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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.

Location

(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.

Location

(W 7:40PM - 8:55PM)

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.

Location

(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.

Location

(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.

Location

(R 7:40PM - 8:55PM)

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.

Location

(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.

Location

(F 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.

Location

(F 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.

Location

(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.

Location

(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.

Location

(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.

Location

(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.

Location

(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.

Location

(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.

Location

(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.

Location

(R 7:40PM - 8:55PM)

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.

Location

(W 4:50PM - 6:05PM)

Location

( 7:00PM - 7:00PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(TR 2:00PM - 3:15PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(T 6:15PM - 7:30PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(T 3:25PM - 4:40PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(W 10:25AM - 11:40AM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(W 4:50PM - 6:05PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(W 4:50PM - 6:05PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(W 2:00PM - 3:15PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(W 3:25PM - 4:40PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(W 6:15PM - 7:30PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(R 3:25PM - 4:40PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(R 6:15PM - 7:30PM)

Prerequisites: MATH 141 or equivalent.
This course is an introduction to statistical methodology that focuses on the mathematical and computational tools needed to practically apply such methods. Topics to be covered include data exploration and summarization, elementary probability, combinatorics, random variables, distribution theory, parameter estimation, and methods of statistical inference, including hypothesis tests and confidence intervals for one and two sample means and proportions, ANOVA, contingency table analyses, and regression analysis, including multiple regression. Students will perform calculations with the statistical software R/RStudio. This course is recommended for students majoring in Statistics, Economics, Computer Science, Data Science, or Business or students looking for a mathematical introduction to statistics who are likely to continue on to other 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.

Location

(T 4:50PM - 6:05PM)

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.

Location

(MW 2:00PM - 3:15PM)

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.

Location

(TR 2:00PM - 3:15PM)

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.

Location

(TR 3:25PM - 4:40PM)

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.

Location

(W 4:50PM - 6:05PM)

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.

Location

(M 3:25PM - 4:40PM)

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.

Location

(F 12:30PM - 1:45PM)

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.

Location

(TR 9:40AM - 10:55AM)

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.

Location

(MW 2:00PM - 3:15PM)

Pre-requisities: STAT 180, STAT 190

This course offers an introduction to methods used for analyzing categorical data. The first portion of this course focuses on contingency table analyses. In particular, both two-way and three-way tables are introduced, along with inferential methods for determining significant associations between categorical responses. In the second portion of the course, emphasis is placed on regression models for categorical outcomes that are binary, polytomous, ordinal, or counts. Particular attention is given to logistic, probit, and log-linear models, along with associated inferential tests and model diagnostics. Examples and applications are taken from public health, epidemiology, and the behavioral and social sciences. R is introduced as the primary statistical software for performing data analyses.

Location

(TR 11:05AM - 12:20PM)

This course offers an introduction to nonparametric statistical methodology, modeling techniques and theory. Topics include, but are not limited to, nonparametric hypothesis testing and inference, density estimation, adaptive smoothing, and resampling estimation procedures. ÌýR/RStudio will be used for computational examples, so previous experience with such software is recommended.

Location

(MW 9:00AM - 10:15AM)

Pre-requisities: STAT 180 or STAT 190, and STAT 203

Description: Simple random, stratified, systematic, and cluster sampling; estimation of the means, proportions, variance, and ratios of a finite population. Ratio and regression methods of estimation and the use of auxiliary information. The nonresponse problem. Prerequisite: familiarity with the concepts of expectation, variance, covariance and correlation.

Location

(TR 12:30PM - 1:45PM)

Pre-req: STAT 212, STAT 180 and STAT 216 (or equivalent) or instructor permission

This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.

Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.

Location

(MW 10:25AM - 11:40AM)

Pre-req: STAT 212 and STAT 216 (or equivalent) or instructor permission

This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.

Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.

Location

(MW 10:25AM - 11:40AM)

Prerequisites: STAT 216 or STAT 226W. CROSS LISTED with STAT 477.

Description: The first half of this course covers programming in R, SAS, and JMP. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numerically-oriented procedures such as linear models will be included where appropriate. Each student will analyze a real-life data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. Basic skills with the Windows operating system, a text editor (such as Notepad), and Microsoft Excel is assumed.

Location

(MW 11:50AM - 1:05PM)

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)

Location

( 7:00PM - 7:00PM)

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.Ìý 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)

Location

( 7:00PM - 7:00PM)

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.Ìý 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)​

Location

( 7:00PM - 7:00PM)

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.

Location

(TR 9:40AM - 10:55AM)

Pre-requisities: STAT 180, STAT 190 or equivalent

This course offers an introduction to methods used for analyzing categorical data. The first portion of this course focuses on contingency table analyses. In particular, both two-way and three-way tables are introduced, along with inferential methods for determining significant associations between categorical responses. In the second portion of the course, emphasis is placed on regression models for categorical outcomes that are binary, polytomous, ordinal, or counts. Particular attention is given to logistic, probit, and log-linear models, along with associated inferential tests and model diagnostics. Examples and applications are taken from public health, epidemiology, and the behavioral and social sciences. R is introduced as the primary statistical software for performing data analyses.

Location

(TR 11:05AM - 12:20PM)

Co-located with STT 419, STT 219

Prerequisites: STT 216, and MTH 142. STT 203 is recommended

Description: This course offers an applied introduction to nonparametric statistical methods and modeling techniques. The first half of the course covers nonparametric hypothesis testing and inference. The second half covers density estimation techniques, smoothing splines, local polynomial regression, and cross-validation. The course culminates in an applied project involving nonparametric techniques to analyze real-world data. R/RStudio will be used for computation, so previous experience with such software is recommended.

Location

(MW 9:00AM - 10:15AM)

Pre-req: STAT 212, STAT 180 and STAT 216 (or equivalent) or instructor permission

This course offers an introduction to statistical computing in the R environment with the goal of exploratory analyses and effective communication using "tidyverse". With a main goal of communicating results to various audiences, this course will require writing via communicating results in a clear and effective manner based on the intended audience. This includes cleaning and preparing data for analysis, exploratory data analyses using simple graphics and tables, acknowledging and working with missing data, advanced graphics including map graphics to communicate results, statistical hypothesis generation & confirmation, introduction to the LaTeX typesetting language, advanced R Markdown formatting techniques (HTML, PDF, Word), figure and table creation with proper adaptive labels and captions, and bibliography with adaptive citations.

Basic skills with a test editor (such as Notepad) and Microsoft Excel are assumed. Students are expected to have basic skills in R and RStudio as covered in STAT 212. This course will be held in a computer lab with R and RStudio installed but students will need computer access outside of class.

Location

(MW 10:25AM - 11:40AM)

Prerequisites: STAT 216 or STAT 226W. CROSS LISTED with STAT 477.

Description: The first half of this course covers programming in R, SAS, and JMP. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numerically-oriented procedures such as linear models will be included where appropriate. Each student will analyze a real-life data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. Basic skills with the Windows operating system, a text editor (such as Notepad), and Microsoft Excel is assumed.

Location

(MW 11:50AM - 1:05PM)