
1
Presentation and Data

http://www.lisa.stat.vt.edu

Short Courses

Intro to SAS

Download Data to Desktop
Introduction to SAS
Mark Seiss, Dept. of Statistics
November 8 and 9, 2010
Reference Material
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The Little SAS Book – Delwiche and Slaughter

SAS Programming I: Essentials


SAS Programming II: Manipulating Data with the
DATA Step
Presentation and Data

http://www.lisa.stat.vt.edu
Presentation Outline
1.
Introduction to the SAS Environment
2.
Working With SAS Data Sets
3.
Summary Procedures
4.
Basic Statistical Analysis Procedures
Presentation Outline
Questions/Comments
Individual Goals/Interests
Introduction to the
SAS Environment
1.
2.
3.
4.
SAS Programs
SAS Data Sets and Data Libraries
SAS System Help
Creating SAS Data Sets
SAS Programs
• File extension - .sas
• Editor window has four uses:
• Access and edit existing SAS programs
• Write new SAS programs
• Submitting SAS programs for execution
• Saving SAS programs
• SAS program – sequence of steps that the user submits for
execution
• Submitting SAS programs
• Entire program
• Selection of the program
SAS Programs
• Syntax Rules for SAS statements
• Free-format – can use upper or lower case
• Usually begin with an identifying keyword
• Can span multiple lines
• Always end with a semicolon
• Multiple statements can be on the same line
• Errors
• Misspelled key words
• Missing or invalid punctuation (missing semi-colon common)
• Invalid options
• Indicated in the Log window
SAS Programs
• 2 Basic steps in SAS programs:
• Data Steps
• Typically used to create SAS datasets and manipulate data,
• Begins with DATA statement
• Proc Steps
• Typically used to process SAS data sets
• Begins with PROC statement
• The end of the data or proc steps are indicated by:
• RUN statement – most steps
• QUIT statement – some steps
• Beginning of another step (DATA or PROC statement)
SAS Programs
• Output generated from SAS program – 2 Windows
• SAS log
• Information about the processing of the SAS program
• Includes any warnings or error messages
• Accumulated in the order the data and procedure steps are
submitted
• SAS output
• Reports generated by the SAS procedures
• Accumulates output in the order it is generated
SAS Data Sets and Data Libraries
• SAS Data Set
• Specifically structured file that contains data values.
• File extension - .sas7bdat
• Rows and Columns format – similar to Excel
• Columns – variables in the table corresponding to fields of data
• Rows – single record or observation
• Two types of variables
• Character – contain any value (letters, numbers, symbols, etc.)
• Numeric – floating point numbers
• Located in SAS Data Libraries
SAS Data Sets and Data Libraries
• SAS Data Libraries
• Contain SAS data sets
• Identified by assigning a library reference name – libref
• Temporary
• Work library
• SAS data files are deleted when session ends
• Library reference name not necessary
• Permanent
• SAS data sets are saved after session ends
• SASUSER library
• You can create and access your own libraries
SAS Data Sets and Data Libraries
• SAS Data Libraries cont.
• Assigning library references
• Syntax
LIBNAME libref ‘SAS-data-library’;
• Rules for Library References
• 8 characters or less
• Must begin with letter or underscore
• Other characters are letters, numbers, or under scores
SAS Data Sets and Data Libraries
• SAS Data Libraries cont.
• Identifying SAS data sets within SAS Data Libraries
libref.filename
• Accessing SAS data sets within SAS Data Libraries
Example: DATA new_data_set;
set libref.filename;
run;
• Creating SAS data sets within SAS Data Libraries
Example: DATA libref.filename;
set old_data_set;
run;
SAS System Help
• SAS Help and Documentation
• Help  SAS Help and Documentation
• Red Book Icon
• SAS Online Help
• http://support.sas.com/
Creating SAS Data Sets
• Creating a SAS data sets from raw data
• 4 methods
1.
Importing existing data sets using Import menu option
2.
Importing existing raw data in SAS program
3.
Manually entering raw data in SAS program
4.
Manually entering raw data using Table Editor
Creating SAS Data Sets
• Using the import data menu option
1.
File  Import Data
2.
Standard data source  select the file format
3.
Specify file location or Browse to select file
4.
Create name for the new SAS data set and specify location
Creating SAS Data Sets
• Compatible file formats
•
Microsoft Excel Spreadsheets
•
Microsoft Access Databases
•
Comma Separate Files (.csv)
•
Tab Delimited Files (.txt)
•
dBASE Files (.dbf)
•
JMP data sets
•
SPSS Files
•
Lotus Spreadsheets
•
Stata Files
•
Paradox Files
Creating SAS Data Sets
•
Example Data Sets
• Excel File – State_SAT_data.xls
• http://www.stat.ucla.edu/labs/datasets/sat.dat
• Extracted from 1997 Digest of Education Statistics, an annual
publication of the U.S. Department of Education
• Contains variables that show the relationship between public
school expenditure and SAT performance
• Variables:
–
–
–
–
–
–
–
–
State (state)
Current expenditure per pupil (expend)
Average pupil to teacher ratio (PT_ratio)
Estimated annual salary of teachers (salary)
Percentage of eligible students taking the SAT (students)
Average verbal SAT score (verbal)
Average math SAT Score (math)
Average total score (total)
Creating SAS Data Sets
• Example Data Sets Cont.
• Text file – State_region_data.txt
•
•
•
•
•
•
•
•
•
•
Contains region assignments for each state
1 = New England
2 = Middle Atlantic
3 = East North Central
4 = West North Central
5 = South Atlantic
6 = East South Central
7 = West South Central
8 = Mountain
9 = Pacific
Creating SAS Data Sets
Import State_SAT_data.xls  Assign as
work.state_sat_data.sas7bdat
Import State_region_data.txt  Assign as
work.state_region_data.sas7bdat
Introduction to the
SAS Environment
Questions/Comments
Working With SAS Data Sets
1.
2.
3.
4.
Data Set Information
Data Set Manipulation
Data Set Processing
Combining Data Sets
A. Concatenating/Appending
B. Merging
5. Saving Data Sets
Data Set Information
• Proc Contents
• Output contains a table of contents of the specified data set
• Data Set Information
• Data set name
• Number of observations
• Number of Variables
• Variable Information
• Type (numeric or character)
• Length
• Syntax:
PROC CONTENTS DATA=input_data_set;
RUN;
Data Set Information
Assignment
Obtain Data Set Information for work.state_sat_data and
work.state_region_data
Data Set Information
Solution
proc contents data=state_sat_data;
run;
proc contents data=state_region_data;
run;
Data Set Manipulation
• Create a new SAS data set using an existing SAS data set as
input
• Specify name of the new SAS data set after the DATA statement
• Use SET statement to identify SAS data set being read
• Syntax:
DATA output_data_set;
SET input_data_set;
<additional SAS statements>;
RUN;
• By default the SET statement reads all observations and variables
from the input data set into the output data set.
Data Set Manipulation
• Assignment Statements
• Evaluate an expression
• Assign resulting value to a variable
• General Form: variable = expression;
• Example: miles_per_hour = distance/time;
• SAS Functions
• Perform arithmetic functions, compute simple statistics, manipulate
dates, etc.
• General Form: variable=function_name(argument1, argument2,…);
• Example: Time_worked = sum(Day1,Day2, Day3, Day4, Day5);
Data Set Manipulation
• Selecting Variables
• Use DROP and KEEP to determine which variables are written to
new SAS data set.
• 2 Ways
• DROP and KEEP as statements
– Form: DROP Variable1 Variable2;
KEEP Variable3 Variable4 Variable5;
• DROP and KEEP options in SET statement
– Form: SET input_data_set (KEEP=Var1);
Data Set Manipulation
• Conditional Processing
• Uses IF-THEN-ELSE logic
• General Form: IF <expression1> THEN <statement>;
ELSE IF <expression2> THEN <statement>;
ELSE <statement>;
• <expression> is a true/false statement, such as:
• Day1=Day2, Day1 > Day2, Day1 < Day2
• Day1+Day2=10
• Sum(day1,day2)=10
• Day1=5 and Day2=5
Data Set Manipulation
• Conditional Processing
Symbolic
Mnemonic
=
EQ
IF region=‘Spain’;
~= or ^=
NE
IF region ne ‘Spain’;
>
GT
IF rainfall > 20;
<
LT
IF rainfall lt 20;
>=
GE
IF rainfall ge 20;
<=
LE
IF rainfall <= 20;
&
AND
IF rainfall ge 20 & temp < 90;
| or !
OR
IF rainfall ge 20 OR temp < 90;
IS NOT
MISSING
BETWEEN AND
CONTAINS
IN
Example
IF region IS NOT MISSING;
IF region BETWEEN ‘Plain’ AND ‘Spain’;
IF region CONTAINS ‘ain’;
IF region IN (‘Rain’, ‘Spain’, ‘Plain’);
Data Set Manipulation
• Conditional Processing cont.
• If <expression1> is true, <statement> is processed
• ELSE IF and ELSE are only processed if <expression1> is false
• Only one statement specified using this form
• Use DO and END statements to execute group of statements
• General Form: IF <expression> THEN DO;
<statements>;
END;
ELSE DO;
<statements>;
END;
Data Set Manipulation
• Subsetting Rows (Observations)
• We will look at two ways
• Using IF statement
• Using WHERE option in SET statement
• IF statement
• Only writes observations to the new data set in which an
expression is true;
• General Form: IF <expression>;
• Example: IF career = ‘Teacher’;
IF sex ne ‘M’;
• In the second example, only observations where sex is not equal
to ‘M’ will be written to the output data set
Data Set Manipulation
• Subsetting Rows (Observations) cont.
• Where Option in SET statement
• Use option to only read rows from the input data set in which the
expression is true
• General Form: SET input_data_set (where=(<expression>));
• Example: SET vacation (where=(destination=‘Bermuda’));
• Only observations where the destination equals ‘Bermuda’ will be
read from the input data set
• Comparison
• Resulting output data set is equivalent
• IF statement – all rows read from the input data set
• Where option – only rows where expression is true are read from
input data set
• Difference in processing time when working with big data sets
Data Set Manipulation
• Assignments
1. Create new dataset work.state_SAT_data2 from
work.state_SAT_data
Assign new variable  upper_ind
If total > 1000 then upper_ind=1
Otherwise upper_ind=0
2. Create new dataset work.south from work.state_region_data
Specify work.south contains only records from regions 5, 6,
or 7
Specify work.south only contains the state variable
Data Set Manipulation
• Solutions
1.
data state_sat_data2;
set state_sat_data;
if total>1000 then upper_ind=1;
else upper_ind=0;
run;
Data Set Manipulation
• Solutions
2.
data south;
set state_region_data;
if region=5 or region=6 or region=7;
keep state;
run;
OR
data south;
set state_region_data(where=(region=5 or region=6 or region=7));
keep state;
run;
Data Set Manipulation
•
PROC SORT sorts data according to specified variables
•
General Form:
•
Sorts data according to Variable1 and then Variable2;
•
By default, SAS sorts data in ascending order
•
•
Number low to high
•
A to Z
PROC SORT DATA=input_data_set <options>;
BY Variable1 Variable2;
RUN;
Use DESCENDING statement for numbers high to low and letters Z to A
•
BY City DESCENDING Population;
•
SAS sorts data first by city A to Z and then Population high to low
Data Set Manipulation
•
Some Options
• NODUPKEY
• Eliminates observations that have the same values for the BY
variables
• OUT=output_data_set
• By default, PROC SORT replaces the input data set with the
sorted data set
• Using this option, PROC SORT creates a newly sorted data set
and the input data set remains unchanged
Data Set Processing
• Data Set Processing
• DATA steps read in data from existing data sets or raw data files one
row at a time, like a loop
• DATA step reads data from the input data set in the following way:
1.
Read in current row from input data set to Program Data
Vector (PDV)
2.
Process SAS statements
3.
PDV to output data set
4.
Set current row to the next row in the input data set
5.
Iterate to Step 1
• One row at a time is processed
• Thus we cannot simply add the value of a variable in one row to the
value in another row
Combining Data Sets
• Concatenating (or Appending)
• Stacks each data set upon the other
• If one data set does not have a variable that the other datasets
do, the variable in the new data set is set to missing for the
observations from that data set.
• General Form: DATA output_data_set;
SET data1 data2;
run;
• PROC APPEND may also be used
Combining Data Sets
•
Merging Data Sets
• One-to-One Match Merge
• A single record in a data set corresponds to a single record in all
other data sets
• Example: Patient and Billing Information
• One-to-Many Match Merge
• Matching one observation from one data set to multiple
observations in other data sets
• Example: County and State Information
• Note: Data must be sorted before merging can be done
(PROC SORT)
Combining Data Sets
•
One-to-One Match Merge
• Usually need at least one common variable between data sets –
matching purposes
• For the example, a patient ID would be needed
• Do not need common variable if all data sets are in exactly the same
order
• General Form: DATA output_data_set;
MERGE input_data_set1 input_data_set2;
By variable1 variable2;
RUN;
Combining Data Sets
•
One-to-One Match Merge
• Example:
Performance
Goals
Month
Goal
Month
Sales
1
8223
1
9000
2
6034
2
6000
3
4220
3
5000
Code:
DATA compare;
MERGE performance goals;
BY month;
difference=sales-goal;
RUN;
Combining Data Sets
•
One-to-One Match Merge
• Example cont.:
Compare
Month
Sales
Goal
Difference
1
8223
9000
-777
2
6034
6000
34
3
4220
5000
-780
Combining Data Sets
•
One-to-Many Match Merge
• Requires at least one common variable in the data sets for matching
purposes
• For the example, State information is in both the state and county
files
• If two data sets have variables with the same name, the variables in
the second data set will overwrite the variable in the first.
• General Form: DATA output_data_set;
MERGE Data1 Data2 Data3;
BY Variable1 Variable2;
RUN:
Combining Data Sets
•
One-to-Many Match Merge
• Example:
Videos
Adjustment
Category
Sales
Category
Adjustment
Aerobics
12.99
Aerobics
.20
Aerobics
13.99
Step
.30
Aerobics
13.99
Weights
.25
Step
12.99
Step
12.99
Weights
15.99
Code:
DATA prices;
MERGE videos adjustment
BY category;
NewPrice=(1-adjustment)*sales;
RUN;
Combining Data Sets
•
One-to-One Many Merge
• Example cont.:
Videos
Category
Sales
Adjustment
NewPrice
Aerobics
12.99
.20
10.39
Aerobics
13.99
.20
11.19
Aerobics
13.99
.20
11.19
Step
12.99
.30
9.09
Step
12.99
.30
9.09
Weights
15.99
.25
11.99
Combining Data Sets
• Assignment
Create the dataset work.state_data
Merge work.state_sat_data2 with work.state_region_data by the
state variable
Combining Data Sets
• Solution
proc sort data=state_sat_data2;
by state;
run;
proc sort data=state_region_data;
by state;
run;
data state_data;
merge state_sat_data2 state_region_data;
by state;
run;
Saving Data Sets
•
Save as SAS dataset (.sas7bdat)
LIBNAME libref “destination folder”;
DATA libref.filename;
SET current_name;
optional commands;
RUN;
•
Other Formats
1.
File  Export Data
2.
Specify SAS data set
3.
Standard data source  select the file format
4.
Specify File Folder and Filename
Working With SAS Data Sets
Questions/Comments
Summary Procedures
1.
2.
3.
4.
5.
Print Procedure
Plot Procedure
Univariate Procedure
Means Procedure
Freq Procedure
Print Procedure
•
PROC PRINT is used to print data to the output window
•
By default, prints all observations and variables in the SAS data set
•
General Form:
PROC PRINT DATA=input_data_set <options>
<optional SAS statements>;
RUN;
•
Some Options
• input_data_set (obs=n) -
Specifies the number of observations to
be printed in the output
• NOOBS -
Suppresses printing observation number
• LABEL -
Prints the labels instead of variable
names
Print Procedure
• Optional SAS statements
• BY variable1 variable2 variable3;
• Starts a new section of output for every new value of the BY
variables
• ID variable1 variable2 variable3;
• Prints ID variables on the left hand side of the page and
suppresses the printing of the observation numbers
• SUM variable1 variable2 variable3;
• Prints sum of listed variables at the bottom of the output
• VAR variable1 variable2 variable3;
• Prints only listed variables in the output
Print Procedure
• Assignment
Use PROC PRINT to print out the state variable separately for
each region
Note: All procedures for the remainder of the course will be run on the
data set work.state_data.
Print Procedure
• Solution
proc sort data=state_data;
by region;
run;
proc print data=state_data;
var state;
by region;
run;
Plot Procedure
•
Used to create basic scatter plots of the data
•
Use PROC GPLOT or PROC SGPLOT for more sophisticated plots
•
General Form: PROC PLOT DATA=input_data_set;
PLOT vertical_variable *
horizontal_variable/<options>;
RUN;
•
By default, SAS uses letters to mark points on plots
• A for a single observation, B for two observations at the same point,
etc.
•
To specify a different character to represent a point
• PLOT vertical_variable * horizontal variable = ‘*’;
Plot Procedure
•
To specify a third variable to use to mark points
• PLOT vertical_variable * horizontal_variable = third_variable;
•
To plot more than one variable on the vertical axis
• PLOT vertical_variable1 * horizontal_variable=‘2’
vertical_variable2 * horizontal_variable=‘1’/OVERLAY;
Plot Procedure
• Assignment
Use the PLOT PROCEDURE to plot SAT Verbal scores versus
SAT Math Scores
Use the value of the region variable to mark points
Plot Procedure
• Solution
proc plot data=state_data;
plot math*verbal=region;
run;
Univariate Procedure
•
PROC UNIVARIATE is used to examine the distribution of data
•
Produces summary statistics for a single variable
• Includes mean, median, mode, standard deviation, skewness,
kurtosis, quantiles, etc.
•
General Form: PROC UNIVARIATE DATA=input_data_set <options>;
VAR variable1 variable2 variable3;
RUN ;
•
If the variable statement is not used, summary statistics will be produced
for all numeric variables in the input data set.
Univariate Procedure
•
Options include:
• PLOT – produces Stem-and-leaf plot, Box plot, and Normal
probability plot;
• NORMAL – produces tests of Normality
Univariate Procedure
•
Assignment
Use PROC UNIVARIATE to produce a normal probability plot and test
the normality of the SAT Total variable and Expenditure variable
Univariate Procedure
• Solution
proc univariate data=state_data normal plot;
var expend total;
run;
Means Procedure
•
Similar to the Univariate procedure
•
General Form:
PROC MEANS DATA=input_data_set options;
<Optional SAS statements>;
RUN;
•
With no options or optional SAS statements, the Means procedure will
print out the number of non-missing values, mean, standard deviation,
minimum, and maximum for all numeric variables in the input data set
Means Procedure
•
Options
•
•
Statistics Available
CLM
Two-Sided Confidence Limits
RANGE
Range
CSS
Corrected Sum of Squares
SKEWNESS
Skewness
CV
Coefficient of Variation
STDDEV
Standard Deviation
KURTOSIS
Kurtosis
STDERR
Standard Error of Mean
LCLM
Lower Confidence Limit
SUM
Sum
MAX
Maximum Value
SUMWGT
Sum of Weight Variables
MEAN
Mean
UCLM
Upper Confidence Limit
MIN
Minimum Value
USS
Uncorrected Sum of Squares
N
Number Non-missing Values
VAR
Variance
NMISS
Number Missing Values
PROBT
Probability for Student’s t
MEDIAN (or P50)
Median
T
Student’s t
Q1 (P25)
25% Quantile
Q3 (P75)
75% Quantile
P1
1% Quantile
P5
5% Quantile
P10
10% Quantile
P90
90% Quantile
P95
95% Quantile
P99
99% Quantile
Note: The default alpha level for confidence limits is 95%. Use ALPHA= option to
specify different alpha level.
Means Procedure
•
Optional SAS Statements
• VAR Variable1 Variable2;
• Specifies which numeric variables statistics will be produced for
• BY Variable1 Variable2;
• Calculates statistics for each combination of the BY variables
• Output out=output_data_set;
• Creates data set with the default statistics
Means Procedure
• Assignment
Use PROC MEANS to calculate the mean and variance of the
expenditure variable for each region
Means Procedure
• Solution
proc sort data=state_data;
by region;
run;
proc means data=state_data mean var;
var expend;
by region;
run;
FREQ Procedure
•
PROC FREQ is used to generate frequency tables
•
Most common usage is create table showing the distribution of categorical
variables
•
General Form:
•
Options
•
PROC FREQ DATA=input_data_set;
TABLE variable1*variable2*variable3/<options>;
RUN;
•
LIST – prints cross tabulations in list format rather than grid
•
MISSING – specifies that missing values should be included in the tabulations
•
OUT=output_data_set – creates a data set containing frequencies, list format
•
NOPRINT – suppress printing in the output window
Use BY statement to get percentages within each category of a variable
FREQ Procedure
• Assignment
Use PROC FREQ to find the number of states within each region
FREQ Procedure
• Solution
proc freq data=state_data;
table region;
run;
Summary Procedures
Questions/Comments
Statistical Analysis Procedures
1.
2.
3.
4.
5.
Correlation – PROC CORR
Regression – PROC REG
Analysis of Variance – PROC ANOVA
Chi-square Test of Association – PROC FREQ
General Linear Models – PROC GENMOD
CORR Procedure
•
PROC CORR is used to calculate the correlations between variables
•
Correlation coefficient measures the linear relationship between two variables
•
Values Range from -1 to 1
• Negative correlation - as one variable increases the other decreases
• Positive correlation – as one variable increases the other increases
• 0 – no linear relationship between the two variables
• 1 – perfect positive linear relationship
• -1 – perfect negative linear relationship
•
General Form:
PROC CORR DATA=input_data_set <options>
VAR Variable1 Variable2;
With Variable3;
RUN;
CORR Procedure
•
If the VAR and WITH statements are not used, correlation is computed
for all pairs of numeric variables
•
Options include
•
SPEARMAN – computes Spearman’s rank correlations
•
KENDALL – computes Kendall’s Tau coefficients
CORR Procedure
•
Question: What is the correlation between the SAT Total variable and
Expenditure variable? Is it significant?
Based on previous exercises, which correlation coefficient
should we use?
•
Assignment:
Use PROC CORR to find the correlation between the SAT
Total variable and Expenditure Variable
CORR Procedure
• Solution
If the normality assumption is valid
proc corr data=state_data /;
var total expend;
run;
If the normality assumption is not valid
proc corr data=state_data spearman;
var total expend;
run;
REG Procedure
•
PROC REG is used to fit linear regression models by least squares estimation
•
One of many SAS procedures that can perform regression analysis
•
Only continuous independent variables (Use GENMOD for categorical variables)
•
General Form:
PROC REG DATA=input_data_set <options>
MODEL dependent=independent1 independent2/<options>;
<optional statements>;
RUN;
•
PROC REG statement options include
•
PCOMIT=m - performs principle component estimation with m principle
components
•
CORR – displays correlation matrix for independent variables in the model
REG Procedure
•
MODEL statement options include
• SELECTION=
• Specifies a model selection procedure be conducted –
FORWARD, BACKWARD, and STEPWISE
• ADJRSQ - Computes the Adjusted R-Square
• MSE – Computes the Mean Square Error
• COLLIN – performs collinearity analysis
• CLB – computes confidence limits for parameter estimates
• ALPHA=
• Sets significance value for confidence and prediction intervals
and tests
REG Procedure
•
Optional statements include
• PLOT Dependent*Independent1 – generates plot of data
REG Procedure
• Assignment
Use PROC REG to generate a multiple linear regression model
Dependent Variable – SAT Total (total)
Use Stepwise Selection  Possible Independent Variables
– Average pupil to teacher ratio (PT_ratio)
– Current expenditure per pupil (expend)
– Estimated annual salary of teachers (salary)
– Percentage of eligible students taking the SAT
(students)
REG Procedure
• Solution
proc reg data=state_data;
model total=pt_ratio expend salary students/selection=stepwise;
run;
ANOVA Procedure
•
PROC ANOVA performs analysis of variance
•
Designed for balanced data (PROC GLM used for unbalance data)
•
Can handle nested and crossed effects and repeated measures
•
General Form:
PROC ANOVA DATA=input_data_set <options>;
CLASS independent1 independent2;
MODEL dependent=independent1 independent2;
<optional statements>;
Run;
•
Class statement must come before model statement, used to define
classification variables
ANOVA Procedure
•
Useful PROC ANOVA statement option – OUTSTAT=output_data_set
• Generates output data set that contains sums of squares,
degrees of freedom, statistics, and p-values for each effect in the
model
•
Useful optional statement – MEANS independent1/<comparison type>
• Used to perform multiple comparisons analysis
• Set <comparison type> to:
• TUKEY – Tukey’s studentized range test
• BON – Bonferroni t test
• T – pairwise t tests
• Duncan – Duncan’s multiple-range test
• Scheffe – Scheffe’s multiple comparison procedure
ANOVA Procedure
•
Question: Are there significant differences between the Match SAT
scores of students from different regions?
If there are significant differences, which regions are different?
•
Assignment:
Use PROC ANOVA to determine if there are significant
differences in the Math SAT variable between regions
Perform multiple comparisons between regions using
Tukey’s Adjustment
ANOVA Procedure
•
Solution
proc anova data=state_data;
class region;
model math=region;
means region/tukey;
run;
FREQ Procedure
•
PROC FREQ can also be used to perform analysis with categorical data
•
General Form:
PROC FREQ DATA=input_data_set;
TABLE variable1 variable2/<options>;
RUN;
•
TABLE statement options include:
•
AGREE –
Tests and measures of classification agreement including McNemar’s test,
Bowker’s test, Cochran’s Q test, and Kappa statistics
•
CHISQ -
Chi-square test of homogeneity and measures of association
•
MEASURE - Measures of association include Pearson and Spearman correlation,
gamma, Kendall’s Tau, Stuart’s tau, Somer’s D, lambda, odds ratios, risk
ratios, and confidence intervals
GENMOD Procedure
•
PROC GENMOD is used to estimate linear models in which the response
is not necessarily normal
•
Logistic and Poisson regression are examples of generalized linear
models
•
General Form:
PROC GENMOD DATA=input_data_set;
CLASS independent1;
MODEL dependent = independent1 independent2/
dist= <option>
link=<option>;
run;
GENMOD Procedure
•
DIST = - specifies the distribution of the response variable
•
LINK= - specifies the link function from the linear predictor to the mean of
the response
•
Example – Logistic Regression
•
•
DIST = binomial
•
LINK = logit
Example – Poisson Regression
•
DIST = poisson
•
LINK = log
GENMOD Procedure
• Question:
How do we model the probability of having a high
total SAT average based on other variables in the
dataset?
Is the dependent variable normal, or does it have a
different distribution?
What link function would you specify?
• Assignment: Use PROC GENMOD to perform Logistic
Regression on the work.state_data data set
• Dependent variable – upper_ind
• Independent variables
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–
–
–
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Average pupil to teacher ratio (PT_ratio)
Current expenditure per pupil (expend)
Estimated annual salary of teachers (salary)
Percentage of eligible students taking the SAT (students)
Region (region)
GENMOD Procedure
• Solution
proc genmod data=state_data descending;
class region;
model upper_ind=pt_ratio expend salary students/dist=bin link=logit;
run;
Statistical Analysis Procedures
Questions/Comments
Attendee Questions
If time permits