Courses
Spring Term Schedule
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Spring 2021
Number  Title  Instructor  Time 

STAT 2011
Thomas Tucker
MW 10:25AM  11:40AM


Cross Listed: MTH 201 (P), STT 201 Prerequisites: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. Description: 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. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time.


STAT 2012
Ian Alevy
TR 12:30PM  1:45PM


Cross Listed: MTH 201 (P), STT 201 Prerequisites: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. Description: 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. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time.


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


Cross Listed: MTH 203 (P), STT 203 Prerequisites: MTH 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 2121
Katherine Grzesik
MW 2:00PM  3:15PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP 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: STT211, STT212, STT213, and BIO/STT214. 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 21210
Katherine Grzesik
F 2:00AM  3:15AM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21211
Katherine Grzesik
W 4:50PM  6:05PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21212
Katherine Grzesik
W 6:15PM  7:30PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21213
Katherine Grzesik
F 3:25PM  4:40PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21214
Katherine Grzesik
R 12:30PM  1:45PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21215
Katherine Grzesik
R 2:00PM  3:15PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21216
Katherine Grzesik
F 10:25AM  11:40AM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21217
Katherine Grzesik
W 7:40PM  8:55PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21218
Katherine Grzesik
W 4:50PM  6:05PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21219
Katherine Grzesik
F 9:00AM  10:15AM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2122
Maria McDermott
TR 11:05AM  12:20PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP 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: STT211, STT212, STT213, and BIO/STT214. 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
Katherine Grzesik
F 10:25AM  11:40AM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21221
Katherine Grzesik
F 11:50AM  1:05PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 21223
Katherine Grzesik
R 2:00PM  3:15PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2123
Katherine Grzesik
F 2:00PM  3:15PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2124
Katherine Grzesik
R 7:40PM  8:55PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2125
Katherine Grzesik
R 3:25PM  4:40PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2126
Katherine Grzesik
F 12:30PM  1:45PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2127
Katherine Grzesik
R 4:50PM  6:05PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2128
Katherine Grzesik
R 6:15PM  7:30PM


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R.


STAT 2131
Maria McDermott
TR 2:00PM  3:15PM


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MTH 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: STT 211, STT 212, STT 213, or BIO/STT 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
Maria McDermott
M 4:50PM  6:05PM


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


STAT 21311
Maria McDermott
M 6:15PM  7:30PM


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


STAT 21312
Maria McDermott
M 7:40PM  8:55PM


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


STAT 21313
Maria McDermott
M 3:25PM  4:40PM


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


STAT 21314
Maria McDermott
T 3:25PM  4:40PM


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


STAT 21315
Maria McDermott
W 3:25PM  4:40PM


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


STAT 2132
Maria McDermott
M 12:30PM  1:45PM


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


STAT 2133
Maria McDermott
W 12:30PM  1:45PM


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


STAT 2134
Maria McDermott
T 4:50PM  6:05PM


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


STAT 2135
Maria McDermott
T 11:05AM  12:20PM


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


STAT 2141
Danni Presgraves
TR 12:30PM  1:45PM


No description


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


Prerequisites: STT 211, STT 212, or STT 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


Prerequisites: STT 216. Description: STT 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


Prerequisites: STT 203 and MTH 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: STT 211, STT 212 or 213, and STT 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 276W1
Katherine Grzesik
MW 10:25AM  11:40AM


Prerequisites: STT 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 390A1
Katherine Grzesik
–


No description 

STAT 390A2
Maria McDermott
–


No description 

STAT 390A3
Joseph Ciminelli
–


No description 

STAT 390A4
Nicholas Zaino
–


No description 

STAT 390A5
Javier Bautista
–


No description 

STAT 3911
–
–


STAT 391  an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independentstudies.html Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration. 

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 3941
–
–


STAT 394  an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independentstudies.html. Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration. 

STAT 3951
–
–


STAT 395  an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independentstudies.html Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration. 
Spring 2021
Number  Title  Instructor  Time 

Monday  
STAT 2132
Maria McDermott


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

STAT 21313
Maria McDermott


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

STAT 21310
Maria McDermott


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

STAT 21311
Maria McDermott


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

STAT 21312
Maria McDermott


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

Monday and Wednesday  
STAT 2011
Thomas Tucker


Cross Listed: MTH 201 (P), STT 201 Prerequisites: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. Description: 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. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. 

STAT 276W1
Katherine Grzesik


Prerequisites: STT 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 2121
Katherine Grzesik


Class Info: YOU MUST REGISTER FOR A WORKSHOP 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: STT211, STT212, STT213, and BIO/STT214. 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. 

Tuesday  
STAT 2135
Maria McDermott


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

STAT 21314
Maria McDermott


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

STAT 2134
Maria McDermott


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

Tuesday and Thursday  
STAT 2161
Nicholas Zaino


Prerequisites: STT 211, STT 212, or STT 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 2122
Maria McDermott


Class Info: YOU MUST REGISTER FOR A WORKSHOP 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: STT211, STT212, STT213, and BIO/STT214. 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 2231
Joseph Ciminelli


Prerequisites: STT 203 and MTH 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 2012
Ian Alevy


Cross Listed: MTH 201 (P), STT 201 Prerequisites: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. Description: 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. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently.This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. 

STAT 2141
Danni Presgraves


No description 

STAT 2171
Nicholas Zaino


Prerequisites: STT 216. Description: STT 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 2131
Maria McDermott


Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MTH 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: STT 211, STT 212, STT 213, or BIO/STT 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: STT 211, STT 212 or 213, and STT 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: MTH 203 (P), STT 203 Prerequisites: MTH 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. 

Wednesday  
STAT 2133
Maria McDermott


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

STAT 21315
Maria McDermott


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

STAT 21211
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21218
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21212
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21217
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

Thursday  
STAT 21214
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21215
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21223
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 2125
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 2127
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 2128
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 2124
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

Friday  
STAT 21210
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21219
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21216
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21220
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21221
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 2126
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 2123
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 

STAT 21213
Katherine Grzesik


Workshop that accompanies STAT 212 to further explore concepts of statistical methodology and computing in R. 