# Statistics

The Department of Statistics offers courses in the basic principles and techniques of probability and statistics, advanced theory and methods courses, courses in stochastic processes and methods, and courses statistical methods in finance.

For questions about specific courses, contact the department.

Directory of Classes

The course information displayed on this page relies on an external system and may be incomplete. Please visit Statistics on the Directory of Classes for complete course information.

After finding your course in the Directory of Classes, click on the section number to open an expanded view. The "Open To" field will indicate whether the course is open to School of Professional Studies students. If School of Professional Studies is not included in the field, students may still be able to cross-register for the course by obtaining permission after being admitted to an academic program.

STAT GR5291 Advanced Data Analysis. 3 points.

Prerequisites: W4315 and either another statistics course numbered above the 4200 or permission of instructor.
Required for the major in statistics. Data analysis using a computer statistical package and selected exploratory data analysis subroutines. Topics include editing of data for errors, exploratory and standard techniques for one-way analysis of variance, linear regression, and two-way analysis of variance. Material is presented in case-study format

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT GR5291 STAT 5291 001/63855 F 10:10am - 12:40pm614 Schermerhorn Hall Hammou ElBarmi 3 90/100 Fall 2019: STAT GR5291 STAT 5291 001/48498 F 5:10pm - 7:40pmRoom TBA Demissie Alemayehu 3 234/275

STAT GU4001 Introduction to Probability and Statistics. 3 points.

BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: Calculus through multiple integration and infinite sums.

A calculus-based tour of the fundamentals of probability theory and statistical inference. Probability models, random variables, useful distributions, conditioning, expectations, law of large numbers, central limit theorem, point and confidence interval estimation, hypothesis tests, linear regression.  This course replaces SIEO 4150.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT GU4001 STAT 4001 001/10448 T Th 1:10pm - 2:25pm207 Mathematics Building David Rios 3 121/150 Fall 2019: STAT GU4001 STAT 4001 001/48429 T Th 2:40pm - 3:55pmRoom TBA Gabriel Young 3 117/125 STAT 4001 002/48430 M W 5:40pm - 6:55pmRoom TBA Larry Wright 3 67/125

STAT GU4204 Statistical Inference. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4203. At least one semester of calculus is required; two or three semesters are strongly recommended.

Calculus-based introduction to the theory of statistics. Useful distributions, law of large numbers and central limit theorem, point estimation, hypothesis testing, confidence intervals maximum likelihood, likelihood ratio tests, nonparametric procedures, theory of least squares and analysis of variance.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT GU4204 STAT 4204 001/71372 M W 2:40pm - 3:55pm313 Fayerweather Thibault Vatter 3 16/75 STAT 4204 002/70013 T Th 6:10pm - 7:25pm104 Green Hall Law Building Gabriel Young 3 37/50 Fall 2019: STAT GU4204 STAT 4204 003/48440 M W 6:10pm - 7:25pmRoom TBA Ronald Neath 3 56/65

STAT GU4231 Survival Analysis. 0 points.

BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: STAT GU4205 or the equivalent.

Survival distributions, types of censored data, estimation for various survival models, nonparametric estimation of survival distributions, the proportional hazard and accelerated lifetime models for regression analysis with failure-time data. Extensive use of the computer.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT GU4231 STAT 4231 001/73265 M W 6:10pm - 7:25pm209 Havemeyer Hall Michael Shnaidman 0 5/25

STAT GU4282 Linear Regression and Time Series Methods. 3 points.

CC/GS: Partial Fulfillment of Science Requirement

Prerequisites: STAT GU4204 or the equivalent.

A one semester course covering: simple and multiple regression, including testing, estimation, and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, colinearity and confounding, model selection, geometry of least squares. Linear time series models. Auto-regressive, moving average and ARIMA models. Estimation and forecasting with time series models. Confidence intervals and prediction error. Students may not receive credit for more than two of STAT W4315, W4437, and W4440. Satisfies the SOA VEE requirements in regression and in time-series.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT GU4282 STAT 4282 001/24483 M W 2:40pm - 3:55pm214 Pupin Laboratories Flavio Bartmann 3 17/50 Fall 2019: STAT GU4282 STAT 4282 001/48459 T Th 1:10pm - 2:25pmRoom TBA Flavio Bartmann 3 7/50

STAT UN1001 Introduction to Statistical Reasoning. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

A friendly introduction to statistical concepts and reasoning with emphasis on developing statistical intuition rather than on mathematical rigor. Topics include design of experiments, descriptive statistics, correlation and regression, probability, chance variability, sampling, chance models, and tests of significance.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT UN1001 STAT 1001 001/22551 M W 10:10am - 11:25am702 Hamilton Hall Guy Cohen 3 81/86 STAT 1001 002/73054 T Th 2:40pm - 3:55pm602 Hamilton Hall Ronald Neath 3 64/86 STAT 1001 003/29213 T Th 6:10pm - 7:25pm903 School Of Social Work Yayun Hsu 3 19/50 Fall 2019: STAT UN1001 STAT 1001 001/48410 M W 10:10am - 11:25amRoom TBA Guy Cohen 3 96/87 STAT 1001 002/48415 T Th 8:40am - 9:55amRoom TBA Ronald Neath 3 23/86 STAT 1001 003/48416 M W 6:10pm - 7:25pmRoom TBA Anthony Donoghue 3 15/86

STAT UN1101 Introduction to Statistics. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: intermediate high school algebra.

Designed for students in fields that emphasize quantitative methods. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, analysis of variance, confidence intervals and hypothesis testing. Quantitative reasoning and data analysis. Practical experience with statistical software. Illustrations are taken from a variety of fields. Data-collection/analysis project with emphasis on study designs is part of the coursework requirement.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT UN1101 STAT 1101 001/64911 M W 8:40am - 9:55am420 Pupin Laboratories Hok Kan Ling 3 45/50 STAT 1101 002/11022 T Th 8:40am - 9:55am310 Fayerweather Anthony Donoghue 3 63/86 STAT 1101 003/70920 T Th 6:10pm - 7:25pm602 Hamilton Hall Ha Nguyen 3 74/86 Fall 2019: STAT UN1101 STAT 1101 001/48411 M W 8:40am - 9:55amRoom TBA Banu Baydil 3 47/86 STAT 1101 002/10361 T Th 11:40am - 12:55pmRoom TBA Banu Baydil 3 59/86 STAT 1101 003/48412 T Th 6:10pm - 8:15pmRoom TBA Ha Nguyen 3 23/86

STAT UN1201 Calculus-Based Introduction to Statistics. 3 points.

CC/GS: Partial Fulfillment of Science Requirement, BC: Fulfillment of General Education Requirement: Quantitative and Deductive Reasoning (QUA).

Prerequisites: one semester of calculus.

Designed for students who desire a strong grounding in statistical concepts with a greater degree of mathematical rigor than in STAT W1111. Random variables, probability distributions, pdf, cdf, mean, variance, correlation, conditional distribution, conditional mean and conditional variance, law of iterated expectations, normal, chi-square, F and t distributions, law of large numbers, central limit theorem, parameter estimation, unbiasedness, consistency, efficiency, hypothesis testing, p-value, confidence intervals, maximum likelihood estimation. Serves as the pre-requisite for ECON W3412.

Course Number Section/Call Number Times/Location Instructor Points Enrollment Spring 2019: STAT UN1201 STAT 1201 001/70138 T Th 10:10am - 11:25am501 Northwest Corner David Rios 3 58/86 STAT 1201 002/67351 M W 8:40am - 9:55am602 Hamilton Hall Joyce Robbins 3 83/86 STAT 1201 003/68149 T Th 8:40am - 9:55am517 Hamilton Hall Joyce Robbins 3 72/86 STAT 1201 004/70428 T Th 6:10pm - 7:25pmRoom TBA Daniel Rabinowitz 3 65/86 Fall 2019: STAT UN1201 STAT 1201 001/48420 M W 11:40am - 12:55pmRoom TBA Sumit Mukherjee 3 86/86 STAT 1201 002/48422 T Th 8:40am - 9:55amRoom TBA Joyce Robbins 3 86/86 STAT 1201 003/48423 M W 10:10am - 11:25amRoom TBA Philip Protter 3 19/86 STAT 1201 004/48424 T Th 6:10pm - 7:25pmRoom TBA Samory Kpotufe 3 29/86

STAT W4201 Advanced Data Analysis. 3 points.

Prerequisites: W4315 and either another statistics course numbered above the 4200 or permission of instructor.
Required for the major in statistics. Data analysis using a computer statistical package and selected exploratory data analysis subroutines. Topics include editing of data for errors, exploratory and standard techniques for one-way analysis of variance, linear regression, and two-way analysis of variance. Material is presented in case-study format

STAT W4315 Linear Regression Models. 3 points.

Prerequisites: STAT W3000 or the equivalent and STAT W3659 or the equivalent.
Corequisites: MATH V2110, V1101, and V1102
Theory and practice of regression analysis. Simple and multiple regression including testing, estimation and confidence procedures, modeling, regression diagnostics and plots, polynomial regression, fixed effects ANOVA and ANCOVA models, nonlinear regression, multiple comparisons, co-linearity and confounding, model selection. Emphasis on geometric approach to the theory and computer use to analyze data

The University reserves the right to withdraw or modify the courses of instruction or to change the instructors as may become necessary.