STAT 426: SAS PROGRAMMING
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Lectures
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Module 0: Accessing SAS
Module 1: Getting Started
Module 2: Data Input and Output
Module 3: SAS Libraries
Module 4: Logic and Control
Module 5: Enhancing reports
Module 6: Iterative programming
Module 7: Data Validation
Module 8: Cleaning Data
Module 9: Graphs
Module 10: Combining datasets
Module 11: Macros
Module 12: Probability and simulation
Module 13: Introductory Statistical Analyses
Review
>
Intro class lectures
Coursework
Instructions
Labs
Code
Instructions for accessing drives:
Mapping shared drive
OneDrive in Vlab, see
ITS website
SAS data files for libraries
(save zip file into folder on computer, then unzip)
Module 1
_null_ basic math1
_null_ basic math2
data step xvar, xyz
data time, int
sgplot int
data moose
sgplot moose
data moose2
sgplot moose2
secret moose
Module 2
gpa data step
gpa sgplot
gpa multi print
Data
GPAdata.csv
Module 3
proc contents all
proc contents usnewhire
proc datasets all
Module 4
herc library/print
where proc print
where data step
if family data
if family alt
Module 5
format and informat
proc format character
proc format numeric
label
bonus label
length
ODS files
ODS style
split label
title/footnote
Misc
ODS styles list
Module 6
if-then/else
if-then/do
basic do loops
Fibonacci loop
do while/do until
nested do loops
Module 7
proc print validation
proc freq validation
proc means validation
proc univariate validation
Module 8
clean by steps
clean all-in-one
Module 9
iris dataset
dotplot
histogram
histogram+density curve
one variable boxplot (horiz)
one variable boxplot (vert)
scatterplot
scatterplot points+lines
scatterplot matrix
boxplot by cat var
simple horiz barplot
pie 1
v barplot of means by cat
chocolate pie
Data
iris.csv
Module 10
proc sort
proc append
concatenate
interleave
merge
merge+nodupeq
proc sql example
Data
mtcars.csv
Module 11
sum function
common functions
hypotenuse calculation1
(via _NULL_ data step)
hypotenuse
calculation2
(via data step)
%LET macro variable
%printit macro
%sortandprint macro
reports macro
pythagoras
molar mass
Module 12
lln sim
binomial pdf, cdf, random sample
normal distribution
CLT Poisson
CLT binomial
Module 13
1-sample t-tests
2-sample t-tests
categorical data tests (chi-squared distribution)
anova and multiple comparisons
simple linear regression (slr)
Data
iris.csv
faithful.csv
decagon.csv
hands.csv
crabs.csv
tree_diameters.csv
Home
Course Logistics
Contact
Syllabus
Links
Lectures
Schedule
Module 0: Accessing SAS
Module 1: Getting Started
Module 2: Data Input and Output
Module 3: SAS Libraries
Module 4: Logic and Control
Module 5: Enhancing reports
Module 6: Iterative programming
Module 7: Data Validation
Module 8: Cleaning Data
Module 9: Graphs
Module 10: Combining datasets
Module 11: Macros
Module 12: Probability and simulation
Module 13: Introductory Statistical Analyses
Review
>
Intro class lectures
Coursework
Instructions
Labs
Code