# BMI/STAT 727: Theory and Methods of Longitudinal Data Analysis | UW–Madison [View on UW Courses](https://uwcourses.com/courses/BMI_STAT_727) ## Dataset ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ### repository twangodev/uwcourses ### schema version 6 ### importer version 7 ### projection id 94468b7d4154126ca413937bb4738a6d21e0c2277f6fa397a6078ad80a32384f ### observed at 2026-09-07T15:55:43.033547+00:00 ### built at 2026-09-10T23:19:01.870283+00:00 ### courses 8951 ### current instructors 5754 ### limited false ### terms * 1272 * 1264 * 1262 * 1254 * 1252 * 1244 * 1242 * 1234 * 1232 * 1224 * 1222 * 1212 * 1204 * 1202 * 1194 * 1192 * 1184 * 1182 * 1174 * 1172 * 1164 * 1162 * 1154 * 1152 * 1144 * 1142 * 1134 * 1132 * 1124 * 1122 * 1114 * 1112 * 1104 * 1102 * 1094 * 1092 * 1084 * 1082 * 1074 * 1072 ### term 1272 ### departments | subject | count | | --------- | ----- | | AAE | 90 | | ABT | 20 | | ACCTIS | 36 | | ACTSCI | 14 | | AFAERO | 10 | | AFRICAN | 87 | | AFROAMER | 71 | | AGROECOL | 21 | | AMERIND | 53 | | ANAT\&PHY | 7 | | ANATOMY | 2 | | ANESTHES | 8 | | ANSCI | 57 | | ANTHRO | 95 | | ART | 125 | | ARTED | 10 | | ARTHIST | 121 | | ASIALANG | 129 | | ASIAN | 109 | | ASIANAM | 28 | | ASTRON | 38 | | ATMOCN | 69 | | BIOCHEM | 51 | | BIOCORE | 10 | | BIOLOGY | 14 | | BIOMDSCI | 18 | | BME | 66 | | BMI | 41 | | BMOLCHEM | 10 | | BOTANY | 69 | | BSE | 44 | | C\&ESOC | 73 | | CBE | 48 | | CHEM | 101 | | CHICLA | 52 | | CIVENGR | 133 | | CLASSICS | 47 | | CNP | 10 | | CNSRSCI | 50 | | COMARTS | 137 | | COMPBIO | 15 | | COMPLIT | 11 | | COMPSCI | 138 | | COUNPSY | 78 | | CRB | 19 | | CS\&D | 71 | | CSCS | 40 | | CURRIC | 193 | | DANCE | 93 | | DERM | 8 | | DS | 88 | | DYSCI | 35 | | ECE | 155 | | ECON | 145 | | EDPOL | 114 | | EDPSYCH | 97 | | ELPA | 70 | | EMA | 53 | | EMERMED | 17 | | ENGL | 206 | | ENTOM | 40 | | ENVIRST | 148 | | EP | 15 | | EPD | 67 | | ESL | 16 | | F\&WECOL | 60 | | FAMMED | 22 | | FINANCE | 50 | | FOLKLORE | 40 | | FOODSCI | 43 | | FRENCH | 63 | | GEN\&WS | 145 | | GENBUS | 72 | | GENECSLR | 17 | | GENETICS | 60 | | GEOG | 113 | | GEOSCI | 83 | | GERMAN | 82 | | GLE | 48 | | GNS | 38 | | GREEK | 27 | | HDFS | 41 | | HEBR-BIB | 13 | | HEBR-MOD | 10 | | HISTORY | 233 | | HISTSCI | 56 | | HONCOL | 13 | | ILS | 43 | | INFOSYS | 9 | | INTEGART | 7 | | INTEGSCI | 22 | | INTER-AG | 18 | | INTER-HE | 11 | | INTER-LS | 22 | | INTEREGR | 16 | | INTLBUS | 21 | | INTLST | 50 | | ISYE | 83 | | ITALIAN | 53 | | JEWISH | 56 | | JOURN | 88 | | KINES | 128 | | LACIS | 26 | | LANDARC | 61 | | LATIN | 24 | | LAW | 120 | | LEGALST | 48 | | LINGUIS | 35 | | LIS | 89 | | LITTRANS | 78 | | LSC | 52 | | M\&ENVTOX | 7 | | MARKETNG | 62 | | MATH | 153 | | MDGENET | 8 | | ME | 130 | | MEDHIST | 43 | | MEDICINE | 60 | | MEDIEVAL | 32 | | MEDPHYS | 39 | | MEDSC-M | 29 | | MEDSC-V | 41 | | MHR | 68 | | MICROBIO | 45 | | MILSCI | 16 | | MM\&I | 22 | | MOLBIOL | 6 | | MS\&E | 59 | | MUSIC | 165 | | MUSPERF | 126 | | NAVSCI | 20 | | NE | 44 | | NEURODPT | 11 | | NEUROL | 7 | | NEURSURG | 4 | | NTP | 11 | | NURSING | 111 | | NUTRSCI | 65 | | OBS\&GYN | 18 | | OCCTHER | 39 | | ONCOLOGY | 14 | | OPHTHALM | 5 | | OTM | 43 | | PATH | 34 | | PATH-BIO | 29 | | PEDIAT | 26 | | PHARMACY | 31 | | PHILOS | 77 | | PHMCOL-M | 11 | | PHMPRAC | 45 | | PHMSCI | 60 | | PHYASST | 37 | | PHYSICS | 88 | | PHYSIOL | 3 | | PHYTHER | 37 | | PLANTSCI | 52 | | PLPATH | 35 | | POLISCI | 192 | | POPHLTH | 58 | | PORTUG | 33 | | PSYCH | 101 | | PSYCHIAT | 22 | | PUBAFFR | 54 | | PUBLHLTH | 23 | | RADIOL | 11 | | REALEST | 41 | | RELIGST | 90 | | RHABMED | 9 | | RMI | 24 | | RP\&SE | 102 | | S\&APHM | 17 | | SCANDST | 73 | | SLAVIC | 89 | | SOC | 149 | | SOCWORK | 87 | | SOILSCI | 46 | | SPANISH | 83 | | SRMED | 21 | | STAT | 94 | | STDYABRD | 52 | | STS | 8 | | SURGERY | 25 | | SURGSCI | 33 | | THEATRE | 91 | | URBRPL | 60 | | UROLOGY | 5 | | ZOOLOGY | 89 | ## course ### run id 20260907T155543-ce3781c4 ### semester 1272 ### observed at 2026-09-07 15:55:43.033547+00:00 ### record version id ab3d2ade8e8993de6ad11a24b0ef8030aa21806af084a82ccc398a6537c2834a ### course id BMI/STAT 727 ### course uid course\_7ee7d113f019ab01285276bf ### catalog version id 0695c21d19465323dee6895223a91d31b8711dd67ed49a567757bd168da83210 ### course number 727 ### subjects * BMI * STAT ### title THEORY AND METHODS OF LONGITUDINAL DATA ANALYSIS ### description Theory and methods of fundamental statistical models for the analysis of longitudinal data, including repeated measures analysis of variance, linear mixed models, generalized linear mixed models, and generalized estimating equations. Introduction of how to implement these methods in statistical softwares such as in R and/or SAS, within the context of appropriate statistical models and carry out and interpret analyses. ### requirements text STAT 610 ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 054093ae69f5325b31a12849bd6d2e1896e5c4a3bfbf33d02bb63985fe239980 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Covers theory and methods for longitudinal data analysis, including mixed models and GEEs, with implementation in R and SAS. ### llm topics * Repeated measures analysis of variance * Linear mixed models * Generalized linear mixed models * Generalized estimating equations ### llm skills * Implementing statistical models in R and SAS * Carrying out and interpreting statistical analyses ### llm assumed background * Statistical inference, distribution theory, and hypothesis testing ### llm search phrases * longitudinal data analysis * linear mixed models * generalized estimating equations * R SAS statistical software * repeated measures ANOVA ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 2e3f39aa7a37e701c924986082695028d1e34b44f7ab546f9c7b4cba142da317 #### course id BMI/STAT 727 #### current instructors None recorded. #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered false #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Fall 2023: 3.96 GPA, 100.0% A/AB (n=14 letter grades); Fall 2025: 4.00 GPA, 100.0% A/AB (n=28 letter grades). ```json { "citations": [ { "course_id": "BMI/STAT 727", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "977c2cb3-90b6-3eb4-b7b9-87f12234543c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1242", "type": "grade" }, { "course_id": "BMI/STAT 727", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "977c2cb3-90b6-3eb4-b7b9-87f12234543c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1262", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [], "condition": null, "course": { "course_number": 610, "minimum_grade": null, "subjects": [ "STAT" ], "timing": "prior" }, "evidence": "STAT 610", "id": "n0", "kind": "course" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_57424157730da96ec177f067", "source": "madgrades", "source_instructor_id": "4286103", "identity_basis": "source_id", "identity_status": "source_identified", "name": "JIWEI ZHAO", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.918, "graded": 116, "counts": [ 106, 7, 2, 0, 0, 0, 1 ], "sections": 19 }, "instructor_url": "/instructors/JIWEI_ZHAO--instructor_57424157730da96ec177f067" }, { "instructor_uid": "instructor_89258e9344e6e1020d65c557", "source": "madgrades", "source_instructor_id": "6317790", "identity_basis": "source_id", "identity_status": "source_identified", "name": "TINGHUI XU", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.964, "graded": 14, "counts": [ 13, 1, 0, 0, 0, 0, 0 ], "sections": 1 }, "instructor_url": "/instructors/TINGHUI_XU--instructor_89258e9344e6e1020d65c557" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "BMI/STAT 727", "course_uid": "course_7ee7d113f019ab01285276bf", "term_id": "1242", "term_name": "Fall 2023", "instructors": [ "Jiwei Zhao", "TINGHUI XU" ], "a": 13, "ab": 1, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 14, "source_aliases": [ "BMI/STAT 727" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "BMI/STAT 727", "course_uid": "course_7ee7d113f019ab01285276bf", "term_id": "1262", "term_name": "Fall 2025", "instructors": [ "Jiwei Zhao" ], "a": 28, "ab": 0, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 28, "source_aliases": [ "BMI/STAT 727" ] } ] ``` ### statistics #### gpa 3.988 #### graded 42 #### counts * 41 * 1 * 0 * 0 * 0 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_7ee7d113f019ab01285276bf-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_7ee7d113f019ab01285276bf-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_7ee7d113f019ab01285276bf-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_7ee7d113f019ab01285276bf-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_7ee7d113f019ab01285276bf-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_7ee7d113f019ab01285276bf-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.988095238095238 #### count 42 #### university ##### size 2036 ##### gpa Percentile 93 ##### count Percentile 19 ##### median Count 79.5 ##### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 2 | false | | 2.4–2.8 | 5 | false | | 2.8–3.2 | 175 | false | | 3.2–3.6 | 520 | false | | 3.6–4.0 | 1334 | true | #### departments ```json [ { "subject": "BMI", "comparison": null }, { "subject": "STAT", "comparison": { "size": 46, "gpaPercentile": 100, "countPercentile": 16, "medianCount": 84.5, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 8, "current": false }, { "range": "3.2–3.6", "count": 17, "current": false }, { "range": "3.6–4.0", "count": 21, "current": true } ] } } ] ``` ### terms #### 1242 ##### term 1242 ##### gpa 3.9642857142857144 ##### count 14 ##### departments | subject | comparison | | ------- | ---------- | | BMI | | | STAT | | #### 1262 ##### term 1262 ##### gpa 4 ##### count 28 ##### departments | subject | comparison | | ------- | ---------- | | BMI | | | STAT | | ### benchmarks #### all ##### school ###### size 2036 ###### gpa 3.6586846928561045 ###### top Share 81.96270304729435 ###### count 79.5 ##### STAT ###### size 46 ###### gpa 3.5092281501023104 ###### top Share 72.55740739125099 ###### count 84.5 #### terms ##### 1242 ###### school ###### size 1295 ###### gpa 3.595302892737449 ###### top Share 78.36291889885307 ###### count 67 ###### STAT ###### size 34 ###### gpa 3.4558758327213903 ###### top Share 69.10154863162224 ###### count 55 ##### 1262 ###### school ###### size 1320 ###### gpa 3.628825763035853 ###### top Share 80.21118846327654 ###### count 70 ###### STAT ###### size 29 ###### gpa 3.415022952129974 ###### top Share 64.95385808199006 ###### count 81 ## instructor Trends ```json [ { "uid": "instructor_57424157730da96ec177f067", "name": "JIWEI ZHAO", "count": 42, "terms": [ { "term": "1242", "count": 14, "sections": 1, "gpa": 3.9642857142857144 }, { "term": "1262", "count": 28, "sections": 1, "gpa": 4 } ] }, { "uid": "instructor_89258e9344e6e1020d65c557", "name": "TINGHUI XU", "count": 14, "terms": [ { "term": "1242", "count": 14, "sections": 1, "gpa": 3.9642857142857144 } ] } ] ``` ## following None recorded.