Courses
Spring Term Schedule
The default view for the table below is "Sortable". This will allow you to sort any column in ascending order by clicking on its column heading.
Spring 2018
Number  Title  Instructor  Time 

STT 201 (MTH 201)
GAGE M
MW 2:00PM  3:15PM


Probability spaces; combinatorial problems; random variables and expectations; discrete and continuous distributions; generating functions; independence and dependence; binomial, normal, and Poisson laws; laws of large numbers. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. BUILDING: GRGEN  ROOM: 108 PREREQUISITES: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. 

STT 201 (MTH 201)
ZHONG J
MW 10:25AM  11:40AM


Probability spaces; combinatorial problems; random variables and expectations; discrete and continuous distributions; generating functions; independence and dependence; binomial, normal, and Poisson laws; laws of large numbers. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. BUILDING: B&L  ROOM: 106 PREREQUISITES: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. 

STT 203 (STT 203)
BAUTISTA J
TR 9:40AM  10:55AM


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. BUILDING: GRGEN  ROOM: 109 PREREQUISITES: STT 201 or familiarity with the elementary principles of probability, expected value, variance and covariance. 

STT 203 (STT 203)
PODURI R
TR 11:05AM  12:20PM


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. BUILDING: HYLAN  ROOM: 202 PREREQUISITES: STT 201 or familiarity with the elementary principles of probability, expected value, variance and covariance. Same as MTH 203. 

STT 211
ZAINO N
MW 11:50AM  1:05PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1.Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the social sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn degree credit for only one of these courses: STT 211 and STT 212. BUILDING: LATT  ROOM: 201 

STT 212
MCDERMOTT M
TR 11:05AM  12:20PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1. Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the natural sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STT 211 and STT 212. Students who have successfully completed STT 213 should not take STT 212. BUILDING: STRNG  ROOM: LOWER 

STT 212
MCDERMOTT M
TR 4:50PM  6:05PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1. Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the natural sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STT 211 and STT 212. Students who have successfully completed STT 213 should not take STT 212. BUILDING: HUTCH  ROOM: 140 

STT 212
CIMINELLI J
TR 12:30PM  1:45PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1. Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the natural sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STT 211 and STT 212. Students who have successfully completed STT 213 should not take STT 212 BUILDING: MEL  ROOM: 203 

STT 214 (BIO 214)
PRESGRAVES D
TR 12:30PM  1:45PM


This course will familiarize students with statistical concepts necessary to evaluate the primary literature in the biological sciences. It will improve studentsâ€™ statistical literacy and sharpen analytical thinking. Topics covered in the course will include: descriptive statistics and graphics, estimation, elementary probability theory, statistical distributions, hypothesis testing, goodness of fit, experimental design, correlation, analysis of variance and regression. If students would like to learn R, students should consider the lab, BIO 218P. The lab is not required for BIO 214. BUILDING: GRGEN  ROOM: 109 

STT 214 (BIO 214)
PRESGRAVES D
T 4:15PM  6:15PM


No description BUILDING: GEN  ROOM: 321 

STT 214 (BIO 214)
PRESGRAVES D
M 5:15PM  7:15PM


No description BUILDING: GEN  ROOM: 321 

STT 214 (BIO 214)
PRESGRAVES D
W 5:00PM  7:00PM


No description BUILDING: GEN  ROOM: 323 

STT 214 (BIO 214)
PRESGRAVES D
F 5:15PM  7:15PM


No description BUILDING: GEN  ROOM: 321 

STT 214W (BIO 214W)
PRESGRAVES D
–


Optional UpperLevel Writing Course for BIO 214 BUILDING:  ROOM: 

STT 216 (STT 216)
ZAINO N
TR 9:40AM  10:55AM


This course focuses on the practical use of statistical methods beyond what is covered in STT 211, STT 212 and STT 213. The level of sophistication is high when it comes to the models and methods needed to analyze data and interpret results. Topics include randomization tests, bootstrapping, nonparametric tests, ANOVA models (fixed, random and mixed models, crossed and nested), multiple comparisons and linear contrasts, multiple linear regression, binary logistic regression and related topics. The course uses examples and case studies from both the natural and social sciences. The use of computer programs is emphasized for calculations and there is a strong emphasis on assumptions, models and interpretation of results. BUILDING: CSB  ROOM: 209 PREREQUISITES: STT 211, STT 212, or STT 213 

STT 218
CIMINELLI J
TR 11:05AM  12:20PM


This course offers an introduction to methods used for analyzing categorical data. The first portion of this course focuses on contingency table analyses. In particular, both twoway and threeway tables are introduced, along with inferential methods for determining significant associations between categorical responses. In the second portion of the course, emphasis is placed on regression models for categorical outcomes that are binary, polytomous, ordinal, or counts. Particular attention is given to logistic, probit, and loglinear models, along with associated inferential tests and model diagnostics. Examples and applications are taken from public health, epidemiology, and the behavioral and social sciences. R is introduced as the primary statistical software for performing data analyses. BUILDING: GAVET  ROOM: 312 PREREQUISITES: STT 211, STT 212, STT 213, STT 214 or equivalent 

STT 226W
PODURI R
TR 2:00PM  3:15PM


Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference, and computer packages. Computer programs including JMP and SAS. BUILDING: HYLAN  ROOM: 101 PREREQUISITES: STT 211, STT 212 or 213, and STT 203 or equivalent.. 

STT 391
–
–


No description BUILDING:  ROOM: 

STT 391W
–
–


No description BUILDING:  ROOM: 

STT 591
–
–


No description BUILDING:  ROOM: 

STT 999
–
–


No description BUILDING:  ROOM: 
Spring 2018
Number  Title  Instructor  Time 

Monday  
STT 214 (BIO 214)
PRESGRAVES D
M 5:15PM  7:15PM


No description BUILDING: GEN  ROOM: 321 

Monday and Wednesday  
STT 201 (MTH 201)
ZHONG J
MW 10:25AM  11:40AM


Probability spaces; combinatorial problems; random variables and expectations; discrete and continuous distributions; generating functions; independence and dependence; binomial, normal, and Poisson laws; laws of large numbers. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. BUILDING: B&L  ROOM: 106 PREREQUISITES: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. 

STT 211
ZAINO N
MW 11:50AM  1:05PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1.Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the social sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn degree credit for only one of these courses: STT 211 and STT 212. BUILDING: LATT  ROOM: 201 

STT 201 (MTH 201)
GAGE M
MW 2:00PM  3:15PM


Probability spaces; combinatorial problems; random variables and expectations; discrete and continuous distributions; generating functions; independence and dependence; binomial, normal, and Poisson laws; laws of large numbers. MTH 162 (or equivalent) is a strict prerequisite and must be completed before taking 201. MTH 162 and 201 cannot be taken concurrently. This course uses the Tuesday/Thursday 08:0009:30am Common Exam time. BUILDING: GRGEN  ROOM: 108 PREREQUISITES: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. 

Tuesday  
STT 214 (BIO 214)
PRESGRAVES D
T 4:15PM  6:15PM


No description BUILDING: GEN  ROOM: 321 

Tuesday and Thursday  
STT 216 (STT 216)
ZAINO N
TR 9:40AM  10:55AM


This course focuses on the practical use of statistical methods beyond what is covered in STT 211, STT 212 and STT 213. The level of sophistication is high when it comes to the models and methods needed to analyze data and interpret results. Topics include randomization tests, bootstrapping, nonparametric tests, ANOVA models (fixed, random and mixed models, crossed and nested), multiple comparisons and linear contrasts, multiple linear regression, binary logistic regression and related topics. The course uses examples and case studies from both the natural and social sciences. The use of computer programs is emphasized for calculations and there is a strong emphasis on assumptions, models and interpretation of results. BUILDING: CSB  ROOM: 209 PREREQUISITES: STT 211, STT 212, or STT 213 

STT 203 (STT 203)
BAUTISTA J
TR 9:40AM  10:55AM


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. BUILDING: GRGEN  ROOM: 109 PREREQUISITES: STT 201 or familiarity with the elementary principles of probability, expected value, variance and covariance. 

STT 218
CIMINELLI J
TR 11:05AM  12:20PM


This course offers an introduction to methods used for analyzing categorical data. The first portion of this course focuses on contingency table analyses. In particular, both twoway and threeway tables are introduced, along with inferential methods for determining significant associations between categorical responses. In the second portion of the course, emphasis is placed on regression models for categorical outcomes that are binary, polytomous, ordinal, or counts. Particular attention is given to logistic, probit, and loglinear models, along with associated inferential tests and model diagnostics. Examples and applications are taken from public health, epidemiology, and the behavioral and social sciences. R is introduced as the primary statistical software for performing data analyses. BUILDING: GAVET  ROOM: 312 PREREQUISITES: STT 211, STT 212, STT 213, STT 214 or equivalent 

STT 212
MCDERMOTT M
TR 11:05AM  12:20PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1. Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the natural sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STT 211 and STT 212. Students who have successfully completed STT 213 should not take STT 212. BUILDING: STRNG  ROOM: LOWER 

STT 203 (STT 203)
PODURI R
TR 11:05AM  12:20PM


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. BUILDING: HYLAN  ROOM: 202 PREREQUISITES: STT 201 or familiarity with the elementary principles of probability, expected value, variance and covariance. Same as MTH 203. 

STT 212
CIMINELLI J
TR 12:30PM  1:45PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1. Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the natural sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STT 211 and STT 212. Students who have successfully completed STT 213 should not take STT 212 BUILDING: MEL  ROOM: 203 

STT 214 (BIO 214)
PRESGRAVES D
TR 12:30PM  1:45PM


This course will familiarize students with statistical concepts necessary to evaluate the primary literature in the biological sciences. It will improve studentsâ€™ statistical literacy and sharpen analytical thinking. Topics covered in the course will include: descriptive statistics and graphics, estimation, elementary probability theory, statistical distributions, hypothesis testing, goodness of fit, experimental design, correlation, analysis of variance and regression. If students would like to learn R, students should consider the lab, BIO 218P. The lab is not required for BIO 214. BUILDING: GRGEN  ROOM: 109 

STT 226W
PODURI R
TR 2:00PM  3:15PM


Simple linear, multiple, and polynomial regression methods and applications; ordinary and generalized least squares, estimation, tests of hypotheses, and confidence intervals, and simultaneous inference, and computer packages. Computer programs including JMP and SAS. BUILDING: HYLAN  ROOM: 101 PREREQUISITES: STT 211, STT 212 or 213, and STT 203 or equivalent.. 

STT 212
MCDERMOTT M
TR 4:50PM  6:05PM


This course is a noncalculus based introduction to the major concepts and tools for collecting, analyzing and drawing conclusions from data. Students are exposed to four broad conceptual themes: 1. Understanding data and turning data into information through graphs and numerical methods 2. Collecting data through experiments and observational studies. 3. Exploring random phenomena using probability as the basis for statistical inference 4. Statistical Inference: Estimating population parameters and testing hypotheses Topics include regression, ANOVA, and contingency tables Examples and applications are taken from the natural sciences. Use of TI83/TI84 or common statistical computing packages. Please note that, because of the significant overlap between them, students may earn credit for only one of these courses: STT 211 and STT 212. Students who have successfully completed STT 213 should not take STT 212. BUILDING: HUTCH  ROOM: 140 

Wednesday  
STT 214 (BIO 214)
PRESGRAVES D
W 5:00PM  7:00PM


No description BUILDING: GEN  ROOM: 323 

Friday  
STT 214 (BIO 214)
PRESGRAVES D
F 5:15PM  7:15PM


No description BUILDING: GEN  ROOM: 321 

TBA  
STT 214W (BIO 214W)
PRESGRAVES D
–


Optional UpperLevel Writing Course for BIO 214 BUILDING:  ROOM: 

STT 391
–
–


No description BUILDING:  ROOM: 

STT 391W
–
–


No description BUILDING:  ROOM: 

STT 591
–
–


No description BUILDING:  ROOM: 

STT 999
–
–


No description BUILDING:  ROOM: 