in your project, you can
create reports and run analyses on the data.
• To do this, you select a SAS task from the Task
List or from the Tasks menu. Some tasks have
wizards to guide you through the decisions that
you need to make. Wizards are available from
menus or from a link next to the related task in
the Task List.
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63. Using Tasks in SAS Enterprise Guide
• The icon next to each variable
represents the variable's type.
Country is a character
variable ( ). Year is a
numeric variable ( ). Month
is a numeric variable in date-
and-time format ( ). Actual
and Predict are numeric
variables in currency format
( ).
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64. One-Way Frequencies Task
We should create One-Way Frequencies (tables
and graphs) to check our data set one last time
before we intensively analyze the data.
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65. One-Way Frequencies
Under Data, select Q1-Q19, Gender, Nation,
Year, and Major for Analysis variables.
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66. One-Way Frequencies
Under Plots, check Vertical for Bar chart.
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67. One-Way Frequencies
Check Frequency Tables and/or Bar charts for any
errors (e.g., typo). Make necessary correction(s).
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68. Filter and Sort
Use Tasks >> Data >> Filter and Sort... or Sort data...
to help you find the error(s).
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69. Summary Statistics Task
The Summary Statistics task can be used to
calculate summary statistics based on groups
within the data. You can produce reports,
graphs, and data sets as output.
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70. Summary Statistics Task
The Summary Statistics task has both a wizard
and the standard task dialog box that can be
used to set up the results.
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71. Summary Statistics: Task Roles
Use the wizard to assign variables to roles.
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Specify variables whose
values define subgroups.
Compute statistics
for each numeric
variable in the list.

72. Summary Statistics: Statistics and Results
Choose statistics and results to include, including
a report, graphics, and an output data set.
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73. Summary Statistics: Advanced
ViewOpening the task in Advanced View enables
additional options to further modify the output.
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74. Summary Tables
The Summary Tables wizard or task can be used to
generate a tabular summary report.
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75. Summary Tables Wizard
The Summary Tables wizard enables you to select analysis
variable(s) and statistics, assign classification variables
to define rows and columns, and specify totals.
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76. Summary Tables Wizard
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77. 77

78. One-Sample t-Test
• Tasks >> ANOVA >> t Test
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79. • Selected One Sample.
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80. • Under Data, choose Q19 as the Analysis variable
task role and Gender as the Group analysis by.
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81. • Under Analysis, input H0 = 3.
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82. T-Test Output
82
Since p-value is less than
0.05, it can be concluded that
average male students also
consider themselves as a
well-prepared students for
advising appointment
Since p-value is less than
0.05, it can be concluded that
average male students also
consider themselves as a
well-prepared students for
advising appointment
Since p-value is less than
0.05, it can be concluded that
average female students
consider themselves as a
well-prepared students for
advising appointment
(significantly higher than 3).
Since p-value is less than
0.05, it can be concluded that
average female students
consider themselves as a
well-prepared students for
advising appointment
(significantly higher than 3).

83. Two-Sample t-Test
• Tasks >> ANOVA >> t Test
83

84. • Selected Two Sample.
84

85. • Under Data, choose Q6 as the analysis variable
task role and Gender as the classification
variable.
85

86. • Under Plots, check Summary plot,
Confidence interval plot, and Normal
quantile-quantile (Q-Q) plot.
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87. T-Test Output
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the probability is greater than
0.05. So there is evidence
that the variances for the two
groups, female students and
male students, are not
different.
the probability is greater than
0.05. So there is evidence
that the variances for the two
groups, female students and
male students, are not
different.
Equaled variance is assumed.
Pooled method is used. Since
p-value is greater than 0.05,
it cannot be concluded that
there is significant difference
in Advisor Satisfaction
between male and female
students.
Equaled variance is assumed.
Pooled method is used. Since
p-value is greater than 0.05,
it cannot be concluded that
there is significant difference
in Advisor Satisfaction
between male and female
students.

88. One-Way ANOVA
• Tasks >> ANOVA >> One-Way ANOVA
88

89. • Under Data, assign Q6 and Year to the task
roles of Dependent variable and Independent
variable, respectively.
89

90. • Under Tests, click Levene’s test
90

91. • Under Means Comparison, check
Bonferroni t test, Duncan’s multiple-
range test, and Scheffe’s multiple
comparison procedure for Post Hoc tests
91

92. • Under Plots, check Means for Plots Types.
• Then, click Run.
92

93. One-Way ANOVA results
93
Since p-value is greater than 0.05,
it can be concluded that there is no
significant difference in average
Advisor Satisfaction among
year(s) of study. Therefore, there is
no need to check the Post Hoc tests.
Since p-value is greater than 0.05,
it can be concluded that there is no
significant difference in average
Advisor Satisfaction among
year(s) of study. Therefore, there is
no need to check the Post Hoc tests.

94. Post Hoc Test: Bonferroni t Tests
94

95. Post Hoc Test: Scheffe’s Tests
95

96. ANOVA: Means Plot of Q6 by Year
96

97. 97

98. Data Exploration, Correlations,
and Scatter Plots
• Tasks >> Multivariate >> Correlations
98

99. • With Data selected at the left, assign Q1, Q2, Q3, Q4,
and Q5 to the task role of Analysis variables and Q6
to the role of Correlate with.
99

100. Correlation Types
100

101. • In Results, check the box for Create a scatter plot for
each correlation pair. Also, check the box at the right for
Show correlations in decreasing order of magnitude and
uncheck the box for Show statistics for each variable.
101

102. Correlation Analysis
102
• Since p-values are less than 0.05, there are
significant (positive) relationships between Q6
(Overall satisfaction on Advisor) and Q1, Q2,
Q3, Q4, Q5.

103. Linear Regression
• Tasks >> Regression >> Linear Regression
103

104. • Drag Q6 to the dependent variable task role and
Q1, Q2, Q3, Q4, Q5. to the explanatory
variables task role.
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105. Regression: Model
Model Selection Method: Full model fitted (by
default)
105

106. Regression: Statistics
• Under Details on estimates, check Standardized
regression coefficients
• Perform some Diagnostics
106

107. Regression Diagnostics
• Unusual and Influential data (Outliers/Leverage)
• Tests on Normality of Residuals
• Tests on Nonconstant Error of Variance
(Heteroscedasticity)
• Tests on Correlations among Predictors
(Multicollinearity)
• Tests on Nonlinearity
• Tests on Dependence of Residuals
(Autocorrelation)
• Model Specification
107

108. Diagnostics: Collinearity Analysis
• This option requests a detailed analysis of
collinearity among the regressors. This includes
eigenvalues, condition indices, and
decomposition of the variances of the estimates
with respect to each eigenvalue.
108

109. Diagnostics: Collinearity Analysis
• Check Tolerance (1/VIF) or Variance Inflation (VIF)
• Some researchers use the more lenient cutoff of 5.0 or even
10.0 to signal when multicollinearity is a problem. The
researcher may wish to drop the variable with the highest VIF
if multicollinearity is indicated and theory warrants.
• The condition indices are the square roots of the ratio of the
largest eigenvalue to each individual eigenvalue. The largest
condition index is the condition number of the
scaled X matrix. Belsey, Kuh, and Welsch (1980) suggest that,
when this number is around 10, weak dependencies might be
starting to affect the regression estimates. When this number
is larger than 100, the estimates might have a fair amount of
numerical error (although the statistical standard error almost
always is much greater than the numerical error).
109

110. Diagnostics: Heteroscedasticity Test
• This option tests that the first and second
moments of the model are correctly specified.
• Asymptotic covariance matrix. This option
displays the estimated asymptotic covariance
matrix of the estimates under the hypothesis of
heteroscedasticity.
110

111. Diagnostics: Durbin-Watson Statistic
• The Durbin-Watson statistic shows whether or not the
errors have first-order autocorrelation. (This test is
appropriate only for time series data.) The sample
autocorrelation of the residuals is also produced.
• The value of d ranges from 0 to 4. Values close to 0
indicate extreme positive autocorrelation; close to 4
indicates extreme negative autocorrelation; and close to
2 indicates no serial autocorrelation. As a rule of thumb,
d should be between 1.5 and 2.5 to indicate
independence of observations. Positive autocorrelation
means standard errors of the b coefficients are too small.
Negative autocorrelation means standard errors are too
large.
111

112. • Under Plots, select Custom list of plots under Show plots
for regression analysis. In the menu that appears, uncheck
the box for Diagnostic plots and check the box for
Histogram plot of the residual, Normal quartile
plot of the residual and Residual plots.
112

113. Regression Analysis
113
These are the F Value and
p-value, respectively,
testing the null hypothesis
that the Model does not
explain the variance of
the response variable.
These are the F Value and
p-value, respectively,
testing the null hypothesis
that the Model does not
explain the variance of
the response variable.
R-Square defines the
proportion of the total
variance explained by
the Model.
R-Square defines the
proportion of the total
variance explained by
the Model.

114. Regression Analysis
114
These are the t Value and
p-value, respectively,
testing the null hypothesis
that the coefficients are
significantly equal to 0.
These are the t Value and
p-value, respectively,
testing the null hypothesis
that the coefficients are
significantly equal to 0.

115. Regression: Diagnostics
115
Might suggest violation
of normality of residuals
assumption
Might suggest violation
of normality of residuals
assumption

116. Regression: Diagnostics
116
Might suggest violation
of normality of residuals
assumption
Might suggest violation
of normality of residuals
assumption

117. Regression: Diagnostics
117

118. Q&A
118