Courses
Fall Term Schedule
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Fall 2018
Number  Title  Instructor  Time 

STT 201 (MTH 201)
KRISHNAN A
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: GRGEN  ROOM: 108 PREREQUISITES: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. 

STT 201 (MTH 201)
ALEVY I
TR 3:25PM  4:40PM


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: CSB  ROOM: 209 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: B&L  ROOM: 106 

STT 212
BAUTISTA J
MW 3:25PM  4:40PM


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: GAVET  ROOM: 312 

STT 212
GRZESIK K
MW 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 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
–
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:  ROOM: 

STT 213
MCDERMOTT M
TR 2:00PM  3:15PM


This course is an introduction to the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Linear correlation and simple regression analysis are also introduced. Students will learn how to use the statistical programming language R to analyze data. BUILDING: HOYT  ROOM: AUD PREREQUISITES: MTH 141 or equivalent. 

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: HYLAN  ROOM: 201 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: LCHAS  ROOM: 141 

STT 221W (STT 221W)
PODURI R
TR 12:30PM  1:45PM


Simple random, stratified, systematic, and cluster sampling; estimation of the means, proportions, variance, and ratios of a finite population. Ratio and regression methods of estimation and the use of auxiliary information. The nonresponse problem. Prerequisite: familiarity with the concepts of expectation, variance, covariance and correlation. BUILDING: HYLAN  ROOM: 102 PREREQUISITES: STT 211, STT 212 or STT 213, and 203 or equivalent. 

STT 262 (DSC 262)
CIMINELLI J
TR 2:00PM  3:15PM


This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment. BUILDING: WEGMN  ROOM: 1400 PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 

STT 277 (STT 277)
HECKLER C
TR 9:40AM  10:55AM


The first half of this course covers the elements of programming in R, SAS, and operation of the JMP graphical user interface. The student will learn how to get data into (and out of) these programs, execute fundamental statistical procedures, and write programs in R and SAS to document and automate analyses. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. BUILDING: GAVET  ROOM: 244 PREREQUISITES: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 477. 

STT 390
–
–


No description BUILDING:  ROOM: 

STT 391
–
–


No description BUILDING:  ROOM: 

STT 392
–
–


No description BUILDING:  ROOM: 

STT 394
–
–


No description BUILDING:  ROOM: 

STT 395
–
–


No description BUILDING:  ROOM: 

STT 477 (STT 277)
HECKLER C
TR 9:40AM  10:55AM


The first half of this course covers the elements of programming in R, SAS, and operation of the JMP graphical user interface. The student will learn how to get data into (and out of) these programs, execute fundamental statistical procedures, and write programs in R and SAS to document and automate analyses. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. BUILDING: GAVET  ROOM: 244 PREREQUISITES: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 477. 

STT 591
–
–


No description BUILDING:  ROOM: 

STT 595
–
–


No description BUILDING:  ROOM: 

STT 595A
–
–


No description BUILDING:  ROOM: 

STT 899
–
–


No description BUILDING:  ROOM: 

STT 999
–
–


No description BUILDING:  ROOM: 

STT 999A
–
–


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

Monday and Wednesday  
STT 201 (MTH 201)
KRISHNAN A
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: GRGEN  ROOM: 108 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: B&L  ROOM: 106 

STT 212
GRZESIK K
MW 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 212
BAUTISTA J
MW 3:25PM  4:40PM


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: GAVET  ROOM: 312 

Tuesday and Thursday  
STT 477 (STT 277)
HECKLER C
TR 9:40AM  10:55AM


The first half of this course covers the elements of programming in R, SAS, and operation of the JMP graphical user interface. The student will learn how to get data into (and out of) these programs, execute fundamental statistical procedures, and write programs in R and SAS to document and automate analyses. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. BUILDING: GAVET  ROOM: 244 PREREQUISITES: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 477. 

STT 277 (STT 277)
HECKLER C
TR 9:40AM  10:55AM


The first half of this course covers the elements of programming in R, SAS, and operation of the JMP graphical user interface. The student will learn how to get data into (and out of) these programs, execute fundamental statistical procedures, and write programs in R and SAS to document and automate analyses. The second half explores the use of this software to understand data from observational studies. The student will learn the philosophy, capabilities, and pitfalls of exploratory data analysis. Univariate, bivariate and multivariate methods will be introduced. Graphical methods will be emphasized, but numericallyoriented procedures such as linear models will be included where appropriate. Each student will analyze a reallife data set in some depth and write a report. Registration priority will be given to Statistics majors who will be taking the course in their senior year. BUILDING: GAVET  ROOM: 244 PREREQUISITES: STT 211, STT 212 or STT 213 and STT 216 or STT 226W. CROSS LISTED with STT 477. 

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: HYLAN  ROOM: 201 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: LCHAS  ROOM: 141 

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
–
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:  ROOM: 

STT 221W (STT 221W)
PODURI R
TR 12:30PM  1:45PM


Simple random, stratified, systematic, and cluster sampling; estimation of the means, proportions, variance, and ratios of a finite population. Ratio and regression methods of estimation and the use of auxiliary information. The nonresponse problem. Prerequisite: familiarity with the concepts of expectation, variance, covariance and correlation. BUILDING: HYLAN  ROOM: 102 PREREQUISITES: STT 211, STT 212 or STT 213, and 203 or equivalent. 

STT 262 (DSC 262)
CIMINELLI J
TR 2:00PM  3:15PM


This course will cover foundational concepts in probability and statistical inference, with an emphasis on topics of interest to computer scientists. Following an introduction to elementary probability theory, topics will include applications of combinatorics; Markov chains; principles of statistical classification (Bayes' rule, sensitivity and specificity, ROC curves) and random number generation. The theory of statistical estimation and hypothesis testing will be introduced, and applied to one and two sample inference for population means, proportions, variances and correlations. Nonparametric procedures will be discussed. Topics also include statistical modeling (ANOVA, simple and multiple regression), and computational methods. Students will be introduced to the R statistical computing environment. BUILDING: WEGMN  ROOM: 1400 PREREQUISITES: MTH 150 or MTH 150A; AND MTH 142 or MTH 161 or MTH 171 

STT 213
MCDERMOTT M
TR 2:00PM  3:15PM


This course is an introduction to the probability and statistical theory underlying the estimation of parameters and testing of hypotheses. Linear correlation and simple regression analysis are also introduced. Students will learn how to use the statistical programming language R to analyze data. BUILDING: HOYT  ROOM: AUD PREREQUISITES: MTH 141 or equivalent. 

STT 201 (MTH 201)
ALEVY I
TR 3:25PM  4:40PM


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: CSB  ROOM: 209 PREREQUISITES: MTH 162 or equivalent, MTH 164 recommended. Same as STT 201. 

TBA  
STT 390
–
–


No description BUILDING:  ROOM: 

STT 391
–
–


No description BUILDING:  ROOM: 

STT 392
–
–


No description BUILDING:  ROOM: 

STT 394
–
–


No description BUILDING:  ROOM: 

STT 395
–
–


No description BUILDING:  ROOM: 

STT 591
–
–


No description BUILDING:  ROOM: 

STT 595
–
–


No description BUILDING:  ROOM: 

STT 595A
–
–


No description BUILDING:  ROOM: 

STT 899
–
–


No description BUILDING:  ROOM: 

STT 999
–
–


No description BUILDING:  ROOM: 

STT 999A
–
–


No description BUILDING:  ROOM: 