Courses

This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.

Location

Hutchison Hall Room 141 (MW 2:00PM - 3:15PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 306 (W 7:40PM - 8:55PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 305 (R 12:30PM - 1:45PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 306 (R 2:00PM - 3:15PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 105 (R 7:40PM - 8:55PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Lechase Room 121 (F 12:30PM - 1:45PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 305 (F 3:25PM - 4:40PM)

T

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

I 200.

Location

Hylan Building Room 305 (F 4:50PM - 6:05PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 101 (R 6:15PM - 7:30PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 306 (F 2:00PM - 3:15PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Gavett Hall Room 301 (R 3:25PM - 4:40PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 102 (R 6:15PM - 7:30PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 105 (F 10:25AM - 11:40AM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Harkness Room 115 (W 6:15PM - 7:30PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 305 (F 2:00PM - 3:15PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 203 (R 7:40PM - 8:55PM)

This lab accompanies STAT 180 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 105 (W 4:50PM - 6:05PM)

Location

( 7:00PM - 7:00PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.

Location

Hutchison Hall Room 141 (TR 2:00PM - 3:15PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Morey Room 501 (T 6:15PM - 7:30PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Genesee Hall Room 309 (T 3:25PM - 4:40PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 203 (W 10:25AM - 11:40AM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Meliora Room 209 (W 4:50PM - 6:05PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Meliora Room 206 (W 6:15PM - 7:30PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Meliora Room 206 (W 4:50PM - 6:05PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Meliora Room 224 (W 2:00PM - 3:15PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Genesee Hall Room 308 (W 3:25PM - 4:40PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities."

Location

Meliora Room 218 (W 6:15PM - 7:30PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Lechase Room 104 (R 3:25PM - 4:40PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 203 (R 6:15PM - 7:30PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Frederick Douglass Room 403 (T 4:50PM - 6:05PM)

This lab accompanies STAT 190 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.

Location

Hylan Building Room 306 (R 7:40PM - 8:55PM)

Cross Listed: MATH 201 (P), STAT 201

Prerequisites: MATH 162 or equivalent. MATH 164 recommended.

Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.

Location

Morey Room 504 (MW 10:25AM - 11:40AM)

Cross Listed: MATH 201 (P), STAT 201

Prerequisites: MATH 162 or equivalent. MATH 164 recommended.

Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.

Location

Computer Studies Room 209 (MW 2:00PM - 3:15PM)

Cross Listed: MATH 201 (P), STAT 201

Prerequisites: MATH 162 or equivalent. MATH 164 recommended.

Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.

Location

Goergen Hall Room 108 (TR 2:00PM - 3:15PM)

Cross Listed: MATH 203 (P), STAT 203

Prerequisites: MATH 201

Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.

Location

Morey Room 321 (TR 3:25PM - 4:40PM)

Cross Listed: MATH 203 (P), STAT 203

Prerequisites: MATH 201

Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.

Location

Hylan Building Room 1106A (W 4:50PM - 6:05PM)

Cross Listed: MATH 203 (P), STAT 203

Prerequisites: MATH 201

Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.

Location

Morey Room 502 (M 3:25PM - 4:40PM)

Cross Listed: MATH 203 (P), STAT 203

Prerequisites: MATH 201

Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.

Location

Hylan Building Room 1101 (W 11:50AM - 1:05PM)

Cross Listed: MATH 203 (P), STAT 203

Prerequisites: MATH 201

Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.

Location

Hylan Room 301 (F 12:30PM - 1:45PM)

Prerequisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent

Co-located with STAT 416

Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.

Location

Gavett Hall Room 301 (TR 9:40AM - 10:55AM)

Prerequisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent

Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.

Location

Bausch & Lomb Room 106 (MW 2:00PM - 3:15PM)

Prerequisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent

Description:

THIS SECTION ONLY OPEN TO FIRST YEAR STUDENTS AND SOPHOMORES.

STT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable report

Location

Hylan Building Room 102 (TR 12:30PM - 1:45PM)

Co-located with STAT 418, STAT 218

Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent

This course offers an introduction to methods used for analyzing categorical data. The first portion of this course focuses on contingency table analyses. In particular, both two-way and three-way tables are introduced, along with inferential methods for determining significant associations between categorical responses. In the second portion of the course, emphasis is placed on regression models for categorical outcomes that are binary, polytomous, ordinal, or counts. Particular attention is given to logistic, probit, and log-linear models, along with associated inferential tests and model diagnostics. Examples and applications are taken from public health, epidemiology, and the behavioral and social sciences. R is introduced as the primary statistical software for performing data analyses.

Location

Hylan Building Room 202 (TR 11:05AM - 12:20PM)

Cross Listed: BST 421, STAT 221W (P)

Pre-requisites: STAT 180, STAT 190, STAT 212, or STAT 213, and STAT 203

Description: Simple random, stratified, systematic, and cluster sampling; estimation of the means, proportions, variance, and ratios of a finite population. Ratio and regression methods of estimation and the use of auxiliary information. The nonresponse problem. Prerequisite: familiarity with the concepts of expectation, variance, covariance and correlation.

Location

Hylan Building Room 101 (TR 12:30PM - 1:45PM)

Pre-req: STAT 212, STAT 180 and STAT 216 (or equivalent) or instructor permission

Co-listed with STAT 276W, STAT 476

This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.

Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.

Location

Meliora Room 210 (MW 10:25AM - 11:40AM)

Pre-req: STAT 212, STAT 180 and STAT 216 (or equivalent) or instructor permission

Co-listed with STAT 276, STAT 476

This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.

Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.

Location

Meliora Room 210 (MW 10:25AM - 11:40AM)

Cross Listed: STAT 277 (P), STAT 477

Prerequisites: STAT 216 or STAT 226W. CROSS LISTED with STAT 477.

Description: The first half of this course covers programming in R, SAS, and JMP. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numerically-oriented procedures such as linear models will be included where appropriate. Each student will analyze a real-life data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. Basic skills with the Windows operating system, a text editor (such as Notepad), and Microsoft Excel is assumed.

Location

Dewey Room 2110D (TR 9:40AM - 10:55AM)

Pre-requisites: STAT 203, STAT 216, and one computing course. STAT 217 recommended.

This course focuses on the communication skills that help students best discuss and present their professional selves within Statistics. Students will gain experience refining research papers into publication-ready formats, constructing meaningful abstracts, and articulating their career and graduate school goals. Through creating a portfolio of materials, including a resume, CV, cover letters, and personal statements, students will refine their ability to communicate their statistical work and goals in preparation for graduate school and the job market, utilizing Microsoft Office, LaTeX, and other field-specific software. 

Students interested in the course should submit a "Course Section Pre-Requisite Override" request. In this request, include your class year, list the statistics courses that have been taken, and whether you are planning for an internship, job search or graduate school the following year.

Location

Hylan Building Room 206 (W 3:25PM - 4:40PM)

Pre-requisites: STAT 201, STAT 203, STAT 223 recommended. CSC 171 or equivalent.

Statistics is embedded in pop culture through games of chance. In this course, we will explore the probability mechanisms and considerations that go into such games. We will explore relevant probability theory and work our way through calculating odds and outcomes in common games. Students will create virtual programs to simulate outcomes in such games based on our earlier probability work. Throughout the course, we will explore gaming industry standards and ethical considerations. 

Location

Lattimore Room 413 (TR 2:00PM - 3:15PM)

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( 7:00PM - 7:00PM)

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( 7:00PM - 7:00PM)

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( 7:00PM - 7:00PM)

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( 7:00PM - 7:00PM)

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( 7:00PM - 7:00PM)

Registration for Independent Study courses needs to be completed thru the https://secure1.rochester.edu/registrar/forms/independent-study-form.php instructions for online independent study registration

Location

( 7:00PM - 7:00PM)

STAT 392 - an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independent-studies.html

Location

( 7:00PM - 7:00PM)

Registration for Independent Study courses needs to be completed thru the https://secure1.rochester.edu/registrar/forms/independent-study-form.php instructions for online independent study registration

Location

( 7:00PM - 7:00PM)

STAT 395 - an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independent-studies.html

Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration.

Location

( 7:00PM - 7:00PM)

Co-located with STAT 216-1 STAT 416-1

Prerequisites: STAT 180, STAT 190, STAT 211, STAT 212, or STAT 213.

Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.

Location

Gavett Hall Room 301 (TR 9:40AM - 10:55AM)

Co-located with STAT 418, STAT 218

Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent

This course offers an introduction to methods used for analyzing categorical data. The first portion of this course focuses on contingency table analyses. In particular, both two-way and three-way tables are introduced, along with inferential methods for determining significant associations between categorical responses. In the second portion of the course, emphasis is placed on regression models for categorical outcomes that are binary, polytomous, ordinal, or counts. Particular attention is given to logistic, probit, and log-linear models, along with associated inferential tests and model diagnostics. Examples and applications are taken from public health, epidemiology, and the behavioral and social sciences. R is introduced as the primary statistical software for performing data analyses.

Location

Hylan Building Room 202 (TR 11:05AM - 12:20PM)

Co-listed with STAT 276w, STAT 276

Location

Meliora Room 210 (MW 10:25AM - 11:40AM)

Cross Listed: STAT 277 (P), STAT 477

Prerequisites: STAT 216 or STAT 226W. CROSS LISTED with STAT 477.

Description: The first half of this course covers programming in R, SAS, and JMP. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numerically-oriented procedures such as linear models will be included where appropriate. Each student will analyze a real-life data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. Basic skills with the Windows operating system, a text editor (such as Notepad), and Microsoft Excel is assumed.

Location

Dewey Room 2110D (TR 9:40AM - 10:55AM)