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01:960:285 · Statistics · School 01 — New Brunswick School of Arts and Sciences
Introductory Statistics for Business covers descriptive statistics, probability theory, random variables, sampling distributions, estimation, hypothesis testing, and one- and two-sample t-tests, giving business students the statistical vocabulary used later in analytics, operations, and evidence-based decision making.
How hard?
Students rate teaching
SIRS · 6,555 responses · 2014–2025
Spring 2027
9 open in snapshot · 4 instructors
When it meets
Livingston, C/D, Busch, College Avenue
Checked-in sections · current seat counts are unavailable.
W 10:20 AM · Livingston, C/D · 2 sections · 2 open
TF 8:30 AM · Livingston · 1 section · 1 open
M 7:30 PM · Busch · 1 section · 1 open
T 5:40 PM · Livingston · 1 section · 1 open
AI-generated overview
Higher means more demanding · AI summary
5 units · topic sequence may vary by instructor
The course starts by making raw business-style data readable. Before probability enters the picture, students usually learn how to summarize distributions, compare groups, and notice that averages are only part of the story when spread and unusual observations change the business interpretation.
The official synopsis puts probability theory early for a reason: business decisions are rarely made under certainty. This module usually teaches rules of probability and simple event reasoning so later statistical methods have a foundation instead of feeling like disconnected formulas.
Once uncertainty is formalized, the course moves into random variables and standard distribution families. The practical goal is to recognize which model fits a setting like counts, proportions, or approximately bell-shaped measurements, then use that model to predict how unusual an outcome really is.
This is the bridge from probability to inference. Students learn that a sample statistic changes from sample to sample, and that confidence intervals are a way to quantify how much uncertainty remains when estimating a population mean, proportion, or difference.
The course closes by turning uncertainty into decisions: when does the data support one policy, one market segment, or one process over another? Official topics specifically call out hypothesis testing and one- and two-sample t-tests, so students should expect comparison problems where setup, assumptions, and interpretation matter as much as the calculation.
Public r/rutgers discussion tends to frame 01:960:285 as manageable when students keep up with homework and practice problems, but also notably instructor-dependent in pacing and exam feel. The class is often described as doable rather than effortless.
Statistics matters in business because sales, wait times, prices, and customer behavior fluctuate. The course teaches how to describe that variability before trying to control or exploit it.
Most business questions are answered from a subset of customers, transactions, or time periods. Sampling distributions explain why the same process can generate slightly different answers each time.
Confidence intervals and hypothesis tests do not tell students what decision to make automatically. They quantify how strong the statistical evidence is under a particular model.
A correct calculation is not enough unless it is tied back to the business question, units, direction of difference, and assumptions behind the chosen method.
Common pitfall
Instead
Classify the setting first: one sample or two, mean or proportion, estimate or test. The procedure choice becomes much clearer after that.
Common pitfall
Instead
A result can be statistically detectable yet practically small, so interpret magnitude and context alongside significance.
Common pitfall
Instead
Check what the method assumes about independence, sampling, and the type of data before trusting the final conclusion.
9 open · Livingston, C/D, Busch, College Avenue
| Section | Status | Instructor | Meeting | Campus |
|---|---|---|---|---|
| 0116854 | Open | Gregory Manco | Wednesday 10:20 AM-11:40 AM at TIL 254; Friday 3:50 PM-5:10 PM at TIL 254TIL 254 | Livingston |
| 0216855 | Open | Gregory Manco | Tuesday 12:10 PM-1:30 PM at FSW 105; Friday 12:10 PM-1:30 PM at FSW 105FSW 105 | C/D |
| 0316856 | Open | Lynn Agre | Tuesday 8:30 AM-9:50 AM at TIL 232; Friday 8:30 AM-9:50 AM at TIL 232TIL 232 | Livingston |
| 0416857 | Open | Monday 12:10 PM-1:30 PM at EN B120; Thursday 12:10 PM-1:30 PM at EN B120EN B120 | Busch | |
| 0516858 | Open | Andrew Magyar | Monday 7:30 PM-10:30 PM at EN B120EN B120 | Busch |
| 0616859 | Open | Tuesday 3:50 PM-5:10 PM at MI 100; Thursday 3:50 PM-5:10 PM at MI 100MI 100 | College Avenue | |
| 0716860 | Open | Wednesday 5:40 PM-8:40 PM at MI 100MI 100 | College Avenue | |
| 0816861 | Open | Hk Dong | Tuesday 5:40 PM-8:40 PM at TIL 254TIL 254 | Livingston |
| 0916862 | Open | Monday 10:20 AM-11:40 AM at EN B120; Thursday 10:20 AM-11:40 AM at EN B120EN B120 | Busch |
Historical student surveys
Teaching
3.65
Course quality
3.61
Response rate
46.5%
Coverage
124 offerings · 2014–2025
| Instructor | Offerings | Teaching | Quality |
|---|---|---|---|
| Moondra Shyam | 15 | 3.99 | 3.91 |
| Shyam Moondra | 14 | 3.44 | 3.35 |
| Gregory Manco | 11 | 4.71 | 4.56 |
| Patricio Rojas | 9 | 2.99 | 3.17 |
| Lynn Agre | 7 | 3.40 | 3.30 |
| Zhanyun Zhao | 7 | 3.10 | 3.00 |
Catalog and planning context
Credits
3
Current campuses
Livingston, C/D, Busch, College Avenue
Current availability
9 open of 9
Catalog terms
Fall, Spring, Summer, Winter
Core codes
ITR, QQ, QR
Loaded terms
8
Any Course EQUAL or GREATER Than: (01:640:112)
No direct degree-list membership appears in the checked-in requirement index.