Spring Term Schedule
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Spring 2024
Number  Title  Instructor  Time 

STAT 2011
Yuanyuan Pan
MW 9:00AM  10:15AM


Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, 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. Same as MATH 201.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time.


STAT 2012
Peter Oberly
MW 12:30PM  1:45PM


Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, 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. Same as MATH 201.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time.


STAT 2013
Mary Cook
MW 2:00PM  3:15PM


Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, 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. Same as MATH 201.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time.


STAT 2031
Javier Bautista
TR 3:25PM  4:40PM


Cross Listed: MATH 203 (P), STT 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.


STAT 21210
Bruce Blaine
W 7:40PM  8:55PM


This lab accompanies STAT 212 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.


STAT 21211
Bruce Blaine
R 12:30PM  1:45PM


This lab accompanies STAT 212 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.


STAT 21212
Bruce Blaine
R 2:00PM  3:15PM


This lab accompanies STAT 212 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.


STAT 21213
Bruce Blaine
W 4:50PM  6:05PM


This lab accompanies STAT 212 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.


STAT 21214
Bruce Blaine
F 12:30PM  1:45PM


This lab accompanies STAT 212 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.


STAT 21215
Bruce Blaine
R 2:00PM  3:15PM


This lab accompanies STAT 212 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.


STAT 21216
Bruce Blaine
R 3:25PM  4:40PM


This lab accompanies STAT 212 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.


STAT 21217
Bruce Blaine
R 6:15PM  7:30PM


This lab accompanies STAT 212 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.


STAT 21218
Bruce Blaine
F 2:00PM  3:15PM


This lab accompanies STAT 212 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.


STAT 21219
Bruce Blaine
F 9:00AM  10:15AM


This lab accompanies STAT 212 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.


STAT 2122
Bruce Blaine
TR 11:05AM  12:20PM


Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a noncalculus based introduction to statistical methodology and analyses that focuses on providing students with 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. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. . This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upperlevel methodology courses


STAT 21220
Bruce Blaine
F 10:25AM  11:40AM


This lab accompanies STAT 212 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.


STAT 21221
Bruce Blaine
F 11:50AM  1:05PM


This lab accompanies STAT 212 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.


STAT 21222
Bruce Blaine
F 4:50PM  6:05PM


This lab accompanies STAT 212 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.


STAT 2123
Bruce Blaine
TR 2:00PM  3:15PM


Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a noncalculus based introduction to statistical methodology and analyses that focuses on providing students with 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. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upperlevel methodology courses


STAT 2124
Bruce Blaine
R 6:15PM  7:30PM


This lab accompanies STAT 212 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.


STAT 2125
Bruce Blaine
F 10:25AM  11:40AM


This lab accompanies STAT 212 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.


STAT 2126
Bruce Blaine
W 6:15PM  7:30PM


This lab accompanies STAT 212 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.


STAT 2127
Bruce Blaine
F 2:00PM  3:15PM


This lab accompanies STAT 212 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.


STAT 2128
Bruce Blaine
R 4:50PM  6:05PM


This lab accompanies STAT 212 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.


STAT 2129
Bruce Blaine
W 4:50PM  6:05PM


This lab accompanies STAT 212 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.


STAT 2131
Aruni Jayathilaka
TR 2:00PM  3:15PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses.


STAT 21310
Aruni Jayathilaka
M 4:50PM  6:05PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 21311
Aruni Jayathilaka
M 6:15PM  7:30PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 21312
Aruni Jayathilaka
M 7:40PM  8:55PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 21313
Aruni Jayathilaka
M 3:25PM  4:40PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 21314
Aruni Jayathilaka
T 3:25PM  4:40PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 21315
Aruni Jayathilaka
W 3:25PM  4:40PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 21316
Aruni Jayathilaka
TR 3:25PM  4:40PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses.


STAT 21317
Aruni Jayathilaka
M 9:00AM  10:15AM


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses.


STAT 21318
Aruni Jayathilaka
W 6:15PM  7:30PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses.


STAT 21319
Aruni Jayathilaka
T 6:15PM  7:30PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses.


STAT 2132
Aruni Jayathilaka
M 12:30PM  1:45PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 2133
Aruni Jayathilaka
W 12:30PM  1:45PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 2134
Aruni Jayathilaka
T 4:50PM  6:05PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 2135
Aruni Jayathilaka
T 11:05AM  12:20PM


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.


STAT 2151
Javier Bautista
TR 12:30PM  1:45PM


Prerequisites: STAT 212 or Equivalent CoLocated: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages.


STAT 2161
Nicholas Zaino
TR 9:40AM  10:55AM


Prerequisites: 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 2way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is noncalculus 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.


STAT 2162
Aruni Jayathilaka
TR 11:05AM  12:20PM


Prerequisites: STAT 211, STAT 212, or STAT 213. Description: 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 2way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is noncalculus 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.


STAT 2171
Nicholas Zaino
TR 12:30PM  1:45PM


Colocated: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 217 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/nonlinear models, collinear data, robust regression, time series techniques, and other related modeling topics. In the second half of the course, advanced analysis of variance (ANOVA) techniques will be explored, focusing mainly on repeated measures, mixed models, multivariate ANOVA, and nonparametric alternatives. Additional topics include structural equation models, missing data, and metaanalysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory.


STAT 2231
Joseph Ciminelli
TR 11:05AM  12:20PM


Colocated: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework.


STAT 226W1
Joseph Ciminelli
TR 2:00PM  3:15PM


Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R.


STAT 226W2
Katherine Grzesik
MW 3:25PM  4:40PM


Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R.


STAT 2751
Katherine Grzesik
MW 12:30PM  1:45PM


Prereq: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.


STAT 275W1
Katherine Grzesik
MW 12:30PM  1:45PM


Prereq: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.


STAT 2761
Bruce Blaine
MW 10:25AM  11:40AM


Prereq: STAT 212 and STAT 216 (or equivalent) or instructor permission 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.


STAT 276W1
Bruce Blaine
MW 10:25AM  11:40AM


Prereq: STAT 212 and STAT 216 (or equivalent) or instructor permission 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.


STAT 390A5
Javier Bautista
–


No description 

STAT 390A6
Joseph Ciminelli
–


No description 

STAT 3921
–
–


Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration. STAT 392  an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independentstudies.html 

STAT 4152
Javier Bautista
TR 12:30PM  1:45PM


Prerequisites: STAT 212 or Equivalent CoLocated: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages.


STAT 4162
Nicholas Zaino
TR 9:40AM  10:55AM


Colocated with STAT 216, STAT 416 Prerequisites: 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 2way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is noncalculus 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.


STAT 4231
Joseph Ciminelli
TR 11:05AM  12:20PM


Colocated: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework.


STAT 4761
Bruce Blaine
MW 10:25AM  11:40AM


Colocated: STAT 476, STAT 276W1, STAT 2761 Prerequisites: STAT 211, 212, 213, or equivalent. Description: This course offers an introduction to statistical computing in the R environment. To start, focus is placed on assigning objects, creating data structures, applying Boolean logic, importing and subsetting data, data manipulation (both long and short formats), and implementing elementary commands and builtin functions from R packages. In the second portion of the course, students learn more advanced topics of writing loops, developing functions, building graphics, debugging code, and text mining. Topics will be illustrated using key statistical tools, including basic data summarization and exploration, linear models, and simulations. The course will rely upon the use of R Markdown as an essential tool for effectively integrating R code and output into presentable reports. Basic skills with a text editor (such as Notepad) and Microsoft Excel are assumed, as is basic knowledge of statistical inference.

Spring 2024
Number  Title  Instructor  Time 

Monday  
STAT 21317
Aruni Jayathilaka


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses. 

STAT 2132
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21313
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21310
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21311
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21312
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

Monday and Wednesday  
STAT 2011
Yuanyuan Pan


Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, 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. Same as MATH 201.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. 

STAT 2761
Bruce Blaine


Prereq: STAT 212 and STAT 216 (or equivalent) or instructor permission 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. 

STAT 276W1
Bruce Blaine


Prereq: STAT 212 and STAT 216 (or equivalent) or instructor permission 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. 

STAT 4761
Bruce Blaine


Colocated: STAT 476, STAT 276W1, STAT 2761 Prerequisites: STAT 211, 212, 213, or equivalent. Description: This course offers an introduction to statistical computing in the R environment. To start, focus is placed on assigning objects, creating data structures, applying Boolean logic, importing and subsetting data, data manipulation (both long and short formats), and implementing elementary commands and builtin functions from R packages. In the second portion of the course, students learn more advanced topics of writing loops, developing functions, building graphics, debugging code, and text mining. Topics will be illustrated using key statistical tools, including basic data summarization and exploration, linear models, and simulations. The course will rely upon the use of R Markdown as an essential tool for effectively integrating R code and output into presentable reports. Basic skills with a text editor (such as Notepad) and Microsoft Excel are assumed, as is basic knowledge of statistical inference. 

STAT 2012
Peter Oberly


Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, 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. Same as MATH 201.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. 

STAT 2751
Katherine Grzesik


Prereq: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class. 

STAT 275W1
Katherine Grzesik


Prereq: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class. 

STAT 2013
Mary Cook


Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, 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. Same as MATH 201.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. 

STAT 226W2
Katherine Grzesik


Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R. 

Tuesday  
STAT 2135
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21314
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 2134
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21319
Aruni Jayathilaka


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses. 

Tuesday and Thursday  
STAT 2161
Nicholas Zaino


Prerequisites: 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 2way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is noncalculus 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. 

STAT 4162
Nicholas Zaino


Colocated with STAT 216, STAT 416 Prerequisites: 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 2way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is noncalculus 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. 

STAT 2122
Bruce Blaine


Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a noncalculus based introduction to statistical methodology and analyses that focuses on providing students with 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. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. . This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upperlevel methodology courses 

STAT 2162
Aruni Jayathilaka


Prerequisites: STAT 211, STAT 212, or STAT 213. Description: 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 2way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is noncalculus 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. 

STAT 2231
Joseph Ciminelli


Colocated: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework. 

STAT 4231
Joseph Ciminelli


Colocated: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework. 

STAT 2151
Javier Bautista


Prerequisites: STAT 212 or Equivalent CoLocated: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages. 

STAT 2171
Nicholas Zaino


Colocated: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 217 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/nonlinear models, collinear data, robust regression, time series techniques, and other related modeling topics. In the second half of the course, advanced analysis of variance (ANOVA) techniques will be explored, focusing mainly on repeated measures, mixed models, multivariate ANOVA, and nonparametric alternatives. Additional topics include structural equation models, missing data, and metaanalysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory.


STAT 4152
Javier Bautista


Prerequisites: STAT 212 or Equivalent CoLocated: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages. 

STAT 2123
Bruce Blaine


Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a noncalculus based introduction to statistical methodology and analyses that focuses on providing students with 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. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upperlevel methodology courses 

STAT 2131
Aruni Jayathilaka


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses. 

STAT 226W1
Joseph Ciminelli


Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R. 

STAT 2031
Javier Bautista


Cross Listed: MATH 203 (P), STT 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. 

STAT 21316
Aruni Jayathilaka


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses. 

Wednesday  
STAT 2133
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21315
Aruni Jayathilaka


Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. 

STAT 21213
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 2129
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 2126
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21318
Aruni Jayathilaka


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upperlevel methodology courses. 

STAT 21210
Bruce Blaine


This lab accompanies STAT 212 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. 

Thursday  
STAT 21211
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21212
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21215
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21216
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 2128
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21217
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 2124
Bruce Blaine


This lab accompanies STAT 212 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. 

Friday  
STAT 21219
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21220
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 2125
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21221
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21214
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21218
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 2127
Bruce Blaine


This lab accompanies STAT 212 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. 

STAT 21222
Bruce Blaine


This lab accompanies STAT 212 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. 