Spring Term Schedule
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Spring 2026
| Number | Title | Instructor | Time |
|---|
|
STAT 180-01
Bekki Gibson
MW 2:00PM - 3:15PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-04
Bekki Gibson
F 10:25AM - 11:40AM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-05
Bekki Gibson
W 6:15PM - 7:30PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-06
Bekki Gibson
F 2:00PM - 3:15PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-07
Bekki Gibson
R 4:50PM - 6:05PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-08
Bekki Gibson
W 4:50PM - 6:05PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-10
Bekki Gibson
R 12:30PM - 1:45PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-11
Bekki Gibson
R 2:00PM - 3:15PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-12
Bekki Gibson
W 4:50PM - 6:50PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-13
Bekki Gibson
F 12:30PM - 1:45PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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|
STAT 180-14
Bekki Gibson
R 2:00PM - 3:15PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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|
STAT 180-15
Bekki Gibson
R 3:25PM - 4:40PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-16
Bekki Gibson
R 6:15PM - 7:30PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-18
Bekki Gibson
F 9:00AM - 10:15AM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
|
|
STAT 180-19
Bekki Gibson
F 10:25AM - 11:40AM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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|
STAT 180-20
Bekki Gibson
F 11:50AM - 1:05PM
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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|
STAT 181-01
Aruni Jayathilaka
7:00PM - 7:00PM
|
|
This is a self-paced module for students who already have STAT 180 credit but have since determined a need for STAT 190 for their particular degree program. After independently working through the material of STAT 190, you will complete an equivalency exam at the end of the semester to assess statistical competency at the STAT 190 level. Graded on a pass/fail basis.
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STAT 190-01
Aruni Jayathilaka
TR 2:00PM - 3:15PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-03
Aruni Jayathilaka
T 12:30PM - 1:45PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-04
Aruni Jayathilaka
T 4:50PM - 6:05PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-05
Aruni Jayathilaka
T 11:05AM - 12:20PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-06
Aruni Jayathilaka
M 4:50PM - 6:05PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-07
Aruni Jayathilaka
M 6:15PM - 7:30PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-08
Aruni Jayathilaka
M 7:40PM - 8:55PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-09
Aruni Jayathilaka
M 3:25PM - 4:40PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-10
Aruni Jayathilaka
T 3:25PM - 4:40PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-11
Aruni Jayathilaka
W 3:25PM - 4:40PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-12
Aruni Jayathilaka
M 9:00AM - 10:15AM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-13
Aruni Jayathilaka
W 6:15PM - 7:30PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-14
Aruni Jayathilaka
T 6:15PM - 7:30PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-15
Aruni Jayathilaka
M 2:00PM - 3:15PM
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Prerequisites: MATH 141 or equivalent.
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STAT 201-01
Thomas Tucker
TR 9:40AM - 10:55AM
|
|
Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
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STAT 201-02
Neeraja Kulkarni
MW 12:30PM - 1:45PM
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
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STAT 201-03
Neeraja Kulkarni
MW 2:00PM - 3:15PM
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
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STAT 203-01
Aruni Jayathilaka
TR 11:05AM - 12:20PM
|
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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.
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STAT 203-02
Aruni Jayathilaka
M 4:50PM - 6:05PM
|
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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.
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STAT 203-03
Aruni Jayathilaka
W 10:25AM - 11:40AM
|
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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.
|
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STAT 203-04
Aruni Jayathilaka
F 2:00PM - 3:15PM
|
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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.
|
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STAT 203-05
Aruni Jayathilaka
W 4:50PM - 6:05PM
|
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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.
|
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STAT 203-06
Aruni Jayathilaka
M 9:00AM - 10:15AM
|
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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.
|
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STAT 216-01
Nicholas Zaino
TR 9:40AM - 10:55AM
|
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Pre-requisites: STAT 180, STAT 190, or equivalent Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.
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STAT 216-02
Bruce Blaine
MW 3:25PM - 4:40PM
|
|
Pre-requisites: STAT 180, STAT 190, or equivalent Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.
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STAT 217-01
Nicholas Zaino
TR 12:30PM - 1:45PM
|
|
Prerequisites: STAT 216. STAT 217 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/non-linear 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 meta-analysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory.
|
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STAT 223-01
Joseph Ciminelli
TR 11:05AM - 12:20PM
|
|
Prerequisites: STAT 203 and MATH 164, or instructor permission. 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.
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STAT 226W-01
Katherine Grzesik
TR 2:00PM - 3:15PM
|
|
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.
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STAT 275-01
Bekki Gibson
MW 11:50AM - 1:05PM
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|
This course will teach students about core statistical programming components in the R environment with the goal of producing high level functions, packages and simulation studies. This course will cover base R objects, structures and graphics; importing, exporting and organizing data structures; control statements; loops and iteration processes; function development; basic R Markdown (HTML, PDF, Word); simulation study programming; presentation of simulation results and R package creation. Successful students will have strong familiarity with R and RStudio, be able to manage various data structures using R, program Statistical tasks using R, code user-defined functions, read and understand functions defined by other users and compile reports in R Markdown. Those enrolled in the writing section of the course (STAT 275W) will additionally learn how to write proper and clear documentation for functions and packages in the R repository.
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STAT 275W-01
Bekki Gibson
MW 11:50AM - 1:05PM
|
|
This course will teach students about core statistical programming components in the R environment with the goal of producing high level functions, packages and simulation studies. This course will cover base R objects, structures and graphics; importing, exporting and organizing data structures; control statements; loops and iteration processes; function development; basic R Markdown (HTML, PDF, Word); simulation study programming; presentation of simulation results and R package creation. Successful students will have strong familiarity with R and RStudio, be able to manage various data structures using R, program Statistical tasks using R, code user-defined functions, read and understand functions defined by other users and compile reports in R Markdown. Those enrolled in the writing section of the course (STAT 275W) will additionally learn how to write proper and clear documentation for functions and packages in the R repository.
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STAT 276-01
Bruce Blaine
MW 10:25AM - 11:40AM
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|
This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.
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STAT 276W-01
Bruce Blaine
MW 10:25AM - 11:40AM
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|
This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.
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STAT 392-01
–
7:00PM - 7:00PM
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This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php)
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STAT 395-02
Nicholas Zaino
7:00PM - 7:00PM
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This course provides undergraduate students the opportunity to pursue in-depth, independent exploration of a topic not regularly offered in the curriculum, under the supervision of a faculty member in the form of independent study, practicum, internship or research. The objectives and content are determined in consultation between students and full-time members of the teaching faculty. Responsibilities and expectations vary by course and department. Registration for Independent Study courses needs to be completed through the Independent Study Registration form (https://secure1.rochester.edu/registrar/forms/independent-study-form.php)
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STAT 415-02
–
TR 12:30PM - 1:45PM
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Pre-requisites: STAT 212 or Equivalent Co-Located: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages.
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STAT 416-01
Nicholas Zaino
TR 9:40AM - 10:55AM
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STAT 416 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.
|
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STAT 417-01
Nicholas Zaino
TR 12:30PM - 1:45PM
|
|
Co-located: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 417 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/non-linear 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 meta-analysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory.
|
|
STAT 423-01
Joseph Ciminelli
TR 11:05AM - 12:20PM
|
|
Prerequisites: STAT 203 and MATH 164, or instructor permission. 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 476-01
Bruce Blaine
MW 10:25AM - 11:40AM
|
|
This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.
|
Spring 2026
| Number | Title | Instructor | Time |
|---|---|
| Monday | |
|
STAT 190-12
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 203-06
Aruni Jayathilaka
|
|
|
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 190-15
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
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STAT 190-09
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-06
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
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STAT 203-02
Aruni Jayathilaka
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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. |
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STAT 190-07
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 190-08
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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| Monday and Wednesday | |
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STAT 276-01
Bruce Blaine
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This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. |
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STAT 276W-01
Bruce Blaine
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This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. |
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STAT 476-01
Bruce Blaine
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This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. |
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STAT 275-01
Bekki Gibson
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This course will teach students about core statistical programming components in the R environment with the goal of producing high level functions, packages and simulation studies. This course will cover base R objects, structures and graphics; importing, exporting and organizing data structures; control statements; loops and iteration processes; function development; basic R Markdown (HTML, PDF, Word); simulation study programming; presentation of simulation results and R package creation. Successful students will have strong familiarity with R and RStudio, be able to manage various data structures using R, program Statistical tasks using R, code user-defined functions, read and understand functions defined by other users and compile reports in R Markdown. Those enrolled in the writing section of the course (STAT 275W) will additionally learn how to write proper and clear documentation for functions and packages in the R repository. |
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STAT 275W-01
Bekki Gibson
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This course will teach students about core statistical programming components in the R environment with the goal of producing high level functions, packages and simulation studies. This course will cover base R objects, structures and graphics; importing, exporting and organizing data structures; control statements; loops and iteration processes; function development; basic R Markdown (HTML, PDF, Word); simulation study programming; presentation of simulation results and R package creation. Successful students will have strong familiarity with R and RStudio, be able to manage various data structures using R, program Statistical tasks using R, code user-defined functions, read and understand functions defined by other users and compile reports in R Markdown. Those enrolled in the writing section of the course (STAT 275W) will additionally learn how to write proper and clear documentation for functions and packages in the R repository. |
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STAT 201-02
Neeraja Kulkarni
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time. |
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STAT 180-01
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 201-03
Neeraja Kulkarni
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time. |
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STAT 216-02
Bruce Blaine
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Pre-requisites: STAT 180, STAT 190, or equivalent Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports. |
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| Tuesday | |
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STAT 190-05
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 190-03
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 190-10
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 190-04
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 190-14
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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| Tuesday and Thursday | |
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STAT 201-01
Thomas Tucker
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time. |
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STAT 216-01
Nicholas Zaino
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Pre-requisites: STAT 180, STAT 190, or equivalent Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports. |
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STAT 416-01
Nicholas Zaino
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STAT 416 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports. |
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STAT 203-01
Aruni Jayathilaka
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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. |
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STAT 223-01
Joseph Ciminelli
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Prerequisites: STAT 203 and MATH 164, or instructor permission. 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. |
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STAT 423-01
Joseph Ciminelli
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Prerequisites: STAT 203 and MATH 164, or instructor permission. 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. |
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STAT 217-01
Nicholas Zaino
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Prerequisites: STAT 216. STAT 217 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/non-linear 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 meta-analysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory.
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STAT 415-02
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Pre-requisites: STAT 212 or Equivalent Co-Located: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages. |
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STAT 417-01
Nicholas Zaino
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Co-located: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 417 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/non-linear 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 meta-analysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory. |
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STAT 190-01
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 226W-01
Katherine Grzesik
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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. |
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| Wednesday | |
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STAT 203-03
Aruni Jayathilaka
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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. |
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STAT 190-11
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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STAT 180-08
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-12
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 203-05
Aruni Jayathilaka
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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. |
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STAT 180-05
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 190-13
Aruni Jayathilaka
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Prerequisites: MATH 141 or equivalent. |
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| Thursday | |
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STAT 180-10
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-11
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-14
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-15
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-07
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-16
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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| Friday | |
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STAT 180-18
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-04
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-19
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-20
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-13
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-06
Bekki Gibson
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 203-04
Aruni Jayathilaka
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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. |
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