# STAT 456: Applied Multivariate Analysis | UW–Madison [View on UW Courses](https://uwcourses.com/courses/STAT_456) ## 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 998bee46dd31b7797492232abcb4af8641acf36efc27dfd0179e6a1e4fb38f97 ### course id STAT 456 ### course uid course\_1ea543c8a9e3b7f76f3a2d83 ### catalog version id 05e9b50ed271d86396feb312a304ee0f274b5021163d2e4fe403095abedd6e1d ### course number 456 ### subjects * STAT ### title APPLIED MULTIVARIATE ANALYSIS ### description Multivariate statistical learning methods are essential and broadly applicable tools for analyzing and understanding complex datasets. Emphasizes the conceptual underpinnings, core models, and basic statistical reasoning underlying the techniques. Topics include: dimension reduction (e.g., principal component analysis, multidimensional scaling, factor analysis), clustering (e.g., k-means, hierarchical, density-based, and model-based clustering), and classification (e.g., discriminant analysis, classification tree, and ensemble methods). Utilizes the R programming language. ### requirements text (STAT 333or340) and (MATH 320,340,341,345, or375), graduate/professional standing, or declared in Statistics VISP ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id 751c9d8c9e58e68f2af247f7fad68e39b532610d60c38fe30837d4b64209afa7 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary STAT 456 teaches multivariate statistical learning methods for analyzing complex datasets, covering dimension reduction, clustering, and classification using R. ### llm topics * Dimension reduction * Clustering * Classification ### llm skills * Dimension reduction, clustering, and classification * Statistical computing with R ### llm assumed background * Linear regression and prediction * Probability, hypothesis testing, and regression * Linear algebra and differential equations * Matrix algebra and linear algebra * Theoretical linear algebra and proofs * Linear algebra, calculus, and optimization ### llm search phrases * multivariate statistical learning * dimension reduction PCA * clustering methods * classification ensemble methods * R programming statistics ### llm requirements status valid ### llm student summary status valid ### llm experience status valid ### catalog variants None recorded. ### student summary #### context hash e168eadde82cc372091012a0ae250426554b469470f0ee666ea603305ca29b7a #### course id STAT 456 #### current instructors None recorded. #### difficulty workload Historical reviews of Wei-Yin Loh: The workload consisted of a series of small homework assignments and one major semester project. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2018-12-28 00:43:48 +0000 UTC", "review_id": "db57c3ca2c5827339a1c5277", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTMxMTY1Nzk0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` #### errors None recorded. #### historical context Historical reviews of Wei-Yin Loh: Wei-Yin Loh focused on important attributes of multivariate analysis and spent time on all relevant topics. The course structure relied on a single semester project and small homework assignments. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2018-12-28 00:43:48 +0000 UTC", "review_id": "db57c3ca2c5827339a1c5277", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTMxMTY1Nzk0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` #### offered false #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Historical reviews of Wei-Yin Loh: Wei-Yin Loh's STAT 456 focused on a single semester project and small homeworks, emphasizing applied multivariate analysis attributes without unnecessary content. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2018-12-28 00:43:48 +0000 UTC", "review_id": "db57c3ca2c5827339a1c5277", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTMxMTY1Nzk0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` Recent recorded grades — Spring 2024: 3.50 GPA, 73.0% A/AB (n=37 letter grades); Spring 2025: 3.64 GPA, 81.6% A/AB (n=38 letter grades); Spring 2026: 3.39 GPA, 62.7% A/AB (n=51 letter grades). ```json { "citations": [ { "course_id": "STAT 456", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1244", "type": "grade" }, { "course_id": "STAT 456", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "STAT 456", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience Historical reviews of Wei-Yin Loh: The professor ensured coverage of all important subject attributes, providing a focused and relevant applied learning experience. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2018-12-28 00:43:48 +0000 UTC", "review_id": "db57c3ca2c5827339a1c5277", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTMxMTY1Nzk0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history WEI-YIN LOH is recorded teaching in Fall 2009, Fall 2011, Fall 2014, Fall 2016, Fall 2018, Fall 2019. Recorded history may be incomplete and does not establish a future schedule. ```json { "citations": [ { "course_id": "STAT 456", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "source_record": { "entity_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1102", "type": "grade" }, { "course_id": "STAT 456", "run_id": "20260907T155543-ce3781c4", "section_number": 2, "source_course_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "source_record": { "entity_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1122", "type": "grade" }, { "course_id": "STAT 456", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "0851b066-d815-3eef-9e91-1b2d3ca77e91", 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23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.384, "graded": 129, "counts": [ 47, 37, 27, 12, 3, 2, 1 ], "sections": 5 }, "instructor_url": "/instructors/QILIN_LI--instructor_c46c7e5103606b152346afca" }, { "instructor_uid": "instructor_eeb902c84f921bb8550c1160", "source": "madgrades", "source_instructor_id": "6484577", "identity_basis": "source_id", "identity_status": "source_identified", "name": "BAIHENG CHEN", "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": 3.691, "graded": 217, "counts": [ 133, 45, 29, 9, 1, 0, 0 ], "sections": 6 }, "instructor_url": "/instructors/BAIHENG_CHEN--instructor_eeb902c84f921bb8550c1160" }, { "instructor_uid": "instructor_4e3d02c381227810f056d301", "source": "madgrades", "source_instructor_id": "6490392", "identity_basis": "source_id", "identity_status": "source_identified", "name": "JOSHUA CAPE", "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", "ratings": { "review_count": 6, "quality": 2.5, "difficulty": 3.5, "quality_count": 6, "difficulty_count": 6, "profile_id": "rmp:2835627", "source_url": "https://www.ratemyprofessors.com/professor/2835627", "match_basis": "exact_name", "observed_at": "2026-09-07 16:30:08.006263+00:00", "courses": { "course_821b8b9c055e8ecd0e25a516": { "review_count": 1, "quality": 5, "difficulty": 1, "quality_count": 1, "difficulty_count": 1 } }, "bayesian_quality": 3.392065987343535, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.533, "graded": 224, "counts": [ 88, 77, 51, 6, 1, 0, 1 ], "sections": 19 }, "instructor_url": "/instructors/JOSHUA_CAPE--instructor_4e3d02c381227810f056d301" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1102", "term_name": "Fall 2009", "instructors": [ "Wei-Yin Loh" ], "a": 10, "ab": 4, "b": 6, "bc": 1, "c": 1, "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": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1122", "term_name": "Fall 2011", "instructors": [ "Wei-Yin Loh" ], "a": 5, "ab": 4, "b": 16, "bc": 0, "c": 1, "d": 0, "f": 1, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 28, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1152", "term_name": "Fall 2014", "instructors": [ "Wei-Yin Loh" ], "a": 8, "ab": 8, "b": 5, "bc": 0, "c": 1, "d": 0, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 23, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1162", "term_name": "Fall 2015", "instructors": [ "KRISHNAKUMAR BALASUBRAMANIAN" ], "a": 17, "ab": 6, "b": 5, "bc": 1, "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": 29, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1172", "term_name": "Fall 2016", "instructors": [ "Wei-Yin Loh" ], "a": 10, "ab": 16, "b": 11, "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": 38, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1182", "term_name": "Fall 2017", "instructors": [ "PAUL SAVARIAPPAN", "Yin Li" ], "a": 15, "ab": 7, "b": 4, "bc": 1, "c": 1, "d": 0, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 29, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1192", "term_name": "Fall 2018", "instructors": [ "Wei-Yin Loh" ], "a": 18, "ab": 13, "b": 22, "bc": 0, "c": 7, "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": 60, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1202", "term_name": "Fall 2019", "instructors": [ "SIYU WANG", "Wei-Yin Loh" ], "a": 6, "ab": 8, "b": 5, "bc": 3, "c": 6, "d": 0, "f": 1, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 30, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1234", "term_name": "Spring 2023", "instructors": [ "JOHN FOGG", "Yinqiu He" ], "a": 7, "ab": 10, "b": 6, "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": 23, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1244", "term_name": "Spring 2024", "instructors": [ "Qin Li", "Yinqiu He" ], "a": 13, "ab": 14, "b": 7, "bc": 3, "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": 37, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "Baiheng Chen", "Yinqiu He" ], "a": 21, "ab": 10, "b": 4, "bc": 3, "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": 38, "source_aliases": [ "STAT 456" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 456", "course_uid": "course_1ea543c8a9e3b7f76f3a2d83", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Joshua Cape", "Qin Li" ], "a": 17, "ab": 15, "b": 15, "bc": 3, "c": 0, "d": 0, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 51, "source_aliases": [ "STAT 456" ] } ] ``` ### statistics #### gpa 3.407 #### graded 405 #### counts * 147 * 115 * 106 * 15 * 17 * 0 * 5 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_1ea543c8a9e3b7f76f3a2d83-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_1ea543c8a9e3b7f76f3a2d83-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_1ea543c8a9e3b7f76f3a2d83-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_1ea543c8a9e3b7f76f3a2d83-0.json) #### reviews * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course\_1ea543c8a9e3b7f76f3a2d83-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course_1ea543c8a9e3b7f76f3a2d83-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_1ea543c8a9e3b7f76f3a2d83-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_1ea543c8a9e3b7f76f3a2d83-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.4074074074074074 #### count 405 #### university ##### size 4299 ##### gpa Percentile 20 ##### count Percentile 80 ##### median Count 131 ##### 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 | 15 | false | | 2.8–3.2 | 334 | false | | 3.2–3.6 | 1228 | true | | 3.6–4.0 | 2722 | false | #### departments ```json [ { "subject": "STAT", "comparison": { "size": 65, "gpaPercentile": 30, "countPercentile": 70, "medianCount": 256, "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": 10, "current": false }, { "range": "3.2–3.6", "count": 25, "current": true }, { "range": "3.6–4.0", "count": 30, "current": false } ] } } ] ``` ### terms #### 1102 ##### term 1102 ##### gpa 3.477272727272727 ##### count 22 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1122 ##### term 1122 ##### gpa 3.111111111111111 ##### count 27 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1152 ##### term 1152 ##### gpa 3.347826086956522 ##### count 23 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1162 ##### term 1162 ##### gpa 3.6724137931034484 ##### count 29 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1172 ##### term 1172 ##### gpa 3.4864864864864864 ##### count 37 ##### university ###### size 991 ###### gpa Percentile 50 ###### count Percentile 15 ###### median Count 67 ###### 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 | 22 | false | | 2.8–3.2 | 226 | false | | 3.2–3.6 | 353 | true | | 3.6–4.0 | 390 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 25, "gpaPercentile": 58, "countPercentile": 4, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 8, "current": false }, { "range": "3.2–3.6", "count": 13, "current": true }, { "range": "3.6–4.0", "count": 3, "current": false } ] } } ] ``` #### 1182 ##### term 1182 ##### gpa 3.4827586206896552 ##### count 29 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1192 ##### term 1192 ##### gpa 3.2916666666666665 ##### count 60 ##### university ###### size 1077 ###### gpa Percentile 28 ###### count Percentile 44 ###### median Count 67 ###### 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 | 1 | false | | 2.4–2.8 | 22 | false | | 2.8–3.2 | 203 | false | | 3.2–3.6 | 370 | true | | 3.6–4.0 | 481 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 27, "gpaPercentile": 38, "countPercentile": 31, "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": 1, "current": false }, { "range": "2.8–3.2", "count": 6, "current": false }, { "range": "3.2–3.6", "count": 17, "current": true }, { "range": "3.6–4.0", "count": 3, "current": false } ] } } ] ``` #### 1202 ##### term 1202 ##### gpa 2.9827586206896552 ##### count 29 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1234 ##### term 1234 ##### gpa 3.5217391304347827 ##### count 23 ##### departments | subject | comparison | | ------- | ---------- | | STAT | | #### 1244 ##### term 1244 ##### gpa 3.5 ##### count 37 ##### university ###### size 1241 ###### gpa Percentile 33 ###### count Percentile 15 ###### 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 | true | | 3.6–4.0 | 706 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 29, "gpaPercentile": 61, "countPercentile": 11, "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": true }, { "range": "3.6–4.0", "count": 8, "current": false } ] } } ] ``` #### 1254 ##### term 1254 ##### gpa 3.6447368421052633 ##### count 38 ##### university ###### size 1289 ###### gpa Percentile 46 ###### count Percentile 17 ###### 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": "STAT", "comparison": { "size": 28, "gpaPercentile": 63, "countPercentile": 11, "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 3.392156862745098 ##### count 51 ##### university ###### size 1283 ###### gpa Percentile 22 ###### count Percentile 36 ###### 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 | 1 | false | | 2.4–2.8 | 20 | false | | 2.8–3.2 | 130 | false | | 3.2–3.6 | 343 | true | | 3.6–4.0 | 789 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 26, "gpaPercentile": 52, "countPercentile": 12, "medianCount": 66, "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": 10, "current": false }, { "range": "3.2–3.6", "count": 9, "current": true }, { "range": "3.6–4.0", "count": 7, "current": false } ] } } ] ``` ### benchmarks #### all ##### school ###### size 4299 ###### gpa 3.653914301947051 ###### top Share 81.48839006162206 ###### count 131 ##### STAT ###### size 65 ###### gpa 3.547019309183568 ###### top Share 75.26823339899686 ###### count 256 #### terms ##### 1102 ###### school ###### size 774 ###### gpa 3.36712622366007 ###### top Share 63.703228532165696 ###### count 67 ###### STAT ###### size 15 ###### gpa 3.2431139797798068 ###### top Share 54.806129569025835 ###### count 72 ##### 1122 ###### school ###### size 804 ###### gpa 3.3737149752667013 ###### top Share 63.65118790117902 ###### count 70 ###### STAT ###### size 14 ###### gpa 3.247214504889537 ###### top Share 58.600170104917346 ###### count 78.5 ##### 1152 ###### school ###### size 918 ###### gpa 3.408733538594633 ###### top Share 66.06693005846735 ###### count 65 ###### STAT ###### size 20 ###### gpa 3.247281119355238 ###### top Share 58.400370581589016 ###### count 55 ##### 1162 ###### school ###### size 954 ###### gpa 3.43726781345039 ###### top Share 68.05645304987395 ###### count 64.5 ###### STAT ###### size 24 ###### gpa 3.394300783591793 ###### top Share 65.51183483352922 ###### count 50.5 ##### 1172 ###### school ###### size 991 ###### gpa 3.465129042641772 ###### top Share 69.42600667567366 ###### count 67 ###### STAT ###### size 25 ###### gpa 3.3324928117291264 ###### top Share 61.945618937548005 ###### count 56 ##### 1182 ###### school ###### size 1025 ###### gpa 3.474159360325319 ###### top Share 70.34861441764347 ###### count 69 ###### STAT ###### size 24 ###### gpa 3.4023821420534044 ###### top Share 65.90044412864664 ###### count 65.5 ##### 1192 ###### school ###### size 1077 ###### gpa 3.50327634323125 ###### top Share 72.44641925727845 ###### count 67 ###### STAT ###### size 27 ###### gpa 3.349790708103796 ###### top Share 62.94637491184082 ###### count 74 ##### 1202 ###### school ###### size 1102 ###### gpa 3.5102605987403313 ###### top Share 72.78244569053733 ###### count 68 ###### STAT ###### size 30 ###### gpa 3.4022744829589433 ###### top Share 67.29048111947832 ###### count 80.5 ##### 1234 ###### school ###### size 1188 ###### gpa 3.5799441677552393 ###### top Share 77.18256448537606 ###### count 65 ###### STAT ###### size 25 ###### gpa 3.396227416481204 ###### top Share 66.8429997070879 ###### count 67 ##### 1244 ###### school ###### size 1241 ###### gpa 3.5975573126929192 ###### top Share 78.29036874847135 ###### count 65 ###### 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 ###### 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 ###### STAT ###### size 26 ###### gpa 3.343399816676682 ###### top Share 63.23102425546245 ###### count 66 ## instructor Trends ```json [ { "uid": "instructor_20bff09bd8c7abcebab15a2f", "name": "WEI-YIN LOH", "count": 198, "terms": [ { "term": "1102", "count": 22, "sections": 1, "gpa": 3.477272727272727 }, { "term": "1122", "count": 27, "sections": 1, "gpa": 3.111111111111111 }, { "term": "1152", "count": 23, "sections": 1, "gpa": 3.347826086956522 }, { "term": "1172", "count": 37, "sections": 1, "gpa": 3.4864864864864864 }, { "term": "1192", "count": 60, "sections": 1, "gpa": 3.2916666666666665 }, { "term": "1202", "count": 29, "sections": 1, "gpa": 2.9827586206896552 } ] }, { "uid": "instructor_886411200bdd2835bcd94a44", "name": "YINQIU HE", "count": 98, "terms": [ { "term": "1234", "count": 23, "sections": 2, "gpa": 3.5217391304347827 }, { "term": "1244", "count": 37, "sections": 2, "gpa": 3.5 }, { "term": "1254", "count": 38, "sections": 2, "gpa": 3.6447368421052633 } ] }, { "uid": "instructor_c46c7e5103606b152346afca", "name": "QILIN LI", "count": 88, "terms": [ { "term": "1244", "count": 37, "sections": 2, "gpa": 3.5 }, { "term": "1264", "count": 51, "sections": 2, "gpa": 3.392156862745098 } ] }, { "uid": "instructor_4e3d02c381227810f056d301", "name": "JOSHUA CAPE", "count": 51, "terms": [ { "term": "1264", "count": 51, "sections": 2, "gpa": 3.392156862745098 } ] }, { "uid": "instructor_eeb902c84f921bb8550c1160", "name": "BAIHENG CHEN", "count": 38, "terms": [ { "term": "1254", "count": 38, "sections": 2, "gpa": 3.6447368421052633 } ] }, { "uid": "instructor_26b12c9de8f3be3e7f69746c", "name": "SIYU WANG", "count": 29, "terms": [ { "term": "1202", "count": 29, "sections": 1, "gpa": 2.9827586206896552 } ] }, { "uid": "instructor_2e82e074b9b1de4eed9b4df0", "name": "PAUL SAVARIAPPAN", "count": 29, "terms": [ { "term": "1182", "count": 29, "sections": 1, "gpa": 3.4827586206896552 } ] }, { "uid": "instructor_4ea402fd5651417c35b5c5fb", "name": "KRISHNAKUMAR BALASUBRAMANIAN", "count": 29, "terms": [ { "term": "1162", "count": 29, "sections": 1, "gpa": 3.6724137931034484 } ] }, { "uid": "instructor_ff1f8c3fd86ee30f1585d607", "name": "YI LI", "count": 29, "terms": [ { "term": "1182", "count": 29, "sections": 1, "gpa": 3.4827586206896552 } ] }, { "uid": "instructor_04c4d719d224ce73225df4e2", "name": "JOHN FOGG", "count": 23, "terms": [ { "term": "1234", "count": 23, "sections": 2, "gpa": 3.5217391304347827 } ] } ] ``` ## following None recorded.