printer friendly pagePUBLPOL 105: Empirical Methods in Public Policy (PUBLPOL 205)
Methods of empirical analysis and applications in public policy. Emphasis on causal inference and program evaluation. Public policy applications include health, education, and labor. Assignments include hands-on data analysis, evaluation of existing literature, and a final research project. Objective is to obtain tools to 1) critically evaluate evidence used to make policy decisions and 2) perform empirical analysis to answer questions in public policy. Prerequisites:
ECON 102B.
Terms: Spr
| Units: 5
| UG Reqs: WAY-AQR, WAY-SI
Instructors:
Chee, C. (PI)
;
Schoemaker, L. (PI)
PUBLPOL 157: Political Data Science (POLISCI 155)
Introduction to methods of research design and data analysis used in quantitative political research. Topics covered include hypothesis testing, linear regression, experimental and observational approaches to causal inference, effective data visualization, and working with big data. These topics will be introduced using data sets from American politics, international relations, and comparative politics. The course begins with an intensive introduction to the R programming language used throughout the course. Satisfies quantitative methods requirement for the Political Science Research Honors Track. Prerequisites: Stat 60 or instructor consent.
Terms: Aut
| Units: 5
| UG Reqs: WAY-AQR
Instructors:
Nall, C. (PI)
SOC 180B: Introduction to Data Analysis (SOC 280B)
Methods for analyzing and evaluating quantitative data in sociological research. Students will be taught how to run and interpret multivariate regressions, how to test hypotheses, and how to read and critique published data analyses. Limited enrollment; preference to Sociology majors.
Terms: Win
| Units: 4
| UG Reqs: GER:DB-SocSci, WAY-AQR, WAY-SI
Instructors:
Orth, T. (PI)
;
Powroznik, K. (PI)
STATS 48N: Riding the Data Wave
Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.
Terms: Aut
| Units: 3
| UG Reqs: WAY-AQR, WAY-FR
Instructors:
Sabatti, C. (PI)
STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Bacallado, S. (PI)
;
DiCiccio, C. (PI)
;
Mukherjee, R. (PI)
...
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Instructors:
Bacallado, S. (PI)
;
DiCiccio, C. (PI)
;
Mukherjee, R. (PI)
;
Sun, D. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
DiCiccio, C. (TA)
;
Janson, L. (TA)
;
Sun, D. (TA)
STATS 110: Statistical Methods in Engineering and the Physical Sciences
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum
| Units: 4-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
He, H. (PI)
;
Taylor, J. (PI)
STATS 116: Theory of Probability
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent.
Terms: Aut, Spr, Sum
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Benjamini, Y. (PI)
;
Khare, A. (PI)
;
Patterson, E. (PI)
...
more instructors for STATS 116 »
Instructors:
Benjamini, Y. (PI)
;
Khare, A. (PI)
;
Patterson, E. (PI)
;
Siegmund, D. (PI)
;
Su, W. (SI)
;
Huang, R. (TA)
;
Reid, S. (TA)
;
Sepehri, A. (TA)
;
Su, W. (TA)
STATS 141: Biostatistics (BIO 141)
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut
| Units: 3-5
| UG Reqs: GER:DB-Math, WAY-AQR
STATS 167: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 267)
Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of
STATS 60 or 116.
Last offered: Spring 2013
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
STATS 191: Introduction to Applied Statistics
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-Math, WAY-AQR
Instructors:
Taylor, J. (PI)
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