# STAT 443: Classification and Regression Trees | UW–Madison [View on UW Courses](https://uwcourses.com/courses/STAT_443) ## 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 2c8176fae31555325581dc57e78058323dc38bf332fe67ec6bc9ab60a4cd681b ### course id STAT 443 ### course uid course\_bbb14bdd283ccb9368b888c2 ### catalog version id 0c71a67724b590dbb01f331c5ca0bfc864bf41edfd58d030102a701d4754d4e6 ### course number 443 ### subjects * STAT ### title CLASSIFICATION AND REGRESSION TREES ### description Introduction to algorithms and applications of classification and regression trees. Recursive partitioning, pruning, and cross-validation estimation of prediction error. Class priors and misclassification costs. Univariate and linear splits. Linear and kernel discriminant analysis and nearest-neighbor classification. Unbiased variable selection and importance scoring of variables. Least-squares, quantile, Poisson, logistic, and proportional hazards regression tree models. Tree ensembles. Subgroup identification of differential treatment effects. Multiple and longitudinal response variables. Missing values and multiple missing value codes. Comparisons with neural networks, support vector machines, and other methods. Bootstrap calibration and post-selection inference. Applications to business, social science, engineering, biology, medicine, and other fields. ### requirements text STAT 333,340, graduate/professional standing, or declared in Statistics VISP ### credit offering ids None recorded. ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id e7580d2b4792540cbf04b9fddfea96b97e7951b91d4dc2008b1d84e5cd7621a3 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary STAT 443 teaches algorithms for classification and regression trees, including pruning, ensembles, and variable selection. ### llm topics * Recursive partitioning and pruning. * Class priors and misclassification costs. * Univariate and linear splits. * Discriminant analysis and nearest-neighbor classification. * Tree ensembles. * Subgroup identification of differential treatment effects. * Multiple and longitudinal response variables. * Handling missing values. ### llm skills * Building and pruning classification and regression trees. * Variable selection and importance scoring. * Fitting various regression tree models. * Constructing tree ensembles. * Identifying treatment effect subgroups. * Bootstrap calibration and post-selection inference. ### llm assumed background * Foundational knowledge of regression, probability, hypothesis testing, and R programming. * Prerequisite coursework in statistics and mathematics, including STAT 240. ### llm search phrases * classification and regression trees * recursive partitioning * tree ensembles * STAT 443 algorithms * prediction error estimation ### llm requirements status valid ### llm student summary status valid ### llm experience status valid ### catalog variants None recorded. ### student summary #### context hash 70254b4c8e363caa4cdd5163bd537769100061d96188b887e80ac714594c5bcd #### course id STAT 443 #### current instructors None recorded. #### difficulty workload Historical reviews of Wei-Yin Loh: The course is extremely difficult, with 60% of the grade based on a final project. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2021-05-07 03:00:23 +0000 UTC", "review_id": "fa297eb4bb3f6800d569a45d", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM0NzI5NzE0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" }, { "instructor_name": "Wei-Yin Loh", "review_date": "2022-12-07 02:41:54 +0000 UTC", "review_id": "e85f32f7d995a8899ff15db6", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2OTk5ODQz", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` #### errors None recorded. #### historical context Historical reviews of Wei-Yin Loh: Wei-Yin Loh is described as captivating and approachable, though reviews disagree on lecture clarity and support. Some found him engaging, while others criticized vague content and lack of homework help. The course is noted as extremely difficult with a heavy final project weight. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2021-05-07 03:00:23 +0000 UTC", "review_id": "fa297eb4bb3f6800d569a45d", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM0NzI5NzE0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" }, { "instructor_name": "Wei-Yin Loh", "review_date": "2022-11-11 02:38:55 +0000 UTC", "review_id": "7a4078b2a83db76d08d61e8d", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2ODE1NDUw", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" }, { "instructor_name": "Wei-Yin Loh", "review_date": "2022-12-07 02:41:54 +0000 UTC", "review_id": "e85f32f7d995a8899ff15db6", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2OTk5ODQz", "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 is described as captivating and approachable, yet his teaching style is criticized for vague content and a lack of support, resulting in polarized student experiences. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2021-05-07 03:00:23 +0000 UTC", "review_id": "fa297eb4bb3f6800d569a45d", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM0NzI5NzE0", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" }, { "instructor_name": "Wei-Yin Loh", "review_date": "2022-11-11 02:38:55 +0000 UTC", "review_id": "7a4078b2a83db76d08d61e8d", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2ODE1NDUw", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" }, { "instructor_name": "Wei-Yin Loh", "review_date": "2022-12-07 02:41:54 +0000 UTC", "review_id": "e85f32f7d995a8899ff15db6", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2OTk5ODQz", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` Recent recorded grades — Fall 2022: 3.37 GPA, 65.2% A/AB (n=46 letter grades); Spring 2026: 3.67 GPA, 98.3% A/AB (n=58 letter grades). ```json { "citations": [ { "course_id": "STAT 443", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1232", "type": "grade" }, { "course_id": "STAT 443", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience Historical reviews of Wei-Yin Loh: Students find lecture notes unhelpful, consisting of hundreds of pages of code output, and report that homework help is unavailable during office hours. ```json { "citations": [ { "instructor_name": "Wei-Yin Loh", "review_date": "2022-11-11 02:38:55 +0000 UTC", "review_id": "7a4078b2a83db76d08d61e8d", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2ODE1NDUw", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" }, { "instructor_name": "Wei-Yin Loh", "review_date": "2022-12-07 02:41:54 +0000 UTC", "review_id": "e85f32f7d995a8899ff15db6", "run_id": "20260907T155543-ce3781c4", "source_instructor_id": "rmp:371080", "source_review_id": "UmF0aW5nLTM2OTk5ODQz", "source_url": "https://www.ratemyprofessors.com/professor/371080", "type": "review" } ] } ``` #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history WEI-YIN LOH is recorded teaching in Fall 2022, Spring 2026. Recorded history may be incomplete and does not establish a future schedule. ```json { "citations": [ { "course_id": "STAT 443", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "source_record": { "entity_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1232", "type": "grade" }, { "course_id": "STAT 443", "run_id": "20260907T155543-ce3781c4", "section_number": 2, "source_course_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "source_record": { "entity_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1232", "type": "grade" }, { "course_id": "STAT 443", "run_id": "20260907T155543-ce3781c4", "section_number": 1, "source_course_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "source_record": { "entity_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1264", "type": "grade" }, { "course_id": "STAT 443", "run_id": "20260907T155543-ce3781c4", "section_number": 2, "source_course_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "source_record": { "entity_id": "ea40447a-7fec-39ed-a440-a88544ba920c", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "section_grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [ "n1", "n2", "n3", "n4" ], "condition": null, "course": null, "evidence": "STAT 333,340, graduate/professional standing, or declared in Statistics VISP", "id": "n0", "kind": "any" }, { "children": [], "condition": null, "course": { "course_number": 333, "minimum_grade": null, "subjects": [ "STAT" ], "timing": "prior" }, "evidence": "STAT 333", "id": "n1", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 340, "minimum_grade": null, "subjects": [ "STAT" ], "timing": "prior" }, "evidence": "340", "id": "n2", "kind": "course" }, { "children": [], "condition": "graduate/professional standing", "course": null, "evidence": "graduate/professional standing", "id": "n3", "kind": "condition" }, { "children": [], "condition": "declared in Statistics VISP", "course": null, "evidence": "declared in Statistics VISP", "id": "n4", "kind": "condition" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors None recorded. ### offerings None recorded. ### sections None recorded. ### grade instructors ```json [ { "instructor_uid": "instructor_20bff09bd8c7abcebab15a2f", "source": "madgrades", "source_instructor_id": "2602108", "identity_basis": "source_id", "identity_status": "source_identified", "name": "WEI-YIN LOH", "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": 24, "quality": 3.62, "difficulty": 3.71, "quality_count": 24, "difficulty_count": 24, "profile_id": "rmp:371080", "source_url": "https://www.ratemyprofessors.com/professor/371080", "match_basis": "exact_name", "observed_at": "2026-09-07 15:59:34.335211+00:00", "courses": { "course_3c746e3add116a6aa2b49441": { "review_count": 4, "quality": 4.25, "difficulty": 4, "quality_count": 4, "difficulty_count": 4 }, "course_bbb14bdd283ccb9368b888c2": { "review_count": 3, "quality": 2, "difficulty": 3.67, "quality_count": 3, "difficulty_count": 3 }, "course_d2bc347ed5e5c07178b1f567": { "review_count": 2, "quality": 2, "difficulty": 4.5, "quality_count": 2, "difficulty_count": 2 }, "course_2c47f118c8b20b84e9ca9d5d": { "review_count": 1, "quality": 3, "difficulty": 5, "quality_count": 1, "difficulty_count": 1 }, "course_1ea543c8a9e3b7f76f3a2d83": { "review_count": 1, "quality": 4, "difficulty": 3, "quality_count": 1, "difficulty_count": 1 }, "course_096be5272e913d9005e3298b": { "review_count": 2, "quality": 4.5, "difficulty": 3, "quality_count": 2, "difficulty_count": 2 } }, "bayesian_quality": 3.63803899252118, "prior_mean": 3.6596857835465957, "prior_weight": 20 }, "grade_statistics": { "gpa": 3.512, "graded": 1421, "counts": [ 632, 434, 239, 45, 52, 10, 9 ], "sections": 82 }, "instructor_url": "/instructors/WEI-YIN_LOH--instructor_20bff09bd8c7abcebab15a2f" }, { "instructor_uid": "instructor_26b12c9de8f3be3e7f69746c", "source": "madgrades", "source_instructor_id": "6024940", "identity_basis": "source_id", "identity_status": "source_identified", "name": "SIYU WANG", "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": 3.22, "graded": 75, "counts": [ 20, 24, 16, 7, 6, 0, 2 ], "sections": 3 }, "instructor_url": "/instructors/SIYU_WANG" }, { "instructor_uid": "instructor_1e7afb171cd31321e0bc3f60", "source": "madgrades", "source_instructor_id": "6239626", "identity_basis": "source_id", "identity_status": "source_identified", "name": "ZHIHAO 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": 3.672, "graded": 58, "counts": [ 27, 30, 0, 0, 0, 0, 1 ], "sections": 2 }, "instructor_url": "/instructors/ZHIHAO_ZHAO--instructor_1e7afb171cd31321e0bc3f60" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 443", "course_uid": "course_bbb14bdd283ccb9368b888c2", "term_id": "1232", "term_name": "Fall 2022", "instructors": [ "SIYU WANG", "Wei-Yin Loh" ], "a": 14, "ab": 16, "b": 11, "bc": 4, "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": 46, "source_aliases": [ "STAT 443" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "STAT 443", "course_uid": "course_bbb14bdd283ccb9368b888c2", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "Wei-Yin Loh", "Zhihao Zhao" ], "a": 27, "ab": 30, "b": 0, "bc": 0, "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": 58, "source_aliases": [ "STAT 443" ] } ] ``` ### statistics #### gpa 3.538 #### graded 104 #### counts * 41 * 46 * 11 * 4 * 0 * 0 * 2 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_bbb14bdd283ccb9368b888c2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_bbb14bdd283ccb9368b888c2-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_bbb14bdd283ccb9368b888c2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_bbb14bdd283ccb9368b888c2-0.json) #### reviews * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course\_bbb14bdd283ccb9368b888c2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/reviews/course_bbb14bdd283ccb9368b888c2-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_bbb14bdd283ccb9368b888c2-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_bbb14bdd283ccb9368b888c2-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.5384615384615383 #### count 104 #### university ##### size 2060 ##### gpa Percentile 31 ##### count Percentile 66 ##### median Count 73 ##### 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 | 193 | false | | 3.2–3.6 | 549 | true | | 3.6–4.0 | 1297 | false | #### departments ```json [ { "subject": "STAT", "comparison": { "size": 44, "gpaPercentile": 51, "countPercentile": 60, "medianCount": 83.5, "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": 11, "current": false }, { "range": "3.2–3.6", "count": 18, "current": true }, { "range": "3.6–4.0", "count": 15, "current": false } ] } } ] ``` ### terms #### 1232 ##### term 1232 ##### gpa 3.369565217391304 ##### count 46 ##### university ###### size 1216 ###### gpa Percentile 26 ###### count Percentile 30 ###### 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 | 2 | false | | 2.4–2.8 | 14 | false | | 2.8–3.2 | 174 | false | | 3.2–3.6 | 356 | true | | 3.6–4.0 | 670 | false | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 32, "gpaPercentile": 42, "countPercentile": 26, "medianCount": 60.5, "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": 11, "current": false }, { "range": "3.2–3.6", "count": 13, "current": true }, { "range": "3.6–4.0", "count": 8, "current": false } ] } } ] ``` #### 1264 ##### term 1264 ##### gpa 3.6724137931034484 ##### count 58 ##### university ###### size 1283 ###### gpa Percentile 47 ###### count Percentile 43 ###### 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 | false | | 3.6–4.0 | 789 | true | ##### departments ```json [ { "subject": "STAT", "comparison": { "size": 26, "gpaPercentile": 84, "countPercentile": 32, "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": false }, { "range": "3.6–4.0", "count": 7, "current": true } ] } } ] ``` ### benchmarks #### all ##### school ###### size 2060 ###### gpa 3.6370487936353983 ###### top Share 80.9690233932367 ###### count 73 ##### STAT ###### size 44 ###### gpa 3.4680899397213705 ###### top Share 71.28579540085639 ###### count 83.5 #### terms ##### 1232 ###### school ###### size 1216 ###### gpa 3.575457431972425 ###### top Share 77.33712216234007 ###### count 66 ###### STAT ###### size 32 ###### gpa 3.4024781451477413 ###### top Share 66.63145704464162 ###### count 60.5 ##### 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": 104, "terms": [ { "term": "1232", "count": 46, "sections": 2, "gpa": 3.369565217391304 }, { "term": "1264", "count": 58, "sections": 2, "gpa": 3.6724137931034484 } ] }, { "uid": "instructor_1e7afb171cd31321e0bc3f60", "name": "ZHIHAO ZHAO", "count": 58, "terms": [ { "term": "1264", "count": 58, "sections": 2, "gpa": 3.6724137931034484 } ] }, { "uid": "instructor_26b12c9de8f3be3e7f69746c", "name": "SIYU WANG", "count": 46, "terms": [ { "term": "1232", "count": 46, "sections": 2, "gpa": 3.369565217391304 } ] } ] ``` ## following None recorded.