## Statistics

The Statistics Department offers a range of courses that build on a foundation in probability and statistical theory to provide practical training in statistical methods, study design, and data analysis.

The courses on this page reflect Summer 2019 offerings.

Session 1 (D) courses are May 28 - July 5. Session 2 (Q) courses are July 8 - August 16.

Visit our calendar for a complete list of Summer dates.

##### Courses

Expand All###### Introduction to Statistics (without calculus)

###### STAT S1101D 3 points.

Prerequisites: some high school algebra.

Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. 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 |
---|---|---|---|---|---|

STAT 1101 | 001/13662 | M Tu W Th 10:45a - 12:10p Room TBA Building TBA | Miguel Garrido Garcia | 3 | 14 |

###### Introduction to Statistics (without calculus)

###### STAT S1101Q 3 points.

Prerequisites: some high school algebra.

Designed for students in fields that emphasize quantitative methods. This course satisfies the statistics requirements of all majors except statistics, economics, and engineering. Graphical and numerical summaries, probability, theory of sampling distributions, linear regression, confidence intervals, and hypothesis testing are taught as aids to quantitative reasoning and data analysis. Use of statistical software required. 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 |
---|---|---|---|---|---|

STAT 1101 | 002/73611 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Nicholas Galbraith | 3 | 16 |

###### Introduction to Statistics (with calculus)

###### STAT S1201D 3 points.

Prerequisites: working knowledge of calculus (differentiation and integration).

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. Satisfies the pre-requisites for *ECON W3412*.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 1201 | 001/64699 | M Tu W Th 10:45a - 12:20p Room TBA Building TBA | Hammou ElBarmi | 3 | 15 |

###### Introduction to Statistics (with calculus)

###### STAT S1201Q 3 points.

Prerequisites: working knowledge of calculus (differentiation and integration).

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. Satisfies the pre-requisites for *ECON W3412*.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 1201 | 002/21358 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Promit Ghosal | 3 | 15 |

###### Introduction to Probability and Inference

###### STAT S4001D 3 points.

Prerequisites: A good working knowledge of calculus, including derivatives, single and double, limits, sums and series.

Life is a gamble and with some knowledge of probability / statistics is easier evaluate the risks and rewards involved. Probability theory allows us take a known underlying model and estimate how likely will we be able to see future events. Statistical Inference allows us to take data we have seen and estimate the missing parts of an unknown model. The first part of the course focus on the former and the second part the latter.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 4001 | 001/12446 | M Tu W Th 4:30p - 6:15p Room TBA Building TBA | David Rios | 3 | 9 |

###### Statistical Computing in SAS

###### STAT S4199D 3 points.

Data handling in SAS (including SAS INPUT, reading and writing raw and system files, data set subsetting, concatenating, merging, updating and working with arrays), SAS MACROS, common SAS functions, and graphics in SAS. Review of SAS tools for exploratory data analysis, and common statistical procedures (including, categorical data, dates and longitudinal data, correlation and regression, nonparametric comparisons, ANOVA, multiple regression, multivariate data analysis).

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 4199 | 001/29053 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Anthony Donoghue | 3 | 4 |

###### Probability

###### STAT S4203D 3 points.

Prerequisites: *MATH V1101* Calculus I and *MATH V1102* Calculus II, or the equivalent, and *STAT W1111* or *STAT W1211* (Introduction to Statistics).

Corequisites: *MATH V1201* Calculus III, or the equivalent, or the instructor's permission.

This course can be taken as a single course for students requiring knowledge of probability or as a foundation for more advanced courses. It is open to both undergraduate and master students. This course satisfies the prerequisite for *STAT W3107* and *W4107.* Topics covered include combinatorics, conditional probability, random variables and common distributions, expectation, independence, Bayes' rule, joint distributions, conditional expectations, moment generating functions, central limit theorem, laws of large numbers, characteristic functions.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 4203 | 001/72395 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Young Kim | 3 | 15 |

###### Statistical Inference

###### STAT S4204Q 3 points.

Prerequisites: *STAT W3105* Intro. to Probability or *STAT W4105* Probability, or the equivalent.

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 |
---|---|---|---|---|---|

STAT 4204 | 001/63483 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Marco Avella | 3 | 23 |

###### Time Series Analysis

###### STAT S4221D 3 points.

Prerequisites: STAT GU4205 or the equivalent.

Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 4221 | 001/71179 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Abolfazal Safikhani | 3 | 2 |

###### Statistical Machine Learning

###### STAT S4241D 3 points.

MA students in Statistics should register for STAT S5241

Prerequisites: STAT GU4206

The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 4241 | 001/62267 | M Tu W Th 2:45p - 4:20p Room TBA Building TBA | Gabriel Young | 3 | 20 |

###### Statistical Methods for Finance

###### STAT S4261D 3 points.

MA students in Statistics should register for STAT S5261

Prerequisites: STAT GU4204 and STAT GU4205

A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 4261 | 001/10014 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Pawel Polak | 3 | 12 |

###### Statistical Computing and Introduction to Data Science

###### STAT S5206D 3 points.

Prerequisites: STAT GU5204 and STAT GU5205

Open to MA students in Statistics only

Introduction to programming in the R statistical package: functions, objects, data structures, flow control, input and output, debugging, logical design, and abstraction. Writing code for numerical and graphical statistical analyses. Writing maintainable code and testing, stochastic simulations, paralleizing data analyses, and working with large data sets. Examples from data science will be used for demonstration.

###### Time Series Analysis

###### STAT S5221D 3 points.

Open to MA students in Statistics only

Prerequisites: STAT GU4205 or the equivalent. Least squares smoothing and prediction, linear systems, Fourier analysis, and spectral estimation. Impulse response and transfer function. Fourier series, the fast Fourier transform, autocorrelation function, and spectral density. Univariate Box-Jenkins modeling and forecasting. Emphasis on applications. Examples from the physical sciences, social sciences, and business. Computing is an integral part of the course.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 5221 | 001/27837 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Abolfazal Safikhani | 3 | 5 |

###### Statistical Machine Learning

###### STAT S5241D 3 points.

Prerequisites: STAT GR5206 or the equivalent.

Open to MA students in Statistics only

The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to provide students of statistics with detailed knowledge of how Machine Learning methods work and how statistical models can be brought to bear in computer systems - not only to analyze large data sets, but to let computers perform tasks that traditional methods of computer science are unable to address. Examples range from speech recognition and text analysis through bioinformatics and medical diagnosis. This course provides a first introduction to the statistical methods and mathematical concepts which make such technologies possible.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 5241 | 001/18926 | M Tu W Th 2:45p - 4:20p Room TBA Building TBA | Gabriel Young | 3 | 21 |

###### Statistical Methods for Finance

###### STAT S5261D 3 points.

Prerequisites: STAT GR5204 or the equivalent. STAT GR5205 is recommended.

Open to MA students in Statistics only

A fast-paced introduction to statistical methods used in quantitative finance. Financial applications and statistical methodologies are intertwined in all lectures. Topics include regression analysis and applications to the Capital Asset Pricing Model and multifactor pricing models, principal components and multivariate analysis, smoothing techniques and estimation of yield curves statistical methods for financial time series, value at risk, term structure models and fixed income research, and estimation and modeling of volatilities. Hands-on experience with financial data.

Course Number | Section/Call Number | Times/Location | Instructor | Points | Enrollment |
---|---|---|---|---|---|

STAT 5261 | 001/69963 | M Tu W Th 6:15p - 7:50p Room TBA Building TBA | Pawel Polak | 3 | 9 |