STATS 101A
Introduction to Data Analysis and Regression
Grades
3.66
Average grade awarded in STATS 101A. Not a student rating: this is what the grades were.
In the A range
75%
of the 1,617 letter grades
D, F or W
1.2%
of every letter grade awarded, plus withdrawals
Taken pass/no pass
<1%
4 of 5 students passed
The registrar's own records: 1,617 grades over 12 terms, Fall 2021 to Fall 2025. Every instructor is pooled here.
What is inside that record
4 of those terms are summer sessions, and summer sessions are counted here.
The table below splits them, and term averages across those 12 terms ran 3.18 to 3.95.
Historical grades for all professors, across recorded terms. The course summary above includes all professors.
- A+ 327 · 20.2%
- A 643 · 39.8%
- A- 250 · 15.5%
- B+ 130 · 8.0%
- B 131 · 8.1%
- B- 61 · 3.8%
- C+ 29 · 1.8%
- C 27 · 1.7%
- C- 1 · 0.1%
- D+ 5 · 0.3%
- D 4 · 0.2%
- D- 2 · 0.1%
- F 7 · 0.4%
- P 4 of 1,626
- NP 1 of 1,626
- I 3 of 1,626
- DR 1 of 1,626
Percentages are of the 1,617 letter grades. Grey bars are non-letter outcomes: passes (P), no-passes (NP), incompletes (I), and withdrawals (DR).
How the non-letter outcomes are counted
They are excluded from the GPA entirely rather than scored, because counting a P as a 4.0 would be a fabrication.
Withdrawals are the exception to "excluded": DR is not scored, but it IS counted in the D, F or W figure above, which is reported over the letter grades plus the withdrawals. Pass/no pass and S/U outcomes are outside that figure on both sides.
By instructor
| Instructor | Predicted GPA | Raw | n | Terms | A range | Workload | Winter 2027 |
|---|---|---|---|---|---|---|---|
| CHA, MARIA reviews ↗ | 3.68 | 3.68 | 711 | 6 | 78% | - | not teaching it |
| GOULD, ROBERT L reviews ↗ | 3.66 | 3.66 | 325 | 4 | 72% | - | not teaching it |
| ALMOHALWAS, AKRAM M reviews ↗ | 3.43 | 3.41 | 184 | 2 | 61% | - | not teaching it |
| ZANONTIAN, LINDA ANIA reviews ↗ | 3.69 | 3.70 | 161 | 1 | 78% | - | not teaching it |
| XU, SHIRONG reviews ↗ | 3.71 | 3.72 | 153 | 1 | 75% | - | not teaching it |
| LEW, VIVIAN reviews ↗ | 3.90 | 3.95 | 83 | 1 | 98% | - | not teaching it |
Why Predicted is not the Raw average
“Predicted” is shrunk toward the course average, which is itself shrunk toward the department, so an instructor with a handful of students sits near the course mean rather than topping the list.
Grade records describe outcomes. For what a course is like to take, read student reviews written on dibs below or follow the separate external review link.
Workload and assignments
Work types reported in BruinWalk reviews of this course, across recorded instructors and terms. They may differ from this term's syllabus.
dibs has not read this course’s reviews, so it cannot say what kind of work it sets. That is dibs not having looked. It is not a course without papers or exams, and the reviews linked below are the answer until dibs catches up.
What comes first
dibs has not read a class page for STATS 101A in any term it holds, so it cannot say what comes before it.
Counted in courses rather than quarters: dibs does not know which of them a department lets you take together.
In Winters
Ran in 4 of the last 4 Winters.
From the terms dibs holds, which start in Fall 2021 and exclude summer. A department can run a course in a quarter it never has before.
From the registrar's catalog
Lecture, three hours; discussion, one hour. Requisites: one course from course 10, 12, 13, 15, Economics 41, or Psychology 100A, or score of 4 or higher on Advanced Placement Statistics Examination, and course 20. Recommended: course 102A. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.
Sections in Winter 2027
From the people who took it
Student reviews
What taking STATS 101A was like: the work, the teaching, and what students wish they’d known.
Your experience could be the first.
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