# ECON/GENBUS/STAT 775: Bayesian Statistics | UW–Madison [View on UW Courses](https://uwcourses.com/courses/ECON_GENBUS_STAT_775) ## Dataset ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ### repository twangodev/uwcourses ### schema version 6 ### importer version 7 ### projection id 52a78527ff09011d88cb04c7d43867d9fbfec2930bf1dd2ec359ebd2844e0b07 ### observed at 2026-09-07T15:55:43.033547+00:00 ### built at 2026-09-10T19:50:42.743001+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 0b711c3125ac7d75462d9a9744b459b6937b7efa403a4c5f033bc43cff0d073d ### course id ECON/GENBUS/STAT 775 ### course uid course\_1946b63ab745477aa4576b0b ### catalog version id 383d823de3cd7b4e704ee4ee97ceba96dcfaf0af8d8bf5b32a8373887e25d089 ### course number 775 ### subjects * ECON * GENBUS * STAT ### title BAYESIAN STATISTICS ### description Introduces the theory, methods, and computational procedures needed to perform advanced Bayesian data analyses. Predictive and decision-theoretic motivations including subjective probability, risk, admissibility, and exchangeability; highlights key components of Bayesian analysis (i.e., prior, likelihood, posterior, and predictive distributions) within standard parametric models and advanced hierarchical and multilevel models; demonstrates the iterative process of model specification, implementation, criticism, and revision with applied case studies; implements computational techniques (e.g., Markov chain Monte Carlo, variational inference) in modern probabilistic programming languages. ### requirements text STAT 609,610,611,STAT/​MATH  709,ECON 709,POLI SCI 818, orCOMP SCI/​E C E  761 ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id ddc3c55d8ddfae8b7bf5137d370b967b5de66cc99fa9e66217d2f993795382a3 ### 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 computational procedures for advanced Bayesian data analysis, including MCMC and probabilistic programming. ### llm topics * Decision-theoretic motivations * Bayesian components * Hierarchical and multilevel models * Computational techniques ### llm skills * Advanced Bayesian data analysis * Computational Bayesian methods * Model specification and criticism ### llm assumed background * Probability theory and distribution theory * Statistical inference and estimation * Probability and statistical modeling foundations * Measure-theoretic probability and large sample theory * Calculus and linear algebra ### llm search phrases * Bayesian data analysis * Markov chain Monte Carlo * variational inference * probabilistic programming * hierarchical models * posterior distribution * predictive distribution ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None 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"course_1946b63ab745477aa4576b0b", "term_id": "1204", "term_name": "Spring 2020", "instructors": [ "Michael Newton" ], "a": 32, "ab": 13, "b": 1, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 4, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 50, "source_aliases": [ "ECON/GENBUS/STAT 775" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON/GENBUS/STAT 775", "course_uid": "course_1946b63ab745477aa4576b0b", "term_id": "1224", "term_name": "Spring 2022", "instructors": [ "Sameer Deshpande" ], "a": 30, "ab": 0, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 31, "source_aliases": [ "ECON/GENBUS/STAT 775" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON/GENBUS/STAT 775", "course_uid": "course_1946b63ab745477aa4576b0b", "term_id": "1232", "term_name": "Fall 2022", "instructors": [ "Sameer Deshpande" ], "a": 22, "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": 22, "source_aliases": [ "ECON/GENBUS/STAT 775" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON/GENBUS/STAT 775", "course_uid": "course_1946b63ab745477aa4576b0b", "term_id": "1244", "term_name": "Spring 2024", "instructors": [ "Sameer Deshpande" ], "a": 30, "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": 30, "source_aliases": [ "ECON/GENBUS/STAT 775" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON/GENBUS/STAT 775", "course_uid": "course_1946b63ab745477aa4576b0b", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Sameer Deshpande" ], "a": 26, "ab": 3, "b": 1, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 1, "not_reported": 0, "other": 0, "total": 31, "source_aliases": [ "ECON/GENBUS/STAT 775" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "ECON/GENBUS/STAT 775", "course_uid": "course_1946b63ab745477aa4576b0b", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Sameer Deshpande" ], "a": 16, "ab": 3, "b": 1, "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": 20, "source_aliases": [ "ECON/GENBUS/STAT 775" ] } ] ``` ### statistics #### gpa 3.782 #### graded 425 #### counts * 295 * 85 * 39 * 2 * 4 * 0 * 0 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_1946b63ab745477aa4576b0b-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_1946b63ab745477aa4576b0b-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_1946b63ab745477aa4576b0b-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_1946b63ab745477aa4576b0b-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_1946b63ab745477aa4576b0b-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_1946b63ab745477aa4576b0b-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.7823529411764705 #### count 425 #### university ##### size 4771 ##### gpa Percentile 59 ##### count Percentile 77 ##### median Count 151 ##### 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 | 0 | false | | 2.4–2.8 | 18 | false | | 2.8–3.2 | 364 | false | | 3.2–3.6 | 1357 | false | | 3.6–4.0 | 3032 | true | #### departments ```json [ { "subject": "ECON", "comparison": { "size": 117, "gpaPercentile": 73, "countPercentile": 73, "medianCount": 167, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 22, "current": false }, { "range": "3.2–3.6", "count": 47, "current": false }, { "range": "3.6–4.0", "count": 47, "current": true } ] } }, { "subject": "GENBUS", "comparison": { "size": 48, "gpaPercentile": 51, "countPercentile": 77, "medianCount": 189, "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": 4, "current": false }, { "range": "3.2–3.6", "count": 7, "current": false }, { "range": "3.6–4.0", "count": 37, "current": true } ] } }, { "subject": "STAT", "comparison": { "size": 71, "gpaPercentile": 76, "countPercentile": 69, "medianCount": 218, "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": 9, "current": false }, { "range": "3.2–3.6", "count": 26, "current": false }, { "range": "3.6–4.0", "count": 36, "current": true } ] } } ] ``` ### terms #### 1072 ##### term 1072 ##### gpa 3.5588235294117645 ##### count 17 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1082 ##### term 1082 ##### gpa 3.7916666666666665 ##### count 12 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1092 ##### term 1092 ##### gpa 3.763157894736842 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1102 ##### term 1102 ##### gpa 3.625 ##### count 20 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1112 ##### term 1112 ##### gpa 3.619047619047619 ##### count 21 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1122 ##### term 1122 ##### gpa 3.6041666666666665 ##### count 24 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1132 ##### term 1132 ##### gpa 3.6818181818181817 ##### count 22 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1144 ##### term 1144 ##### gpa 3.8684210526315788 ##### count 19 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1154 ##### term 1154 ##### gpa 3.6666666666666665 ##### count 15 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1164 ##### term 1164 ##### gpa 3.6379310344827585 ##### count 29 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1174 ##### term 1174 ##### gpa 3.6041666666666665 ##### count 24 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1184 ##### term 1184 ##### gpa 3.76 ##### count 25 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1204 ##### term 1204 ##### gpa 3.8369565217391304 ##### count 46 ##### university ###### size 1012 ###### gpa Percentile 63 ###### count Percentile 31 ###### median Count 65.5 ###### 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 | 0 | false | | 2.4–2.8 | 1 | false | | 2.8–3.2 | 25 | false | | 3.2–3.6 | 301 | false | | 3.6–4.0 | 685 | true | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 33, "gpaPercentile": 88, "countPercentile": 19, "medianCount": 79, "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": 1, "current": false }, { "range": "3.2–3.6", "count": 18, "current": false }, { "range": "3.6–4.0", "count": 14, "current": true } ] } }, { "subject": "GENBUS", "comparison": null }, { "subject": "STAT", "comparison": { "size": 27, "gpaPercentile": 77, "countPercentile": 12, "medianCount": 56, "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": 0, "current": false }, { "range": "3.2–3.6", "count": 11, "current": false }, { "range": "3.6–4.0", "count": 16, "current": true } ] } } ] ``` #### 1224 ##### term 1224 ##### gpa 4 ##### count 30 ##### university ###### size 1171 ###### gpa Percentile 94 ###### count Percentile 0 ###### median Count 64 ###### 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 | 3 | false | | 2.4–2.8 | 21 | false | | 2.8–3.2 | 173 | false | | 3.2–3.6 | 358 | false | | 3.6–4.0 | 616 | true | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 37, "gpaPercentile": 97, "countPercentile": 0, "medianCount": 71, "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": 15, "current": false }, { "range": "3.2–3.6", "count": 15, "current": false }, { "range": "3.6–4.0", "count": 7, "current": true } ] } }, { "subject": "GENBUS", "comparison": { "size": 15, "gpaPercentile": 100, "countPercentile": 0, "medianCount": 82, "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": 6, "current": false }, { "range": "3.2–3.6", "count": 1, "current": false }, { "range": "3.6–4.0", "count": 8, "current": true } ] } }, { "subject": "STAT", "comparison": { "size": 27, "gpaPercentile": 100, "countPercentile": 0, "medianCount": 74, "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": 6, "current": false }, { "range": "3.2–3.6", "count": 10, "current": false }, { "range": "3.6–4.0", "count": 11, "current": true } ] } } ] ``` #### 1232 ##### term 1232 ##### gpa 4 ##### count 22 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | #### 1244 ##### term 1244 ##### gpa 4 ##### count 30 ##### university ###### size 1241 ###### gpa Percentile 95 ###### count Percentile 0 ###### median Count 65 ###### 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 | 9 | false | | 2.8–3.2 | 151 | false | | 3.2–3.6 | 373 | false | | 3.6–4.0 | 706 | true | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 36, "gpaPercentile": 97, "countPercentile": 0, "medianCount": 85.5, "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": 12, "current": false }, { "range": "3.2–3.6", "count": 16, "current": false }, { "range": "3.6–4.0", "count": 8, "current": true } ] } }, { "subject": "GENBUS", "comparison": { "size": 19, "gpaPercentile": 100, "countPercentile": 0, "medianCount": 100, "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": 4, "current": false }, { "range": "3.2–3.6", "count": 5, "current": false }, { "range": "3.6–4.0", "count": 10, "current": true } ] } }, { "subject": "STAT", "comparison": { "size": 29, "gpaPercentile": 100, "countPercentile": 0, "medianCount": 62, "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": 13, "current": false }, { "range": "3.6–4.0", "count": 8, "current": true } ] } } ] ``` #### 1254 ##### term 1254 ##### gpa 3.9166666666666665 ##### count 30 ##### university ###### size 1289 ###### gpa Percentile 83 ###### count Percentile 0 ###### median Count 66 ###### 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 | 0 | false | | 2.4–2.8 | 17 | false | | 2.8–3.2 | 128 | false | | 3.2–3.6 | 379 | false | | 3.6–4.0 | 765 | true | ##### departments ```json [ { "subject": "ECON", "comparison": { "size": 39, "gpaPercentile": 97, "countPercentile": 0, "medianCount": 73, "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": 12, "current": false }, { "range": "3.2–3.6", "count": 17, "current": false }, { "range": "3.6–4.0", "count": 10, "current": true } ] } }, { "subject": "GENBUS", "comparison": { "size": 28, "gpaPercentile": 85, "countPercentile": 0, "medianCount": 83, "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": 1, "current": false }, { "range": "3.2–3.6", "count": 11, "current": false }, { "range": "3.6–4.0", "count": 16, "current": true } ] } }, { "subject": "STAT", "comparison": { "size": 28, "gpaPercentile": 93, "countPercentile": 0, "medianCount": 70, "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": 6, "current": false }, { "range": "3.2–3.6", "count": 11, "current": false }, { "range": "3.6–4.0", "count": 11, "current": true } ] } } ] ``` #### 1264 ##### term 1264 ##### gpa 3.875 ##### count 20 ##### departments | subject | comparison | | ------- | ---------- | | ECON | | | GENBUS | | | STAT | | ### benchmarks #### all ##### school ###### size 4771 ###### gpa 3.654941356350452 ###### top Share 81.5540456071996 ###### count 151 ##### ECON ###### size 117 ###### gpa 3.509495123744769 ###### top Share 71.49170985155877 ###### count 167 ##### GENBUS ###### size 48 ###### gpa 3.703222822619457 ###### top Share 84.28111629268216 ###### count 189 ##### STAT ###### size 71 ###### gpa 3.582682925622714 ###### top Share 77.6742040811584 ###### count 218 #### terms ##### 1072 ###### school ###### size 723 ###### gpa 3.344595727439471 ###### top Share 62.012941427864526 ###### count 67 ###### ECON ###### size 22 ###### gpa 3.0214354917219977 ###### top Share 40.565682901047786 ###### count 81.5 ##### 1082 ###### school ###### size 723 ###### gpa 3.3583896732335172 ###### top Share 62.932746798940194 ###### count 68 ###### ECON ###### size 23 ###### gpa 3.0919296833361263 ###### top Share 43.929521475736 ###### count 86 ###### STAT ###### size 10 ###### gpa 3.181469252605204 ###### top Share 50.79697144214053 ###### count 66.5 ##### 1092 ###### school ###### size 732 ###### gpa 3.374660709520374 ###### top Share 63.690339324346596 ###### count 70 ###### ECON ###### size 24 ###### gpa 3.105837139183562 ###### top Share 44.708122686953516 ###### count 78 ###### STAT ###### size 11 ###### gpa 3.2851296989222463 ###### top Share 61.121424545066226 ###### count 65 ##### 1102 ###### school ###### size 774 ###### gpa 3.36712622366007 ###### top Share 63.703228532165696 ###### count 67 ###### ECON ###### size 27 ###### gpa 3.1047684954005166 ###### top Share 46.644448387645376 ###### count 65 ###### STAT ###### size 15 ###### gpa 3.2431139797798068 ###### top Share 54.806129569025835 ###### count 72 ##### 1112 ###### school ###### size 805 ###### gpa 3.3737773184830386 ###### top Share 63.91039244872789 ###### count 66 ###### ECON ###### size 26 ###### gpa 3.0801797521029894 ###### top Share 44.59121845139345 ###### count 60 ###### STAT ###### size 14 ###### gpa 3.242406375705586 ###### top Share 57.34276482904032 ###### count 68.5 ##### 1122 ###### school ###### size 804 ###### gpa 3.3737149752667013 ###### top Share 63.65118790117902 ###### count 70 ###### ECON ###### size 32 ###### gpa 3.148060317171765 ###### top Share 48.69785590964286 ###### count 75 ###### STAT ###### size 14 ###### gpa 3.247214504889537 ###### top Share 58.600170104917346 ###### count 78.5 ##### 1132 ###### school ###### size 851 ###### gpa 3.391128778809763 ###### top Share 64.69509437122704 ###### count 66 ###### ECON ###### size 27 ###### gpa 3.084125462905226 ###### top Share 44.16217101765231 ###### count 103 ###### STAT ###### size 16 ###### gpa 3.261044965144187 ###### top Share 59.360682472524054 ###### count 72 ##### 1144 ###### school ###### size 878 ###### gpa 3.4092443327803017 ###### top Share 65.95978235963416 ###### count 64 ###### ECON ###### size 28 ###### gpa 3.1980885064740128 ###### top Share 51.73890040686708 ###### count 80.5 ###### STAT ###### size 14 ###### gpa 3.0970281238579784 ###### top Share 51.741770775379614 ###### count 74.5 ##### 1154 ###### school ###### size 935 ###### gpa 3.4282378385810093 ###### top Share 67.66674668721691 ###### count 63 ###### ECON ###### size 30 ###### gpa 3.212244027927608 ###### top Share 51.42727757714087 ###### count 77 ###### STAT ###### size 19 ###### gpa 3.247335845906281 ###### top Share 58.329434774225824 ###### count 57 ##### 1164 ###### school ###### size 965 ###### gpa 3.46491230798535 ###### top Share 69.92376287143657 ###### count 68 ###### ECON ###### size 32 ###### gpa 3.2386078353906425 ###### top Share 54.482642655777084 ###### count 83 ###### STAT ###### size 21 ###### gpa 3.317795716655057 ###### top Share 62.84642273068802 ###### count 69 ##### 1174 ###### school ###### size 955 ###### gpa 3.4597947384071412 ###### top Share 69.5290038366363 ###### count 69 ###### ECON ###### size 31 ###### gpa 3.262762293275924 ###### top Share 56.37213628921166 ###### count 97 ###### STAT ###### size 22 ###### gpa 3.3391231320477925 ###### top Share 62.80825113292103 ###### count 71.5 ##### 1184 ###### school ###### size 1010 ###### gpa 3.47925290354517 ###### top Share 70.86270937115944 ###### count 68 ###### ECON ###### size 33 ###### gpa 3.323005038886213 ###### top Share 59.81252867468931 ###### count 87 ###### STAT ###### size 25 ###### gpa 3.460210558661105 ###### top Share 70.42953406269322 ###### count 70 ##### 1204 ###### school ###### size 1012 ###### gpa 3.703747431523761 ###### top Share 84.59811226304916 ###### count 65.5 ###### ECON ###### size 33 ###### gpa 3.5818404132725767 ###### top Share 77.38790104739134 ###### count 79 ###### STAT ###### size 27 ###### gpa 3.6537351147420534 ###### top Share 80.87794592686507 ###### count 56 ##### 1224 ###### school ###### size 1171 ###### gpa 3.5627882428500937 ###### top Share 76.37271640560496 ###### count 64 ###### ECON ###### size 37 ###### gpa 3.358824647431352 ###### top Share 62.02356029673636 ###### count 71 ###### GENBUS ###### size 15 ###### gpa 3.5090687835578316 ###### top Share 72.61861183684195 ###### count 82 ###### STAT ###### size 27 ###### gpa 3.4650681869904103 ###### top Share 69.88606041738046 ###### count 74 ##### 1232 ###### school ###### size 1216 ###### gpa 3.575457431972425 ###### top Share 77.33712216234007 ###### count 66 ###### ECON ###### size 38 ###### gpa 3.3360474441634977 ###### top Share 61.683172425833845 ###### count 68.5 ###### GENBUS ###### size 14 ###### gpa 3.514889967225248 ###### top Share 75.15171041645179 ###### count 213.5 ###### STAT ###### size 32 ###### gpa 3.4024781451477413 ###### top Share 66.63145704464162 ###### count 60.5 ##### 1244 ###### school ###### size 1241 ###### gpa 3.5975573126929192 ###### top Share 78.29036874847135 ###### count 65 ###### ECON ###### size 36 ###### gpa 3.386885708485864 ###### top Share 63.919049551615416 ###### count 85.5 ###### GENBUS ###### size 19 ###### gpa 3.534713725247456 ###### top Share 74.84471631838613 ###### count 100 ###### STAT ###### size 29 ###### gpa 3.390846810323202 ###### top Share 64.2629736611174 ###### count 62 ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ###### ECON ###### size 39 ###### gpa 3.4082741335226703 ###### top Share 66.64551412266185 ###### count 73 ###### GENBUS ###### size 28 ###### gpa 3.642800867856554 ###### top Share 80.80715279553331 ###### count 83 ###### STAT ###### size 28 ###### gpa 3.4752654121860003 ###### top Share 68.35873780152826 ###### count 70 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### ECON ###### size 37 ###### gpa 3.41582518818968 ###### top Share 66.18686699083136 ###### count 53 ###### GENBUS ###### size 30 ###### gpa 3.6817668874579623 ###### top Share 83.39332566820664 ###### count 79 ###### STAT ###### size 26 ###### gpa 3.343399816676682 ###### top Share 63.23102425546245 ###### count 66 ## instructor Trends ```json [ { "uid": "instructor_ac372d2f1d2072f2e887ce2a", "name": "KAM-WAH TSUI", "count": 247, "terms": [ { "term": "1072", "count": 17, "sections": 1, "gpa": 3.5588235294117645 }, { "term": "1082", "count": 12, "sections": 1, "gpa": 3.7916666666666665 }, { "term": "1092", "count": 19, "sections": 1, "gpa": 3.763157894736842 }, { "term": "1102", "count": 20, "sections": 1, "gpa": 3.625 }, { "term": "1112", "count": 21, "sections": 1, "gpa": 3.619047619047619 }, { "term": "1122", "count": 24, "sections": 1, "gpa": 3.6041666666666665 }, { "term": "1132", "count": 22, "sections": 1, "gpa": 3.6818181818181817 }, { "term": "1144", "count": 19, "sections": 1, "gpa": 3.8684210526315788 }, { "term": "1154", "count": 15, "sections": 1, "gpa": 3.6666666666666665 }, { "term": "1164", "count": 29, "sections": 1, "gpa": 3.6379310344827585 }, { "term": "1174", "count": 24, "sections": 1, "gpa": 3.6041666666666665 }, { "term": "1184", "count": 25, "sections": 1, "gpa": 3.76 } ] }, { "uid": "instructor_71e12ad537764a7f7f348a74", "name": "SAMEER DESHPANDE", "count": 132, "terms": [ { "term": "1224", "count": 30, "sections": 1, "gpa": 4 }, { "term": "1232", "count": 22, "sections": 1, "gpa": 4 }, { "term": "1244", "count": 30, "sections": 1, "gpa": 4 }, { "term": "1254", "count": 30, "sections": 1, "gpa": 3.9166666666666665 }, { "term": "1264", "count": 20, "sections": 1, "gpa": 3.875 } ] }, { "uid": "instructor_af1422b8e6ae67945b96390f", "name": "SHENGJI JIA", "count": 49, "terms": [ { "term": "1174", "count": 24, "sections": 1, "gpa": 3.6041666666666665 }, { "term": "1184", "count": 25, "sections": 1, "gpa": 3.76 } ] }, { "uid": "instructor_f371fd2a28dcb497f02e3fc5", "name": "MICHAEL NEWTON", "count": 46, "terms": [ { "term": "1204", "count": 46, "sections": 1, "gpa": 3.8369565217391304 } ] }, { "uid": "instructor_3d9efb4c8f669d6f43489e0b", "name": "SONG WANG", "count": 29, "terms": [ { "term": "1164", "count": 29, "sections": 1, "gpa": 3.6379310344827585 } ] } ] ``` ## following None recorded.