A second course in statistics regression analysis pdf download

General Education Connecting career and curiosity, the General Education curriculum provides the opportunity for students to acquire transferable skills necessary to be successful in the future and to thrive while living in interconnected contexts.

Foundations grade of C or better is required. Cultures Requirement 6 credits are required and may satisfy other requirements United States Cultures: 3 credits International Cultures: 3 credits Writing Across the Curriculum 3 credits required from the college of graduation and likely prescribed as part of major requirements. Total Minimum Credits A minimum of degree credits must be earned for a baccalaureate degree.

Quality of Work Candidates must complete the degree requirements for their major and earn at least a 2. Limitations on Source and Time for Credit Acquisition The college dean or campus chancellor and program faculty may require up to 24 credits of course work in the major to be taken at the location or in the college or program where the degree is earned. Requirements for the Major To graduate, a student enrolled in the major must earn a grade of C or better in each course designated by the major as a C-required course, as specified by Senate Policy STAT CMPSC IE MATH BIOL W.

BIOL MATH W. Integrated B. Suggested Academic Plan The suggested academic plan s listed on this page are the plan s that are in effect during the academic year.

Actuarial Option: Statistics, B. Career Paths Statistics can be applied in a broad range of fields, including business, agriculture, finance, public policy, and many more. Careers Statisticians in the pharmaceutical industry work with doctors and research scientists to design and execute experiments and clinical trials. Print Options. Send Page to Printer. Download PDF of this page. Download Overview PDF.

Download Contact PDF. Prescribed Courses: Require a grade of C or better. Calculus With Analytic Geometry I. Calculus with Analytic Geometry II. Elementary Statistics. Additional Courses: Require a grade of C or better. ECON Introductory Microeconomic Analysis and Policy. Introductory Macroeconomic Analysis and Policy. ACCTG FIN RM Introduction to Programming. Introduction to Programming Techniques. Send to friends and colleagues. Modify, remix, and reuse just remember to cite OCW as the source.

Lecture Notes. Course Home Syllabus Calendar Lecture Notes Assignments Exams Download Course Materials There are two parts to the lecture notes for this class: The Brief Note, which is a summary of the topics discussed in class, and the Application Example, which gives real-wolrd examples of the topics covered. Need help getting started? Don't show me this again Welcome! Restricted to graduate students only.

See CHBE A nonmeasure theoretic introduction of stochastic processes. Students with suitable background in probability theory, real analysis and linear algebra are welcome to attend.

Some classical topics will be included, such as discrete time Markov chains, continuous time Markov chains, Martingales, Renewal processes and Brownian motion.

Students will learn some basic theory of stochastic processes, and their applications in several areas, including Queueing theory, Risk theory and Statistics. Students will also learn some probabilistic intuition and insights in thinking about problems, and some basic tools in the theoretical investigation of stochastic phenomenon and models.

Topics include censoring, discrete survival, parametric models, nonparametric one- and K-sample methods, Cox regression, regression diagnostics, time-dependent covariates, and multivariate survival outcomes.

Emphasis on key underlying concepts. Counting process-based theoretical justification and practical implementation will also be discussed.

The topics of the course focus on clinical trials designs and inferential techniques that are commonly used in the pharmaceutical industry. Topics include fixed sample designs for normal and survival data, two-sided group sequential design, Pocock's and O'Brien-Fleming boundaries, general theory of group sequential design, alpha and beta spending functions, one-sided designs with early stopping to accept the null hypothesis, non-inferiority designs, and inferential techniques.

Computing in SAS will be emphasized. See ASRM Modern techniques of predictive modeling, classification, and clustering are discussed. Examples of these are linear regression, nonparametric regression, kernel methods, regularization, cluster analysis, classification trees, neural networks, boosting, discrimination, support vector machines, and model selection. Applications are discussed as well as computation and theory. Same as CPSC See CPSC Theory and methods for analyzing univariate and multivariate spatial and spatio-temporal data.

Covers both fundamental theories and cutting-edge research advances for geostatistics, and statistical methods for aggregated data and point processes.

Real data examples will be provided in class and statistical software will be used to illustrate the data analysis. Prerequisite: STAT or equivalent. Trains students to analyze large complex data using advanced statistical learning methods and algorithms. The main topics in the course include: data exploration and interpretation in data science; large data processing; regularization methods; optimization tools; deep learning; recommender systems; network and graphical models; text mining; and imaging analyses.

Students will gain practical skills of data mining and knowledge discovery in various applications such as business, political science, biology and medicine.

See MATH Measures and probabilities; integration and expectation; convergence theorems and inequalities for integrals and expectations; independence; convergence in probability, almost surely, and mean; Three Series Theorem; laws of large numbers. Prerequisite: MATH or consent of instructor. Measure extensions, Lebesque-Stieltjes measure, Kolmogorov consistency theorem; conditional expectation, conditional probability, martingales; distribution functions and characteristic functions; convergence in distribution; Central Limit Theorem; Brownian Motion.

This is a graduate-level course on time series analysis, with an emphasis on nonlinear and multivariate time series. Topics include: linear time series, nonlinear time series, continuous-time models, multivariate and high-dimensional models.

Students will learn how to build adequate models, perform statistical estimation and inference, conduct prediction, and related topics. Students will also learn some basic mathematical tools such as Markov chains, martingales, stochastic calculus, concentration inequalities, etc.

Inference in multivariate statistical populations emphasizing the multivariate normal distribution; derivation of tests, estimates, and sampling distributions; and examples from the natural and social sciences.

Limiting distribution of maximum likelihood estimators, likelihood ratio test statistics, U-statistics, M-, L-, and R-estimators, nonparametric test statistics, Von Mises differentiable statistical functions; asymptotic relative efficiencies; asymptotic expansions. Same as ECON A graduate-level introduction to Empirical Process Theory with applications to statistical M estimation, nonparametric regression, and high dimensional statistics.

Empirical Process Theory deals with two fundamental questions: the uniform law of large numbers, and the uniform central limit theorems, both of which will be covered. This course provides rigorous training in empirical process for students with a strong background in mathematical statistics. Topics covered are useful for conducting modern theoretical research in statistics and probability. See EPSY See PSYC Directed reading and research. May be repeated with approval. Supervised, off-campus experience in a field in which statistical science plays an important role.

Prerequisite: STAT and consent of instructor. Prepares Ph. The course will focus on profession, job search, research, teaching and service. May be repeated. Web Privacy Notice.