Syllabus (public companion)
Orientation and course shape — Blackboard is authoritative for graded specifics
This public page is an orientation companion to the syllabus, not the operational syllabus itself. It describes the shape of STAT 44203 — Modern SAS for Statistical Analytics in qualitative terms. It carries no numeric grade weights, no due dates, and no submission rules. For everything that counts toward your grade — exact weights, deadlines, policies, and submissions — Blackboard (the LMS) is authoritative. If this page and Blackboard ever disagree, follow Blackboard.
Course description
Modern SAS for Statistical Analytics treats SAS as a professional statistical analytics environment — an ecosystem for moving from messy data to documented, defensible analytic results — not as “just a language for running procedures.” The throughline of the whole course is the analytics workflow: set up the SAS environment and a project, point a library at your data, read it in with the DATA step, read the log, import and clean and validate, join tables and check the row counts, summarize, send report-ready output to ODS, run statistical procedures, reshape and merge, simulate, and finally assemble a single reproducible program another person could rerun. Syntax is always in service of that workflow, never the other way around.
The course’s soul is reliability, traceability, and verification. Every analysis on this site is followed by what the log should say and a verification check — row counts before and after a join, a variable-type confirmation, an NMISS count, a sanity range. The recurring test is one sentence: would someone else be able to understand, rerun, and verify this? You will also learn to state what an analysis does and does not show — that “statistically significant” is not “practically important,” that an odds ratio is not a risk ratio, and that observational data are not causal.
A note on how SAS appears on this site. SAS is proprietary software, and on this site every SAS program, log excerpt, and procedure output is shown as static, syntax-highlighted code and hand-authored synthetic listings — none of it is executed here. You run SAS yourself in the course-designated environment, which is exactly how you will work on the homework and labs. See Software and reproducibility below.
Who it is for
This course assumes a prior introductory statistics course or comparable preparation: you should be comfortable with variables and data tables, summary statistics, distributions, correlation, simple regression, confidence intervals, hypothesis tests, and p-values at an introductory level. No prior SAS experience is required — the course builds SAS syntax gradually from the environment up, starting with what a library and a DATA step are. Prior experience with R, Python, SQL, or spreadsheets is helpful but not assumed. The main expectation is willingness to work carefully: write and run programs, read the log, check your data before you trust output, and explain analytic conclusions in plain words.
Learning outcomes
By the end of the course, a successful student will be able to:
- Describe SAS as a professional statistical analytics environment and name the stages of a complete analytics workflow, from messy input to a documented, reproducible result.
- Set up the SAS environment and an organized project, assign a
libnameto a permanent library, and run a first program that produces a log and output. - Read the SAS log fluently — distinguish
NOTE,WARNING, andERROR, and treat the log as primary output rather than an afterthought. - Work with libraries, datasets, observations, and variables, and set variable attributes — labels, formats for display, and informats for reading input (so a character date becomes a real SAS date).
- Distinguish character from numeric variables and explain why that distinction is load-bearing, including the silent type conversions the log warns about.
- Write DATA step logic —
IF/THEN, subsetting, derived variables — and handle missing values correctly, including the classic comparison trap and the use ofNMISS. - Import external data and clean and validate it: detect duplicates, impossible values, and bad informats, and confirm the row count goes from raw to clean as expected.
- Join tables with PROC SQL — and check the output row count against what you expect every time (the inner-join vs left-join teaching object).
- Produce summaries and tables with PROC MEANS, PROC FREQ, and PROC UNIVARIATE, and read
NvsNMISSand the mean of a 0/1 variable as a proportion. - Send report-ready output to ODS destinations (HTML, PDF, RTF) and build a simple graph with PROC SGPLOT.
- Run and interpret a two-group comparison (PROC TTEST) and a multi-group comparison (PROC GLM/ANOVA), stating the assumptions before interpreting the result.
- Fit and interpret a linear regression (PROC REG) — coefficients, \(R^2\), RMSE — and read basic diagnostics.
- Fit and interpret a logistic regression (PROC LOGISTIC) for a binary outcome, stating which level is modeled and that an odds ratio is not a risk ratio.
- Reshape and merge data (PROC TRANSPOSE, DATA step
MERGE), recognize the many-to-many merge bug, and validate the result. - Use simulation with
call streaminit(20260824)andRANDto study power, Type I error, and a sampling distribution, and seed any random work reproducibly. - Assemble a reproducible SAS analysis report — one program top to bottom, named files, a verification-notes section — and interpret results responsibly: significance is not importance, association is not causation, and a rendered listing is not a verified run.
Weekly rhythm
This is an in-person lecture/lab course meeting three days a week (MWF), and each day has a settled role:
- Monday — concept + syntax (checkpoint). We introduce the week’s workflow idea and the SAS syntax that serves it, work an example, and close with a short workflow checkpoint.
- Wednesday — build + debug. We build the week’s step hands-on: write code, read the log, debug a
NOTE/WARNING/ERROR, and inspect the output — the day the verification habits get practiced. - Friday — skill check + applied analytics. A short skill check on recent material, then an applied analytics task on the recurring study — a piece of the workflow carried end to end.
This rhythm is the plan; the authoritative weekly schedule, including any shifts (for example the Labor Day adjustment and the fall-break window), lives in Blackboard and on the Schedule page.
Assessment shape (indicative — not a contract)
The table below conveys the relative emphasis of each graded category in qualitative terms only. It is not a grading contract: it carries no percentages and no point values, and the actual weights live in Blackboard.
| Category | Rough emphasis |
|---|---|
| SAS workflow checkpoints | small |
| Weekly skill checks | small |
| SAS homework | the largest single category |
| Analytics labs | small–moderate |
| Midterm practical | moderate |
| Final analytics project | moderate |
| Final practical | moderate |
Read this as a picture of where the weight sits: regular SAS homework is the backbone of the grade; the midterm practical, the final analytics project, and the final practical each carry moderate weight; the analytics labs sit in the middle; and the smaller pieces — the Monday workflow checkpoints and the Friday skill checks — add up around them. The midterm practical is an in-class, hands-on SAS practical (it covers the environment, project setup, libraries, datasets, variable attributes, the DATA step, importing, cleaning, validation, PROC SQL, summaries, and reporting); the final analytics project carries the full workflow on the recurring study from raw data to a reproducible report; the final practical is a timed hands-on exercise. For the exact weighting and the rules of each, consult Blackboard.
Software and reproducibility
We use SAS through the course-designated SAS environment — SAS Studio via SAS OnDemand for Academics, SAS Viya for Learners, SAS Skill Builder for Students, or a university-supported SAS installation. The specific provisioned environment and access steps are a syllabus placeholder confirmed in Blackboard; the SAS access & project setup resource page collects the access path and the project-organization conventions once they are settled.
SAS access is required and is being finalized. This course depends on a working, student-accessible SAS account in the designated environment. Until that access is confirmed for you in Blackboard, you cannot run the programs yourself. Watch Blackboard for the access announcement, and set up your account as soon as it is available.
Every analysis in this course is written so it can be reproduced: a single SAS program that runs top to bottom with no manual point-and-click, an assigned libname, the options you rely on, a fixed simulation seed (call streaminit(20260824)), named output files, and a short verification-notes section. The same program should yield the same result for anyone who reruns it. A result you cannot rerun — or whose log you never read — is a result on trust alone.
As noted above, the SAS code, logs, and output shown on this public site are static and synthetic — typed for teaching, not executed here — so the site renders deterministically and SAS-free. The numbers in the worked examples come from a single recurring synthetic study (RiverCity Wellness, seed streaminit(20260824)) and are flagged on every page; they are not real health data and carry verified: false. You do the real running in your own SAS session.
AI use (summary)
Generative AI tools may be used as a study and workflow aid — to explain a SAS concept a second way, generate practice tasks, help debug your own SAS program, or walk through a similar example. They may not produce work you submit as your own, complete your homework or project, fabricate output or log lines, or replace your responsibility to understand the workflow, and they are prohibited on the practicals and any skill check unless explicitly allowed.
Because SAS results are so sensitive to data quality, variable type, and how a procedure is conditioned, AI output must be checked carefully. Many wrong SAS answers come from confusing character with numeric, ignoring a WARNING in the log, skipping the row-count check after a join, treating an odds ratio as a risk ratio, or reading an unexecuted code block as if it had run. Whenever you use AI on a graded program, lab, or project component, include a brief AI Use Note with three labeled lines:
| Field | What to record |
|---|---|
| Tool | which assistant you used (with approximate date or version) |
| Purpose | what you used it for |
| Verification | how you checked, tested, revised, or validated the output |
Verification is the load-bearing line: rerun the program and read the log, check the row counts and variable types, confirm NMISS, compare to class notes, or rewrite the explanation in your own words after checking it. You are responsible for every line of code and every sentence of interpretation you submit. The full policy lives in Blackboard.
Materials
You will need:
- Instructor notes, examples, and SAS workflow guides — the primary course materials, and the spine of this public site: the weekly Notes, the study Labs, and the Resources (including the SAS workflow glossary). These are the course’s own synthesis, written for this course.
- The course-designated SAS environment — SAS Studio via SAS OnDemand for Academics, SAS Viya for Learners, SAS Skill Builder for Students, or a university-supported SAS installation, as confirmed in Blackboard. See SAS access & project setup.
- SAS documentation — the official SAS Institute documentation and tutorials (documentation.sas.com, support.sas.com), used as a reference you learn to consult. It is copyrighted by SAS Institute Inc., all rights reserved; the notes link and cite specific pages as reading pointers in the course’s own words, never reproducing SAS-doc prose, examples, or figures.
- Introduction to Modern Statistics, 2nd ed. (Çetinkaya-Rundel & Hardin) — a free optional review source for the statistical background on the procedure weeks (t-tests, ANOVA, regression, logistic regression), CC BY-SA 3.0, at openintro-ims.netlify.app. It is not a SAS syntax manual.
- Blackboard (the LMS) — for all graded work, dates, and announcements.
Not used in this course: Cengage, WebAssign, MyLab, or any paid homework platform. SAS® and all SAS Institute product names are the property of SAS Institute Inc.
Where things live
Keep the two homes of the course straight:
- Blackboard (the LMS) is the operational home: graded SAS workflow checkpoints, weekly skill checks, SAS homework, analytics labs, the midterm practical, the final analytics project, the final practical, all due dates, all submissions, and all grades. It is authoritative.
- This public site is the public notes home: the weekly Notes, the Labs as study material, the Resources, and orientation pages like this one and the Schedule. It is ungraded.
This companion exists to orient you. Whenever a graded specific is at stake — a weight, a deadline, a policy — go to Blackboard, which is authoritative.
Verification & reproducibility status
verified: false. This page is an orientation companion, not a verified analysis, and the surrounding course site is a draft. The SAS code, log excerpts, and every numeric value shown anywhere on this site — including the recurring wellness-program study figures (the 210→200 cleaning, the 594-row inner join vs the 596-row left join, the PROC MEANS systolic_bp mean 128.4, the PROC TTEST \(t = -4.27\), the PROC REG \(R^2 = 0.214\), the PROC LOGISTIC arm odds ratio 1.78, the simulation power ≈ 0.99) — are hand-authored, synthetic, and were NOT run. SAS is proprietary and is not executed in this build, so a rendered, syntax-highlighted code block or a typed listing is not evidence that the code runs or that the numbers are right. The course SAS execution/output verification gate is BLOCKED. The data are synthetic (seed streaminit(20260824)); the study is observational (any arm difference is associational, not causal, since the synthetic arms are not described as randomized, and an odds ratio is not a risk ratio). No accessibility-compliance claim is made. Do not treat any value on this site as a confirmed reference until the human/SAS-run sign-off in the course’s private notation and verification ledger §5 is complete.
Public vs. graded
These notes, the SAS examples, and the practice here are public and ungraded — study material only. No graded prompts, answer keys, rubrics, point values, or due dates appear on this site. Graded SAS workflow checkpoints, skill checks, homework, analytics labs, the midterm practical, the final analytics project, and the final practical live in Blackboard (the LMS), which is authoritative for due dates, submissions, and grades. If this page and Blackboard ever disagree, follow Blackboard.