Course Description

Introductory Probability and Statistics for Business, STAT W21

Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

Key Information

Credit: 6 quarter units / 4 semester units credit
UC Berkeley, Statistics

Course Credit:

Upon successful completion, all online courses offered through cross-enrollment provide UC unit credit. Some courses are approved for GE, major preparation and/or, major credit or can be used as a substitute for a course at your campus.

If "unit credit" is listed by your campus, consult your department, academic adviser or Student Affairs division to inquire about the petition process for more than unit credit for the course.

UC Berkeley:
General Education: Quantitative Reasoning
Major Preparation: Pre-major credit

UC Davis:
Course Equivalence: UCD STA 013

UC Irvine:
Unit Credit

UC Los Angeles:
Major Preparation: Psychology Major, Political Science, Sociology
Course Equivalence: Statistics 10
Quantitative Reasoning

UC Merced:
Units toward degree (see your advisor)

UC Riverside:
General Education: STAT Elective Units

UC San Diego:
General Education: TMC 1 course in Mathematics, Statistics & Logic, Introductory Stats Area; Sixth - Exploring Data GE; ERC - Formal Skills; Warren - Formal skills, Revelle math (also need 2 calculus courses); Muir: may count toward a math GE sequence in lieu of MATH 11
Major Requirement: Equivalent to COGS 14B: Intro to Statistics for COGS majors
Course Equivalence: UCSD Math 11 OR COGS 14B (for COGS majors)

UC San Francisco:
Unit Credit

UC Santa Barbara:
General Education: Possible Area C after petition
Course Equivalence: Likely equivalent to: PSTAT 5A after petition

UC Santa Cruz:
General Education: SR
Course Equivalence: AMS 5

More About The Course

Reasoning and fallacies, descriptive statistics, probability models and related concepts, combinatorics, sample surveys, estimates, confidence intervals, tests of significance, controlled experiments vs. observational studies, correlation and regression.

Course Creators

Shobhana Murali Stoyanov
Shobhana Murali Stoyanov is a Continuing Lecturer in the Department of Statistics at the University of California, Berkeley. She began working there in the fall semester of 2009, teaching Stat 155, and has taught a variety of undergraduate courses, including Stat 2, Stat 20, Stat 21, Stat 131A, Stat 134, Stat 151A, Stat 152, and Stat 155. Prior to working at Berkeley, Shobhana was an adjunct Professor in the Department of Mathematics at Saint Mary's College of California, where she had re-entered academia as a lecturer in 2007, after a taking a few years off. After completing her doctorate, Shobhana spent a year at Lehigh University, where she was the C.C.Hsiung Visiting Assistant Professor. The courses she taught there were upper and lower division introductory probability and statistics. Shobhana received her Ph.D. in Applied Mathematics from the Georgia Institute of Technology in 2001, where Professor Christian Houdré was her thesis advisor. Shobhana Murali Stoyanov is a Continuing Lecturer in the Department of Statistics at the University of California, Berkeley. She began working there in the fall semester of 2009, teaching Stat 155, and has taught a variety of undergraduate courses, including Stat 2, Stat 20, Stat 21, Stat 131A, Stat 134, Stat 151A, Stat 152, and Stat 155. Prior to working at Berkeley, Shobhana was an adjunct ...

Shobhana Murali Stoyanov is a Continuing Lecturer in the Department of Statistics at the University of California, Berkeley. She began working there in the fall semester of 2009, teaching Stat 155, and has taught a variety of undergraduate courses, including Stat 2, Stat 20, Stat 21, Stat 131A, Stat 134, Stat 151A, Stat 152, and Stat 155. Prior to working at Berkeley, Shobhana was an adjunct Professor in the Department of Mathematics at Saint Mary's College of California, where she had re-entered academia as a lecturer in 2007, after a taking a few years off. After completing her doctorate, Shobhana spent a year at Lehigh University, where she was the C.C.Hsiung Visiting Assistant Professor. The courses she taught there were upper and lower division introductory probability and statistics. Shobhana received her Ph.D. in Applied Mathematics from the Georgia Institute of Technology in 2001, where Professor Christian Houdré was her thesis advisor.

Philip B. Stark
Philip B. Stark is a professor in the Department of Statistics at UC Berkeley. His research centers on inference (inverse) problems, especially confidence procedures tailored for specific goals. He has published on the Big Bang, causal inference, the U.S. census, climate modeling, earthquake prediction, election auditing, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics (confidence sets for function and probability density estimates with constraints), risk assessment, the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. He is a pioneer of online teaching; his Statistics W21 was the first official online course offered by UC Berkeley. Prof. Stark has consulted for many branches of government, including the Federal Trade Commission, the Department of Agriculture, the Department of Commerce, the Department of Housing and Urban Development, the Department of Justice, the House of Representatives, the California Secretary of State, the California Highway Patrol, the Colorado Secretary of State, and the Illinois State Attorney. Prof. Stark has been a consultant or expert witness for companies including Apple Inc., AT&T, Capital One, Farmers Insurance, Freddie Mac, GMAC, HSBC, K-Mart, R.J. Reynolds, and Verizon Wireless. He has consulted on topics including truth in advertising, election contests, equal protection under the law, intellectual property and patent litigation, jury selection, trade secret litigation, employment discrimination litigation, import restrictions, insurance litigation, natural resource legislation, environmental litigation, sampling in litigation, wage and hour class actions, product liability class actions, consumer class actions, the U.S. census, clinical trials, signal processing, geochemistry, IC mask quality control, behavioral targeting, water treatment, sampling the web, risk assessment, credit risk models, and oil exploration. Philip B. Stark is a professor in the Department of Statistics at UC Berkeley. His research centers on inference (inverse) problems, especially confidence procedures tailored for specific goals. He has published on the Big Bang, causal inference, the U.S. census, climate modeling, earthquake prediction, election auditing, food web models, the geomagnetic field, geriatric hearing loss, ...

Philip B. Stark is a professor in the Department of Statistics at UC Berkeley. His research centers on inference (inverse) problems, especially confidence procedures tailored for specific goals. He has published on the Big Bang, causal inference, the U.S. census, climate modeling, earthquake prediction, election auditing, food web models, the geomagnetic field, geriatric hearing loss, information retrieval, Internet content filters, nonparametrics (confidence sets for function and probability density estimates with constraints), risk assessment, the seismic structure of Sun and Earth, spectroscopy, spectrum estimation, and uncertainty quantification for computational models of complex systems. He is a pioneer of online teaching; his Statistics W21 was the first official online course offered by UC Berkeley. Prof. Stark has consulted for many branches of government, including the Federal Trade Commission, the Department of Agriculture, the Department of Commerce, the Department of Housing and Urban Development, the Department of Justice, the House of Representatives, the California Secretary of State, the California Highway Patrol, the Colorado Secretary of State, and the Illinois State Attorney. Prof. Stark has been a consultant or expert witness for companies including Apple Inc., AT&T, Capital One, Farmers Insurance, Freddie Mac, GMAC, HSBC, K-Mart, R.J. Reynolds, and Verizon Wireless. He has consulted on topics including truth in advertising, election contests, equal protection under the law, intellectual property and patent litigation, jury selection, trade secret litigation, employment discrimination litigation, import restrictions, insurance litigation, natural resource legislation, environmental litigation, sampling in litigation, wage and hour class actions, product liability class actions, consumer class actions, the U.S. census, clinical trials, signal processing, geochemistry, IC mask quality control, behavioral targeting, water treatment, sampling the web, risk assessment, credit risk models, and oil exploration.

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