Spring Term Schedule
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Spring 2025
| Number | Title | Instructor | Time |
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STAT 180-01
Bruce Blaine
MW 2:00PM - 3:15PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-03
Bruce Blaine
R 6:15PM - 7:30PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-04
Bruce Blaine
F 10:25AM - 11:40AM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-05
Bruce Blaine
W 6:15PM - 7:30PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-06
Bruce Blaine
F 2:00PM - 3:15PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-07
Bruce Blaine
R 4:50PM - 6:05PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-08
Bruce Blaine
W 4:50PM - 6:05PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-09
Bruce Blaine
W 7:40PM - 8:55PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-10
Bruce Blaine
R 12:30PM - 1:45PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-11
Bruce Blaine
R 2:00PM - 3:15PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-12
Bruce Blaine
W 4:50PM - 6:50PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-13
Bruce Blaine
F 12:30PM - 1:45PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-14
Bruce Blaine
R 2:00PM - 3:15PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-15
Bruce Blaine
R 3:25PM - 4:40PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-16
Bruce Blaine
R 6:15PM - 7:30PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-17
Bruce Blaine
F 2:00PM - 3:15PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-18
Bruce Blaine
F 9:00AM - 10:15AM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-19
Bruce Blaine
F 10:25AM - 11:40AM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-20
Bruce Blaine
F 11:50AM - 1:05PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 180-21
Bruce Blaine
F 4:50PM - 6:05PM
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200.
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STAT 181-01
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7:00PM - 7:00PM
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This is a self-paced module for students who already have STAT 180 credit but have since determined a need for STAT 190 for their particular degree program. After independently working through the material of STAT 190, you will complete an equivalency exam at the end of the semester to assess statistical competency at the STAT 190 level. Graded on a pass/fail basis.
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STAT 190-01
Aruni Jayathilaka
TR 2:00PM - 3:15PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-03
Aruni Jayathilaka
T 12:30PM - 1:45PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-04
Aruni Jayathilaka
T 4:50PM - 6:05PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-05
Aruni Jayathilaka
T 11:05AM - 12:20PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-06
Aruni Jayathilaka
M 4:50PM - 6:05PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-07
Aruni Jayathilaka
M 6:15PM - 7:30PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-08
Aruni Jayathilaka
M 7:40PM - 8:55PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-09
Aruni Jayathilaka
M 3:25PM - 4:40PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-10
Aruni Jayathilaka
T 3:25PM - 4:40PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-11
Aruni Jayathilaka
W 3:25PM - 4:40PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-12
Aruni Jayathilaka
M 9:00AM - 10:15AM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-13
Aruni Jayathilaka
W 6:15PM - 7:30PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-14
Aruni Jayathilaka
T 6:15PM - 7:30PM
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Prerequisites: MATH 141 or equivalent.
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STAT 190-15
Aruni Jayathilaka
M 2:00PM - 3:15PM
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Prerequisites: MATH 141 or equivalent.
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STAT 201-01
Peter Oberly
MW 10:25AM - 11:40AM
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
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STAT 201-02
Carl Mueller
MW 12:30PM - 1:45PM
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
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STAT 201-03
Carl Mueller
MW 2:00PM - 3:15PM
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Cross Listed: MATH 201 (P), STAT 201 Prerequisites: MATH 162 or equivalent. MATH 164 recommended. Probability spaces; combinatorial problems; discrete and continuous distributions; independence and dependence; moment generating functions; joint distributions; expectation and variance; sums of random variables; central limit theorem; laws of large numbers. MATH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MATH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:00-09:30am Common Exam time.
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STAT 203-01
Javier Bautista
TR 3:25PM - 4:40PM
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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.
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STAT 203-02
Javier Bautista
T 12:30PM - 1:45PM
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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.
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STAT 203-03
Javier Bautista
W 10:25AM - 11:40AM
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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.
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STAT 203-04
Javier Bautista
F 2:00PM - 3:15PM
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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.
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STAT 203-05
Javier Bautista
W 4:50PM - 6:05PM
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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.
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STAT 203-06
Javier Bautista
M 3:25PM - 4:40PM
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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.
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STAT 215-01
Javier Bautista
TR 12:30PM - 1:45PM
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Pre-requisites: STAT 212 or Equivalent Co-Located: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages.
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STAT 216-01
Nicholas Zaino
TR 9:40AM - 10:55AM
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Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent Co-located with STAT 416 Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.
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STAT 216-02
Katherine Grzesik
MW 3:25PM - 4:40PM
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Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.
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STAT 217-01
Nicholas Zaino
TR 12:30PM - 1:45PM
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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.
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STAT 218-01
Aruni Jayathilaka
TR 11:05AM - 12:20PM
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Pre-requisities: STAT 180, STAT 190, STAT 212, STAT 213 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.
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STAT 223-01
Joseph Ciminelli
TR 11:05AM - 12:20PM
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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.
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STAT 226W-01
Joseph Ciminelli
TR 2:00PM - 3:15PM
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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.
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STAT 275-01
Katherine Grzesik
MW 12:30PM - 1:45PM
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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.
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STAT 275W-01
Katherine Grzesik
MW 12:30PM - 1:45PM
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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.
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STAT 276-01
Bruce Blaine
MW 10:25AM - 11:40AM
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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.
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STAT 276W-01
Bruce Blaine
MW 10:25AM - 11:40AM
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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.
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STAT 392-01
–
7:00PM - 7:00PM
|
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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
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STAT 415-02
Javier Bautista
TR 12:30PM - 1:45PM
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|
Pre-requisites: STAT 212 or Equivalent Co-Located: STAT 415 This course will start with an introduction to the scientific method and good practices in experimental design. It will cover a review of point estimation, confidence intervals and hypothesis testing material covered in an introductory statistics course. It will proceed to cover the different experimental designs (Completely Randomized Design, Full Factorial, Central Composite Design, 2k, Fractional Factorial, Screening Designs). The analysis of the data from each design will also be covered using computer software packages.
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STAT 416-01
Nicholas Zaino
TR 9:40AM - 10:55AM
|
|
Co-located with STAT 216, STAT 416 Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent. Description: STAT 216 offers a second course in foundational techniques of statistical analyses, focusing on advanced inference topics (power, inference for variances and correlations, nonparametric testing, exact binomial tests, violations of assumptions), regression modeling (OLS regression, multiple regression, model diagnostics, outlier analysis, transformations, variable selection, logistic models), and analysis of variance (1- and 2-way ANOVA, contrasts, multiple comparisons, analysis of covariance). This course is non-calculus based and will focus on the practical use of statistical techniques for data analyses rather than on theory. As such, this course will rely upon the use of statistical software as a tool for examining data and compiling results into presentable reports.
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|
STAT 417-01
Nicholas Zaino
TR 12:30PM - 1:45PM
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|
Co-located: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 417 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/non-linear models, collinear data, robust regression, time series techniques, and other related modeling topics. In the second half of the course, advanced analysis of variance (ANOVA) techniques will be explored, focusing mainly on repeated measures, mixed models, multivariate ANOVA, and nonparametric alternatives. Additional topics include structural equation models, missing data, and meta-analysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory.
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|
STAT 423-01
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.
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|
STAT 476-01
Bruce Blaine
MW 10:25AM - 11:40AM
|
|
Co-listed with STAT 276w, STAT 276
|
Spring 2025
| Number | Title | Instructor | Time |
|---|---|
| Monday | |
|
STAT 190-12
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-15
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-09
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 203-06
Javier Bautista
|
|
|
*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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 190-06
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-07
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-08
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
| Monday and Wednesday | |
|
STAT 201-01
Peter Oberly
|
|
|
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. |
|
|
STAT 276-01
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-01
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 476-01
Bruce Blaine
|
|
|
Co-listed with STAT 276w, STAT 276 |
|
|
STAT 201-02
Carl Mueller
|
|
|
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. |
|
|
STAT 275-01
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-01
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 180-01
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 201-03
Carl Mueller
|
|
|
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. |
|
|
STAT 216-02
Katherine Grzesik
|
|
|
Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, 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. |
|
| Tuesday | |
|
STAT 190-05
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-03
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 203-02
Javier Bautista
|
|
|
*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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 190-10
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-04
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 190-14
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
| Tuesday and Thursday | |
|
STAT 216-01
Nicholas Zaino
|
|
|
Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, or equivalent Co-located with STAT 416 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 416-01
Nicholas Zaino
|
|
|
Co-located with STAT 216, STAT 416 Pre-requisites: STAT 180, STAT 190, STAT 212, STAT 213, 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. |
|
|
STAT 218-01
Aruni Jayathilaka
|
|
|
Pre-requisities: STAT 180, STAT 190, STAT 212, STAT 213 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. |
|
|
STAT 223-01
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 423-01
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 215-01
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 217-01
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 415-02
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 417-01
Nicholas Zaino
|
|
|
Co-located: STAT 417, STAT 217 Prerequisites: STAT 216. Description: STAT 417 offers an advanced exploration of statistical techniques used for data analyses. The first half of the course will focus on regression, with topics including weighted least squares, polynomial/non-linear models, collinear data, robust regression, time series techniques, and other related modeling topics. In the second half of the course, advanced analysis of variance (ANOVA) techniques will be explored, focusing mainly on repeated measures, mixed models, multivariate ANOVA, and nonparametric alternatives. Additional topics include structural equation models, missing data, and meta-analysis. This course will focus on the practical use of statistical techniques and will incorporate some basic theory. |
|
|
STAT 190-01
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 226W-01
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 203-01
Javier Bautista
|
|
|
*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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. |
|
| Wednesday | |
|
STAT 203-03
Javier Bautista
|
|
|
*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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 190-11
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 180-08
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-12
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 203-05
Javier Bautista
|
|
|
*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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 180-05
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 190-13
Aruni Jayathilaka
|
|
|
Prerequisites: MATH 141 or equivalent. |
|
|
STAT 180-09
Bruce Blaine
|
|
|
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. |
|
| Thursday | |
|
STAT 180-10
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-11
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-14
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-15
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-07
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-03
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-16
Bruce Blaine
|
|
|
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. |
|
| Friday | |
|
STAT 180-18
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-04
Bruce Blaine
|
|
|
This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
|
|
STAT 180-19
Bruce Blaine
|
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-20
Bruce Blaine
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-13
Bruce Blaine
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-06
Bruce Blaine
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 180-17
Bruce Blaine
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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STAT 203-04
Javier Bautista
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*YOU MUST REGISTER FOR A RECITATION WHEN REGISTERING FOR MAIN COURSE.* 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. |
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STAT 180-21
Bruce Blaine
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This course is a non-calculus based introduction to statistical analyses that focuses on the tools and computational experience needed to analyze data in the applied setting. Topics to be covered include data collection through experiments and observational studies, numerical and graphical data summarization, basic probability rules, statistical distributions, parameter estimation, and methods of statistical inference, regression analysis, ANOVA, and contingency tables. Calculations are performed with statistical software such as R/RStudio. This course is recommended for students majoring/minoring in statistics, fulfilling pre-medical requirement, or in the social and natural sciences looking for an applied statistics course that can be used as a foundation for upper-level methodology courses. Students may earn degree credit for only one of these courses: STAT 180, STAT 190, STAT 212, STAT 213, ECON 230, PSCI 200. |
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