Spring Term Schedule
The default view for the table below is "Sortable". This will allow you to sort any column in ascending order by clicking on its column heading.
Spring 2024
| Number | Title | Instructor | Time |
|---|
|
STAT 201-1
Yuanyuan Pan
MW 9:00AM - 10:15AM
|
|
Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. Same as MATH 201.This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
|
|
STAT 201-2
Peter Oberly
MW 12:30PM - 1:45PM
|
|
Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. Same as MATH 201.This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
|
|
STAT 201-3
Mary Cook
MW 2:00PM - 3:15PM
|
|
Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. Same as MATH 201.This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
|
|
STAT 203-1
Javier Bautista
TR 3:25PM - 4:40PM
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.
|
|
STAT 203-3
Javier Bautista
T 12:30PM - 1:45PM
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.
|
|
STAT 203-4
Javier Bautista
W 11:50AM - 1:05PM
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.
|
|
STAT 203-6
Javier Bautista
W 4:50PM - 6:05PM
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics.
|
|
STAT 212-10
Bruce Blaine
W 7:40PM - 8:55PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-11
Bruce Blaine
R 12:30PM - 1:45PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-12
Bruce Blaine
R 2:00PM - 3:15PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-13
Bruce Blaine
W 4:50PM - 6:05PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-14
Bruce Blaine
F 12:30PM - 1:45PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-15
Bruce Blaine
R 2:00PM - 3:15PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-16
Bruce Blaine
R 3:25PM - 4:40PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-17
Bruce Blaine
R 6:15PM - 7:30PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-18
Bruce Blaine
F 2:00PM - 3:15PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-19
Bruce Blaine
F 9:00AM - 10:15AM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-2
Bruce Blaine
TR 11:05AM - 12:20PM
|
|
Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a non-calculus based introduction to statistical methodology and analyses that focuses on providing students with the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. . This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses
|
|
STAT 212-20
Bruce Blaine
F 10:25AM - 11:40AM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-21
Bruce Blaine
F 11:50AM - 1:05PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-22
Bruce Blaine
F 4:50PM - 6:05PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-3
Bruce Blaine
TR 2:00PM - 3:15PM
|
|
Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a non-calculus based introduction to statistical methodology and analyses that focuses on providing students with the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses
|
|
STAT 212-4
Bruce Blaine
R 6:15PM - 7:30PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-5
Bruce Blaine
F 10:25AM - 11:40AM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-6
Bruce Blaine
W 6:15PM - 7:30PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-7
Bruce Blaine
F 2:00PM - 3:15PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-8
Bruce Blaine
R 4:50PM - 6:05PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 212-9
Bruce Blaine
W 4:50PM - 6:05PM
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities.
|
|
STAT 213-1
Aruni Jayathilaka
TR 2:00PM - 3:15PM
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses.
|
|
STAT 213-10
Aruni Jayathilaka
M 4:50PM - 6:05PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-11
Aruni Jayathilaka
M 6:15PM - 7:30PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-12
Aruni Jayathilaka
M 7:40PM - 8:55PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-13
Aruni Jayathilaka
M 3:25PM - 4:40PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-14
Aruni Jayathilaka
T 3:25PM - 4:40PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-15
Aruni Jayathilaka
W 3:25PM - 4:40PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-16
Aruni Jayathilaka
TR 3:25PM - 4:40PM
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses.
|
|
STAT 213-17
Aruni Jayathilaka
M 9:00AM - 10:15AM
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses.
|
|
STAT 213-18
Aruni Jayathilaka
W 6:15PM - 7:30PM
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses.
|
|
STAT 213-19
Aruni Jayathilaka
T 6:15PM - 7:30PM
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses.
|
|
STAT 213-2
Aruni Jayathilaka
M 12:30PM - 1:45PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-3
Aruni Jayathilaka
W 12:30PM - 1:45PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-4
Aruni Jayathilaka
T 4:50PM - 6:05PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 213-5
Aruni Jayathilaka
T 11:05AM - 12:20PM
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R.
|
|
STAT 215-1
Javier Bautista
TR 12:30PM - 1:45PM
|
|
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.
|
|
STAT 216-1
Nicholas Zaino
TR 9:40AM - 10:55AM
|
|
Prerequisites: STAT 211, STAT 212, or STAT 213. Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 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.
|
|
STAT 216-2
Aruni Jayathilaka
TR 11:05AM - 12:20PM
|
|
Prerequisites: STAT 211, STAT 212, or STAT 213. Description: STT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 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.
|
|
STAT 217-1
Nicholas Zaino
TR 12:30PM - 1:45PM
|
|
Co-located: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 217 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/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 223-1
Joseph Ciminelli
TR 11:05AM - 12:20PM
|
|
Co-located: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework.
|
|
STAT 226W-1
Joseph Ciminelli
TR 2:00PM - 3:15PM
|
|
Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R.
|
|
STAT 226W-2
Katherine Grzesik
MW 3:25PM - 4:40PM
|
|
Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R.
|
|
STAT 275-1
Katherine Grzesik
MW 12:30PM - 1:45PM
|
|
Pre-req: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.
|
|
STAT 275W-1
Katherine Grzesik
MW 12:30PM - 1:45PM
|
|
Pre-req: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.
|
|
STAT 276-1
Bruce Blaine
MW 10:25AM - 11:40AM
|
|
Pre-req: STAT 212 and STAT 216 (or equivalent) or instructor permission Co-listed with STAT 276W, STAT 476 This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.
|
|
STAT 276W-1
Bruce Blaine
MW 10:25AM - 11:40AM
|
|
Pre-req: STAT 212 and STAT 216 (or equivalent) or instructor permission Co-listed with STAT 276, STAT 476 This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.
|
|
STAT 390A-5
Javier Bautista
7:00PM - 7:00PM
|
|
No description
|
|
STAT 390A-6
Joseph Ciminelli
7:00PM - 7:00PM
|
|
No description
|
|
STAT 392-1
–
7:00PM - 7:00PM
|
|
Registration for Independent Study courses needs to be completed thru the instructions for online independent study registration. STAT 392 - an online independent study form is available at: https://www.rochester.edu/college/ccas/handbook/independent-studies.html
|
|
STAT 415-2
Javier Bautista
TR 12:30PM - 1:45PM
|
|
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.
|
|
STAT 416-2
Nicholas Zaino
TR 9:40AM - 10:55AM
|
|
Co-located with STAT 216, STAT 416 Prerequisites: STAT 211, STAT 212, or STAT 213. Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 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.
|
|
STAT 423-1
Joseph Ciminelli
TR 11:05AM - 12:20PM
|
|
Co-located: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework.
|
|
STAT 476-1
Bruce Blaine
MW 10:25AM - 11:40AM
|
|
Co-listed with STAT 276w, STAT 276
|
Spring 2024
| Number | Title | Instructor | Time |
|---|---|
| Monday | |
|
STAT 213-17
Aruni Jayathilaka
|
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses. |
|
|
STAT 213-2
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-13
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-10
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-11
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-12
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
| Monday and Wednesday | |
|
STAT 201-1
Yuanyuan Pan
|
|
|
Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. Same as MATH 201.This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time. |
|
|
STAT 276-1
Bruce Blaine
|
|
|
Pre-req: STAT 212 and STAT 216 (or equivalent) or instructor permission Co-listed with STAT 276W, STAT 476 This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class. |
|
|
STAT 276W-1
Bruce Blaine
|
|
|
Pre-req: STAT 212 and STAT 216 (or equivalent) or instructor permission Co-listed with STAT 276, STAT 476 This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations. Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class. |
|
|
STAT 476-1
Bruce Blaine
|
|
|
Co-listed with STAT 276w, STAT 276 |
|
|
STAT 201-2
Peter Oberly
|
|
|
Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. Same as MATH 201.This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time. |
|
|
STAT 275-1
Katherine Grzesik
|
|
|
Pre-req: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class. |
|
|
STAT 275W-1
Katherine Grzesik
|
|
|
Pre-req: STAT 212 or instructor permission. Base R knowledge from STAT 212 is assumed, contact the instructor if this is not the case.
This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class. |
|
|
STAT 201-3
Mary Cook
|
|
|
Probability spaces, combinatorial problems, random variables and expectations, discrete and continuous distributions, generating functions, independence and dependence, binomial, normal, and Poisson laws, laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. Same as MATH 201.This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time. |
|
|
STAT 226W-2
Katherine Grzesik
|
|
|
Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R. |
|
| Tuesday | |
|
STAT 213-5
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 203-3
Javier Bautista
|
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics. |
|
|
STAT 213-14
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-4
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-19
Aruni Jayathilaka
|
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses. |
|
| Tuesday and Thursday | |
|
STAT 216-1
Nicholas Zaino
|
|
|
Prerequisites: STAT 211, STAT 212, or STAT 213. Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 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. |
|
|
STAT 416-2
Nicholas Zaino
|
|
|
Co-located with STAT 216, STAT 416 Prerequisites: STAT 211, STAT 212, or STAT 213. Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 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. |
|
|
STAT 212-2
Bruce Blaine
|
|
|
Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a non-calculus based introduction to statistical methodology and analyses that focuses on providing students with the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. . This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses |
|
|
STAT 216-2
Aruni Jayathilaka
|
|
|
Prerequisites: STAT 211, STAT 212, or STAT 213. Description: STT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 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. |
|
|
STAT 223-1
Joseph Ciminelli
|
|
|
Co-located: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework. |
|
|
STAT 423-1
Joseph Ciminelli
|
|
|
Co-located: STAT 223, STAT 423 Prerequisites: STAT 203 and MATH 164, or instructor permission. Description: In this course, the Bayesian approach to statistical inference will be explored. Topics to be discussed include single and multiple parameter models under conjugacy, uninformative and informative prior distribution specifications, hierarchical models, model checking, and modern computational techniques for posterior distribution approximation (e.g. Markov chain Monte Carlo). Basic familiarity with the R computing environment is assumed, as the course includes extensive R programming. Applications will be drawn from across the social and natural sciences, providing a strong foundation for applied data analyses within the Bayesian statistical framework. |
|
|
STAT 215-1
Javier Bautista
|
|
|
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. |
|
|
STAT 217-1
Nicholas Zaino
|
|
|
Co-located: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 217 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/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 415-2
Javier Bautista
|
|
|
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. |
|
|
STAT 212-3
Bruce Blaine
|
|
|
Class Info: YOU MUST REGISTER FOR A LAB WHEN REGISTERING FOR THE MAIN COURSE. Description: This course is a non-calculus based introduction to statistical methodology and analyses that focuses on providing students with the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Applications are taken from the social and natural sciences. Calculations are performed with statistical software such as R/RStudio. Students may earn degree credit for only one of these courses: STAT211, STAT212, STAT213, and BIOL/STAT214. This course is recommended for students majoring/minoring in statistics and students in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses |
|
|
STAT 213-1
Aruni Jayathilaka
|
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses. |
|
|
STAT 226W-1
Joseph Ciminelli
|
|
|
Prerequisites: STAT 211, STAT 212 or 213, and STAT 203 or equivalent. Description: Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference. Computing in R. |
|
|
STAT 203-1
Javier Bautista
|
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics. |
|
|
STAT 213-16
Aruni Jayathilaka
|
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses. |
|
| Wednesday | |
|
STAT 203-4
Javier Bautista
|
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics. |
|
|
STAT 213-3
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 213-15
Aruni Jayathilaka
|
|
|
Workshop that accompanies STAT 213 to further explore concepts of statistical methodology and computing in R. |
|
|
STAT 203-6
Javier Bautista
|
|
|
Cross Listed: MATH 203 (P), STAT 203 Prerequisites: MATH 201 Description: Discrete and continuous probability distributions and their properties. Principle of statistical estimation and inference. Point and interval estimation. Maximum likelihood method for estimation and inference. Tests of hypotheses and confidence intervals, contingency tables, and related topics. |
|
|
STAT 212-13
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-9
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-6
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 213-18
Aruni Jayathilaka
|
|
|
Class Info: YOU MUST REGISTER FOR A WORKSHOP WHEN REGISTERING FOR THE MAIN COURSE. Prerequisites: MATH 141 or equivalent. Description: This course is an introduction to statistical methodology, focusing on the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Students are exposed to basic data exploration, summarization of graphical display of data, axioms of probability, distributions and related theory, parameter estimation, and statistical inference. Advanced topics include linear correlation and regression analysis. Students will perform calculations with statistical software such as R/RStudio. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STAT 211, STAT 212, STAT 213, or BIOL/STAT 214.This course is recommend for students majoring in Statistics, Economics, or Computer Science, or students looking for a mathematical introduction to statistics who are likely to continue on to other upper-level methodology courses. |
|
|
STAT 212-10
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
| Thursday | |
|
STAT 212-11
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-12
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-15
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-16
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-8
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-17
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-4
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
| Friday | |
|
STAT 212-19
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-20
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-5
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-21
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-14
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-18
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-7
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|
|
STAT 212-22
Bruce Blaine
|
|
|
This lab accompanies STAT 212 to further explore applying statistical methodology through computing in R. Students must register for a lab when registering for the main lecture. Please plan on bringing a personal laptop with you to complete the computing activities. |
|