Introduction to business statistics pdf download

Enter your website URL optional. Search this website Type then hit enter to search. Share via. Copy Link. Powered by Social Snap. Copy link. Copy Copied. International Business Management Question Paper. This course covers fundamentals and axioms; combinatorial probability; conditional probability and independence; binomial, Poisson, and normal distributions; the law of large numbers and the central limit theorem; and random variables and generating functions.

Or instructor consent. Introduction to Mathematical Probability-A. This course covers combinatorics; basic notions of probability and conditional probability; independence; expectation, variance, and covariance; discrete and continuous random variables, including distributions such as binomial, normal, multinomial, geometric, hypergeometric, negative binomial, and Poisson; Gambler's Ruin; generating functions and applications to branching processes; the Weak Law of Large Numbers and its application to approximation by polynomials, i.

Introduction to Random Matrices. The course is an introduction to the random matrix theory. We will study the asymptotic properties of various random matrix models Wigner matrices, Gaussian ensembles, etc. We will also discuss some applications to statistics and neural networks.

Introduction to Probability Models. This course introduces stochastic processes as models for a variety of phenomena in the physical and biological sciences.

Following a brief review of basic concepts in probability, we introduce stochastic processes that are popular in applications in sciences e. This course considers the modeling and analysis of data that are ordered in time. The main focus is on quantitative observations taken at evenly spaced intervals and includes both time-domain and spectral approaches. Instructor s : W. Some previous exposure to Fourier series is helpful but not required.

Introduction to Statistical Genetics. As a result of technological advances over the past few decades, there is a tremendous wealth of genetic data currently being collected. These data have the potential to shed light on the genetic factors influencing traits and diseases, as well as on questions of ancestry and population history.

The aim of this course is to develop a thorough understanding of probabilistic models and statistical theory and methods underlying analysis of genetic data, focusing on problems in complex trait mapping, with some coverage of population genetics. Although the case studies are all in the area of statistical genetics, the statistical inference topics, which will include likelihood-based inference, linear mixed models, and restricted maximum likelihood, among others, are widely applicable to other areas.

No biological background is needed, but a strong foundation in linear algebra, as well as probability and statistics at the level of STAT STAT or higher is assumed. McPeek Terms Offered: Not offered in This course covers topics in the history of statistics, from the eleventh century to the middle of the twentieth century. We focus on the period from to , with an emphasis on the mathematical developments in the theory of probability and how they came to be used in the sciences.

Our goals are both to quantify uncertainty in observational data and to develop a conceptual framework for scientific theories. This course includes broad views of the development of the subject and closer looks at specific people and investigations, including reanalyses of historical data.

Instructor s : S. Stigler Terms Offered: Not offered in Nonparametric inference is about developing statistical methods and models that make weak assumptions.

A typical nonparametric approach estimates a nonlinear function from an infinite dimensional space rather than a linear model from a finite dimensional space. This course gives an introduction to nonparametric inference, with a focus on density estimation, regression, confidence sets, orthogonal functions, random processes, and kernels. The course treats nonparametric methodology and its use, together with theory that explains the statistical properties of the methods.

Master's students in Statistics can enroll without prerequisites. Introduction to Bayesian Data Analysis. In recent years, Bayes and empirical Bayes EB methods have continued to increase in popularity and impact. These methods combine information from similar and independent experiments and yield improved estimation of both individual and shared model characteristics. In this course, we introduce Bayes and EB methods, as well as the necessary tools needed to evaluate their performances relative to traditional, frequentist methods.

We shall focus on more practical, data analytic and computing issues. Various computing methods will be discussed, in order to find the posterior distributions, including Markov chain Monte Carlo methods such as the Gibbs sampler. We will use R to implement these methods to solve real world problems. The methods will be illustrated from applications in various areas, such as biological science, biomedical science, public health, epidemiology, education, social science, economics, psychology, agriculture and engineering.

Recent developments of Bayesian methods on nonlinear models, longitudinal data analysis, hierarchical models, time series, survival analysis, spatial statistics will also be explored. Introduction to Causality with Machine Learning. This course is an introduction to causal inference. We'll cover the core ideas of causal inference and what distinguishes it from traditional observational modeling. This includes an introduction to some foundational ideasstructural equation models, causal directed acyclic graphs, and then do calculus.

The course has a particular emphasis on the estimation of causal effects using machine learning methods. Instructor s : V. Mathematical Foundations of Machine Learning. This course is an introduction to the mathematical foundations of machine learning that focuses on matrix methods and features real-world applications ranging from classification and clustering to denoising and data analysis.

Mathematical topics covered include linear equations, regression, regularization, the singular value decomposition, and iterative algorithms. Machine learning topics include the lasso, support vector machines, kernel methods, clustering, dictionary learning, neural networks, and deep learning. Students are expected to have taken calculus and have exposure to numerical computing e. Matlab, Python, Julia, R. This course introduces the foundations of machine learning and provides a systematic view of a range of machine learning algorithms.

Topics covered include two parts: 1 a gentle introduction of machine learning: generalization and model selection, regression and classification, kernels, neural networks, clustering and dimensionality reduction; 2 a statistical perspective of machine learning, where we will dive into several probabilistic supervised and unsupervised models, including logistic regression, Gaussian mixture models, and generative adversarial networks.

Multiple Testing, Modern Inference, and Replicability. This course examines the problems of multiple testing and statistical inference from a modern point of view. High-dimensional data is now common in many applications across the biological, physical, and social sciences. With this increased capacity to generate and analyze data, classical statistical methods may no longer ensure the reliability or replicability of scientific discoveries.

We will examine a range of modern methods that provide statistical inference tools in the context of modern large-scale data analysis. The course will have weekly assignments as well as a final project, both of which will include both theoretical and computational components. Familiarity with regression and with coding in R are recommended. This is an introductory course on optimization that will cover the rudiments of unconstrained and constrained optimization of a real-valued multivariate function.

The focus is on the settings where this function is, respectively, linear, quadratic, convex, or differentiable. Time permitting, topics such as nonsmooth, integer, vector, and dynamic optimization may be briefly addressed.

Materials will include basic duality theory, optimality conditions, and intractability results, as well as algorithms and applications. Dynamical Systems with Applications. This course is concerned with the analysis of nonlinear dynamical systems arising in the context of mathematical modeling. The focus is on qualitative analysis of solutions as trajectories in phase space, including the role of invariant manifolds as organizers of behavior.

Local and global bifurcations, which occur as system parameters change, will be highlighted, along with other dimension reduction methods that arise when there is a natural time-scale separation. Concepts of bi-stability, spontaneous oscillations, and chaotic dynamics will be explored through investigation of conceptual mathematical models arising in the physical and biological sciences. Previous knowledge of elementary differential equations is helpful but not required. This course consists of reading and research in an area of statistics or probability under the guidance of a faculty member in the Department of Statistics.

Open to all students, including non-majors. This course consists of reading and research in an area of statistics or probability under the guidance of a faculty member in the Department of Statistics, leading to a bachelor's paper. A good draft of the paper must be submitted by the first day of exam period. Open only to students who are majoring in Statistics. Yibi Huang Jones Undergraduate Program Chair Prof. Mary Sara McPeek Jones The University of Chicago.

Statistics Toggle Navigation. Send to friends and colleagues. Modify, remix, and reuse just remember to cite OCW as the source. Lecture Notes. PDF IX. PDF 23 Hypothesis tests cont. Need help getting started? Don't show me this again Welcome! Probability distributions and random variables. Sets and events PDF. A key No Data Available. Filter By. My Topics No Response. Clear Close Apply.

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