Courses
Fall Term Schedule
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Fall 2021
Number  Title  Instructor  Time 

STAT 2011
Arjun Krishnan
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
Ivan Chio
MW 2:00PM  3:15PM


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 12:30PM  1:45PM


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
Maria McDermott
W 7:40PM  8:55PM


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 21211
Maria McDermott
R 12:30PM  1:45PM


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 21212
Maria McDermott
R 2:00PM  3:15PM


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 21213
Maria McDermott
R 7:40PM  8:55PM


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 21214
Maria McDermott
F 12:30PM  1:45PM


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 21215
Maria McDermott
F 3:25PM  4:40PM


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 21216
Maria McDermott
F 4:50PM  6:05PM


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 21217
Maria McDermott
R 6:15PM  7:30PM


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 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 2123
Maria McDermott
R 3:25PM  4:40PM


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 2124
Maria McDermott
R 6:15PM  7:30PM


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 2125
Maria McDermott
F 10:25AM  11:40AM


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 2126
Maria McDermott
W 6:15PM  7:30PM


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 2127
Maria McDermott
F 2:00PM  3:15PM


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 2128
Maria McDermott
R 7:40PM  8:55PM


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 2129
Maria McDermott
W 4:50PM  6:05PM


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 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
T 6:15PM  7:30PM


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 21311
Maria McDermott
T 3:25PM  4:40PM


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 21312
Maria McDermott
W 10:25AM  11:40AM


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 21313
Maria McDermott
W 4:50PM  6:05PM


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 21314
Maria McDermott
W 6:15PM  7:30PM


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 21315
Maria McDermott
W 7:40PM  8:55PM


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 2132
Maria McDermott
M 2:00PM  3:15PM


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 2133
Maria McDermott
W 2:00PM  3:15PM


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 2134
Maria McDermott
W 3:25PM  4:40PM


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 2135
Maria McDermott
M 4:50PM  6:05PM


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 2136
Maria McDermott
M 6:15PM  7:30PM


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 2137
Maria McDermott
M 7:40PM  8:55PM


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 2138
Maria McDermott
T 12:30PM  1:45PM


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 2139
Maria McDermott
T 4:50PM  6:05PM


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 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 2162
Joseph Ciminelli
TR 12:30PM  1:45PM


Colocated with STT 2162, STT 4162 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 2181
Joseph Ciminelli
TR 11:05AM  12:20PM


Colocated with STT 418, STT 218 NOTE: waitlist requests can be sent to Professor Joseph Ciminelli at Joseph.ciminelli@rochester.edu 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 twoway and threeway 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 loglinear 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.


STAT 2191
Katherine Grzesik
MW 10:25AM  11:40AM


Colocated with STT 419, STT 219 Prerequisites: STT 216, and MTH 142. STT 203 is recommended Description: This course offers an applied introduction to nonparametric statistical methods and modeling techniques. The first half of the course covers nonparametric hypothesis testing and inference. The second half covers density estimation techniques, smoothing splines, local polynomial regression, and crossvalidation. The course culminates in an applied project involving nonparametric techniques to analyze realworld data. R/RStudio will be used for computation, so previous experience with such software is recommended.


STAT 221W1
Nicholas Zaino
TR 12:30PM  1:45PM


Cross Listed: BST 421, STT 221W (P) Prerequisites: STT 211, STT 212 or STT 213, and 203 or equivalent. 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.


STAT 2621
Yusuf Bilgic
TR 2:00PM  3:15PM


This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Nonparametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171


STAT 2771
Maria McDermott
TR 9:40AM  10:55AM


Cross Listed: STT 277 (P), STT 477 Prerequisites: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 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 numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife 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.


STAT 3901
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No description 

STAT 390A1
Katherine Grzesik
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No description 

STAT 390A2
Katherine Grzesik
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No description 

STAT 390A3
Maria McDermott
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No description 

STAT 3911
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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
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STAT 392  an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independentstudies.html 

STAT 3941
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STAT 394  an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independentstudies.html 

STAT 3951
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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. 

STAT 4162
Joseph Ciminelli
TR 12:30PM  1:45PM


Colocated with STT 2162, STT 4162 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 4181
Joseph Ciminelli
TR 11:05AM  12:20PM


Colocated with STT 418, STT 218 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 twoway and threeway 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 loglinear 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.


STAT 4191
Katherine Grzesik
MW 10:25AM  11:40AM


Colocated with STT 419, STT 219 Prerequisites: STT 216, and MTH 142. STT 203 is recommended Description: This course offers an applied introduction to nonparametric statistical methods and modeling techniques. The first half of the course covers nonparametric hypothesis testing and inference. The second half covers density estimation techniques, smoothing splines, local polynomial regression, and crossvalidation. The course culminates in an applied project involving nonparametric techniques to analyze realworld data. R/RStudio will be used for computation, so previous experience with such software is recommended.


STAT 4771
Maria McDermott
TR 9:40AM  10:55AM


Cross Listed: STT 277 (P), STT 477 Prerequisites: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 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 numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife 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.

Fall 2021
Number  Title  Instructor  Time 

Monday  
STAT 2132
Maria McDermott


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 2135
Maria McDermott


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 2136
Maria McDermott


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 2137
Maria McDermott


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. 

Monday and Wednesday  
STAT 2011
Arjun Krishnan


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 2191
Katherine Grzesik


Colocated with STT 419, STT 219 Prerequisites: STT 216, and MTH 142. STT 203 is recommended Description: This course offers an applied introduction to nonparametric statistical methods and modeling techniques. The first half of the course covers nonparametric hypothesis testing and inference. The second half covers density estimation techniques, smoothing splines, local polynomial regression, and crossvalidation. The course culminates in an applied project involving nonparametric techniques to analyze realworld data. R/RStudio will be used for computation, so previous experience with such software is recommended. 

STAT 4191
Katherine Grzesik


Colocated with STT 419, STT 219 Prerequisites: STT 216, and MTH 142. STT 203 is recommended Description: This course offers an applied introduction to nonparametric statistical methods and modeling techniques. The first half of the course covers nonparametric hypothesis testing and inference. The second half covers density estimation techniques, smoothing splines, local polynomial regression, and crossvalidation. The course culminates in an applied project involving nonparametric techniques to analyze realworld data. R/RStudio will be used for computation, so previous experience with such software is recommended. 

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. 

STAT 2012
Ivan Chio


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. 

Tuesday  
STAT 2138
Maria McDermott


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 21311
Maria McDermott


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 2139
Maria McDermott


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


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. 

Tuesday and Thursday  
STAT 2771
Maria McDermott


Cross Listed: STT 277 (P), STT 477 Prerequisites: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 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 numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife 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. 

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 4771
Maria McDermott


Cross Listed: STT 277 (P), STT 477 Prerequisites: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 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 numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife 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. 

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 2181
Joseph Ciminelli


Colocated with STT 418, STT 218 NOTE: waitlist requests can be sent to Professor Joseph Ciminelli at Joseph.ciminelli@rochester.edu 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 twoway and threeway 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 loglinear 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. 

STAT 4181
Joseph Ciminelli


Colocated with STT 418, STT 218 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 twoway and threeway 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 loglinear 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. 

STAT 2162
Joseph Ciminelli


Colocated with STT 2162, STT 4162 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 221W1
Nicholas Zaino


Cross Listed: BST 421, STT 221W (P) Prerequisites: STT 211, STT 212 or STT 213, and 203 or equivalent. 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. 

STAT 4162
Joseph Ciminelli


Colocated with STT 2162, STT 4162 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 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 2621
Yusuf Bilgic


This course will cover foundational concepts in descriptive analyses, probability, and statistical inference. Topics to be covered include data exploration through descriptive statistics (with a heavy emphasis on using R for such analyses), elementary probability, diagnostic testing, combinatorics, random variables, elementary distribution theory, statistical inference, and statistical modeling. The inference portion of the course will focus on building and applying hypothesis tests and confidence intervals for population means, proportions, variances, and correlations. Nonparametric alternatives will also be introduced. The modeling portion of the course will include ANOVA, and simple and multiple regression and their respective computational methods. Students will be introduced to the R statistical computing environment. PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 

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 21312
Maria McDermott


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 2133
Maria McDermott


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 2134
Maria McDermott


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 2129
Maria McDermott


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 21313
Maria McDermott


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 2126
Maria McDermott


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 21314
Maria McDermott


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 21210
Maria McDermott


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 21315
Maria McDermott


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. 

Thursday  
STAT 21211
Maria McDermott


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 21212
Maria McDermott


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 2123
Maria McDermott


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 21217
Maria McDermott


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 2124
Maria McDermott


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 21213
Maria McDermott


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 2128
Maria McDermott


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 

Friday  
STAT 2125
Maria McDermott


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 21214
Maria McDermott


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 2127
Maria McDermott


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 21215
Maria McDermott


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 21216
Maria McDermott


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 