Biostatistics (STA)

Recommended for undergraduate students in the health sciences. Covers basic statistical concepts and techniques such as descriptive statistics, regression and correlation, analysis-of-variance, survival analysis and categorical data analysis as it pertains to clinical experiments and epidemiological investigations.

Covers topics in descriptive statistics, probability, inference, and experimental design, all of which are put together to draw conclusions from uncertainty through analysis of experimental data. Although a general statistical methods course, the material (through examples) is geared towards sciences majors, especially those in the health sciences. Looks into the underlying reasoning behind the techniques rather than just pure application. Students enrolled in this class have the additional resource of using the statistics lab for extra help which is located in 806 Kimball Tower.

Covers topics in descriptive statistics, probability, inference, and experimental design, all of which are put together to draw conclusions from uncertainty through analysis of experimental data. Although a general statistical methods course, the material (through examples) is geared towards sciences majors, especially those in the health sciences. Looks into the underlying reasoning behind the techniques rather than just pure application.

The one credit UB Seminar is focused on a big idea or challenging issue to engage students with questions of significance in a field of study and, ultimately, to connect their studies with issues of consequence in the wider world. Essential to the UB Curriculum, the Seminar helps transition to UB through an early connection to UB faculty and the undergraduate experience at a comprehensive, research university. This course is equivalent to any 198 offered in any subject. This course is a controlled enrollment (impacted) course. Students who have previously attempted the course and received a grade of F or R may not be able to repeat the course during the fall or spring semester.

The three credit UB Seminar is focused on a big idea or challenging issue to engage students with questions of significance in a field of study and, ultimately, to connect their studies with issues of consequence in the wider world. Essential to the UB Curriculum, the Seminar helps students with common learning outcomes focused on fundamental expectations for critical thinking, ethical reasoning, and oral communication, and learning at a university, all within topic focused subject matter. The Seminars provide students with an early connection to UB faculty and the undergraduate experience at a comprehensive, research university. This course is equivalent to any 199 offered in any subject. This course is a controlled enrollment (impacted) course. Students who have previously attempted the course and received a grade of F or R may not be able to repeat the course during the fall or spring semester.

The purpose of this course is to familiarize students with PC-based statistical computing applications for public health. This course will develop basic skills in the use of a statistical package through classroom demonstrations and independent lab assignments. The course will emphasize data definition, verification, descriptive and inferential statistics, and graphical presentation. The course should familiarize the students with the use of a statistical package and give them the skills needed for effective data management, data manipulation, and data analysis at a basic level.

Covers regression analysis and introduction to linear models. Topics include point estimation, confidence intervals, hypothesis testing, simple linear regression, multiple regression, analysis of covariance, and non-linear regression. The course uses statistical software and emphasizes hands-on applications to data sets from a variety of settings.

Advanced presentation of statistical methods for comparing populations and estimating and testing associations between variables. Topics include point estimation; confidence intervals; hypothesis testing; ANOVA models for 1, 2, and k way classifications; multiple comparisons; chi-square test of homogeneity; Fisher's exact test; McNemar's test; measures of association, including odds ratio, relative risks, Mantel-Haenszel tests of association, and standardized rates; repeated measures ANOVA; simple regression; and correlation.

Covers statistical methods for planning, conducting, and analyzing experiments to optimize a process. Topics include point estimation, confidence intervals, hypothesis testing, ANOVA models for 1, 2, and k-way classifications, multiple comparisons, randomized complete block designs, Latin square designs, and factorial designs. The course uses statistical software and emphasizes hands-on applications to data sets from a variety of settings.

Covers regression analysis and introduction to linear models. Topics include multiple regression, analysis of covariance, least square means, logistic regression, and non-linear regression. The course includes a one-hour computer lab and emphasizes hands-on applications to datasets from the health sciences.

Provides students with probability and distribution theory necessary for the study of statistics. Topics include axioms of probability theory, independence, conditional probability, random variables, discrete and continuous probability distributions, functions of random variables, moment generating functions, the Law of Large Numbers, and the Central Limit Theorem.

Provides students with probability and distribution theory necessary for the study of statistics. Topics include axioms of probability theory, independence, conditional probability, random variables, discrete and continuous probability distributions, functions of random variables, moment generating functions, the Law of Large Numbers, and the Central Limit Theorem.

Introduces principles of statistical inference. Introduces and develops classical methods of estimation, tests of significance, the Neyman-Pearson Theory of testing hypotheses, maximum likelihood methods, and Bayesian statistics.

Introduces principles of statistical inference. Introduces and develops classical methods of estimation, tests of significance, the Neyman-Pearson Theory of testing hypotheses, maximum likelihood methods, and Bayesian statistics.

Topics include descriptive statistics, probability concepts (such as independence and conditional probability), probability distributions of random variables, sampling distributions, estimation, confidence intervals, hypothesis testing, analysis of variance procedures, linear regression, and nonparametric methods. Computers and statistical packages are used throughout the course. Requires no extensive computer experience. Students enrolled in this class have the additional resource of using the statistics lab for specific questions which is located in 806 Kimball Tower.

Topics include descriptive statistics, probability concepts (such as independence and conditional probability), probability distributions of random variables, sampling distributions, estimation, confidence intervals, hypothesis testing, analysis of variance procedures, linear regression, and nonparametric methods. Computers and statistical packages are used throughout the course. Requires no extensive computer experience.

Topics vary by semester