# 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.

For questions about specific courses, contact the department.

### Courses

#### Spring 2019

##### Course Number

STAT 5291##### Section/Call Number

001/63855##### Times/Location

F 10:10a - 12:40p614 SCHERMERHORN HALL

##### Instructor

Hammou ElBarmi##### Points

3##### Enrollment

90##### Prerequisite

W4315 and either another statistics course numbered above the 4200 or permission of instructor.#### Spring 2019

##### Course Number

STAT 5291##### Section/Call Number

001/48498##### Times/Location

F 5:10p - 7:40p309 HAVEMEYER HALL

##### Instructor

Demissie Alemayehu##### Points

3##### Enrollment

241##### Prerequisite

W4315 and either another statistics course numbered above the 4200 or permission of instructor.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.

#### Spring 2019

##### Course Number

STAT 4001##### Section/Call Number

001/10448##### Times/Location

Tu Th 1:10p - 2:25p207 MATHEMATICS BUILDING

##### Instructor

David Rios##### Points

3##### Enrollment

120##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 4001##### Section/Call Number

001/48429##### Times/Location

Tu Th 2:40p - 3:55p501 SCHERMERHORN HALL

##### Instructor

Ronald Neath##### Points

3##### Enrollment

131##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 4001##### Section/Call Number

002/48430##### Times/Location

M W 5:40p - 6:55p103 JEROME L GREENE HALL

##### Instructor

Tat Sang Fung##### Points

3##### Enrollment

86##### Prerequisite

Calculus through multiple integration and infinite sums.#### Spring 2019

##### Course Number

STAT 4001##### Section/Call Number

003/13294##### Times/Location

Tu Th 11:40a - 12:55p428 PUPIN LABORATORIES

##### Instructor

David Rios##### Points

3##### Enrollment

40##### Prerequisite

Calculus through multiple integration and infinite sums.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.

#### Spring 2019

##### Course Number

STAT 4204##### Section/Call Number

001/71372##### Times/Location

M W 2:40p - 3:55p313 FAYERWEATHER

##### Instructor

Thibault Vatter##### Points

3##### Enrollment

15##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 4204##### Section/Call Number

002/70013##### Times/Location

Tu Th 6:10p - 7:25p104 GREEN HALL LAW BUILDING

##### Instructor

Gabriel Young##### Points

3##### Enrollment

37##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 4204##### Section/Call Number

003/48440##### Times/Location

M W 6:10p - 7:25p428 PUPIN LABORATORIES

##### Instructor

Ronald Neath##### Points

3##### Enrollment

52##### Prerequisite

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

#### Spring 2019

##### Course Number

STAT 4231##### Section/Call Number

001/73265##### Times/Location

M W 6:10p - 7:25p209 HAVEMEYER HALL

##### Instructor

Michael Shnaidman##### Enrollment

5##### Prerequisite

STAT GU4205 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.

#### Spring 2019

##### Course Number

STAT 4282##### Section/Call Number

001/24483##### Times/Location

M W 2:40p - 3:55p214 PUPIN LABORATORIES

##### Instructor

Flavio Bartmann##### Points

3##### Enrollment

17##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 4282##### Section/Call Number

001/48459##### Times/Location

Tu Th 1:10p - 2:25p703 HAMILTON HALL

##### Instructor

Flavio Bartmann##### Points

3##### Enrollment

11##### Prerequisite

STAT GU4204 or the equivalent.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.

#### Spring 2019

##### Course Number

STAT 1001##### Section/Call Number

001/22551##### Times/Location

M W 10:10a - 11:25a702 HAMILTON HALL

##### Instructor

Guy Cohen##### Points

3##### Enrollment

80A 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.

#### Spring 2019

##### Course Number

STAT 1001##### Section/Call Number

002/73054##### Times/Location

Tu Th 2:40p - 3:55p602 HAMILTON HALL

##### Instructor

Ronald Neath##### Points

3##### Enrollment

64A 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.

#### Spring 2019

##### Course Number

STAT 1001##### Section/Call Number

003/29213##### Times/Location

Tu Th 6:10p - 7:25p903 SCHOOL OF SOCIAL WORK

##### Instructor

Yayun Hsu##### Points

3##### Enrollment

18#### Spring 2019

##### Course Number

STAT 1001##### Section/Call Number

001/48410##### Times/Location

M W 10:10a - 11:25a614 SCHERMERHORN HALL

##### Instructor

Guy Cohen##### Points

3##### Enrollment

124#### Spring 2019

##### Course Number

STAT 1001##### Section/Call Number

002/48415##### Times/Location

Tu Th 8:40a - 9:55a517 HAMILTON HALL

##### Instructor

Ronald Neath##### Points

3##### Enrollment

55#### Spring 2019

##### Course Number

STAT 1001##### Section/Call Number

003/48416##### Times/Location

M W 6:10p - 7:25p517 HAMILTON HALL

##### Instructor

Anthony Donoghue##### Points

3##### Enrollment

35Designed 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.

#### Spring 2019

##### Course Number

STAT 1101##### Section/Call Number

001/64911##### Times/Location

M W 8:40a - 9:55a420 PUPIN LABORATORIES

##### Instructor

Hok Kan Ling##### Points

3##### Enrollment

45##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 1101##### Section/Call Number

002/11022##### Times/Location

Tu Th 8:40a - 9:55a310 FAYERWEATHER

##### Instructor

Anthony Donoghue##### Points

3##### Enrollment

62##### Prerequisite

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.

#### Spring 2019

##### Course Number

STAT 1101##### Section/Call Number

003/70920##### Times/Location

Tu Th 6:10p - 7:25p602 HAMILTON HALL

##### Instructor

Ha Nguyen##### Points

3##### Enrollment

73##### Prerequisite

intermediate high school algebra.#### Spring 2019

##### Course Number

STAT 1101##### Section/Call Number

001/48411##### Times/Location

M W 8:40a - 9:55a517 HAMILTON HALL

##### Instructor

Banu Baydil##### Points

3##### Enrollment

58##### Prerequisite

intermediate high school algebra.#### Spring 2019

##### Course Number

STAT 1101##### Section/Call Number

002/10361##### Times/Location

Tu Th 11:40a - 12:55p702 HAMILTON HALL

##### Instructor

Banu Baydil##### Points

3##### Enrollment

77##### Prerequisite

intermediate high school algebra.#### Spring 2019

##### Course Number

STAT 1101##### Section/Call Number

003/48412##### Times/Location

M W 6:10p - 7:25p602 HAMILTON HALL

##### Instructor

Ha Nguyen##### Points

3##### Enrollment

48##### Prerequisite

intermediate high school algebra.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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

001/70138##### Times/Location

Tu Th 10:10a - 11:25a501 NORTHWEST CORNER

##### Instructor

David Rios##### Points

3##### Enrollment

56##### Prerequisite

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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

002/67351##### Times/Location

M W 8:40a - 9:55a602 HAMILTON HALL

##### Instructor

Joyce Robbins##### Points

3##### Enrollment

82##### Prerequisite

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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

003/68149##### Times/Location

Tu Th 8:40a - 9:55a517 HAMILTON HALL

##### Instructor

Joyce Robbins##### Points

3##### Enrollment

72##### Prerequisite

one semester of calculus.*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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

004/70428##### Times/Location

Tu Th 6:10p - 7:25pRoom TBA Building TBA

##### Instructor

Daniel Rabinowitz##### Points

3##### Enrollment

65##### Prerequisite

one semester of calculus.*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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

001/48420##### Times/Location

M W 11:40a - 12:55p602 HAMILTON HALL

##### Instructor

Sumit Mukherjee##### Points

3##### Enrollment

68##### Prerequisite

one semester of calculus.*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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

002/48422##### Times/Location

Tu Th 8:40a - 9:55a602 HAMILTON HALL

##### Instructor

Hammou ElBarmi##### Points

3##### Enrollment

81##### Prerequisite

one semester of calculus.*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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

003/48423##### Times/Location

M W 10:10a - 11:25a602 HAMILTON HALL

##### Instructor

Philip Protter##### Points

3##### Enrollment

65##### Prerequisite

one semester of calculus.*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*.

#### Spring 2019

##### Course Number

STAT 1201##### Section/Call Number

004/48424##### Times/Location

Tu Th 6:10p - 7:25p602 HAMILTON HALL