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Jameson Bell
Jameson Bell

Introduction To Statistics And Data Analysis : ...


This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. It presents descriptive, inductive and explorative statistical methods and guides the reader through the process of quantitative data analysis. In the experimental sciences and interdisciplinary research, data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained results and finally drawing the correct and appropriate conclusions from the results are vital.




Introduction to Statistics and Data Analysis : ...



Dr. Christian Heumann is a professor at the Ludwig-Maximilian-Universität Munich, where he teaches students in Bachelor and Master programs offered by the Department of Statistics, as well as undergraduate students in the Bachelor of Science programs in business administration and economics. His research interests include statistical modeling, computational statistics and all aspects of missing data.


Dr. Shalabh is a Professor at the Indian Institute of Technology Kanpur (India). He received his Ph.D. from the University of Lucknow (India) and completed his post-doctoral work at the University of Pittsburgh (USA) and University of Munich (Germany). He has over twenty years experience in teaching and research. His main research areas are linear models, regression analysis, econometrics, error-measurement models, missing data models and sampling theory.


This class covers applied statistical methodology from an analysis-of-data viewpoint. Topics covered include frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis is also explored.


Brenda Gunderson is a senior lecturer at the University of Michigan Department of Statistics in the College of Literature, Science, and the Arts. She is also a member of the UM MERLOT Community of Practice Committee, the Textbook Steering Committee, and a recipient of a 2011 Provost's Teaching Innovation Prize for infusing technology for guided continous learning in a large gateway course. Her research interests include statistical education, applied statistics, biopharmaceutical applications, and multivariate analysis. more...


The purpose of the course is to introduce the statistical methods that are critical in the performance analysis and selection of information systems and networks. It includes fundamental topics as well as applications: data analysis and representation; probability models; conditional probability and independence; reliability of systems and networks; binomial, Poisson, and geometric distributions; data relationships; correlation; inference with confidence; significance tests; network simulation and analysis; performance analysis of systems and networks.


COURSE GOALS: To provide basic understanding of probabilistic and statistical methods and the knowledge of the application of such methods to the evaluation and analysis of communication systems, information technology systems and networks, as well as application to other business models based on statistical data, including inference and hypothesis testing.


When a student completes this course, he/she should be able to:1. Perform basic analysis of data using statistical methods;2. Perform simple inference of system parameters from measured data;3. Perform simple performance analysis of network or switch models;4. Analyze the reliability of interconnected systems;5. Perform a significance test to verify assumptions based on measured data;6. Make decisions based on statistical data.


Different topics in statistics, probability and game theory will be discussed in the context of various current events and media stories. The seminar will emphasize the understanding of the complexity of the data analysis involved.


Data science combines mathematical and computational skills, together with statistical and ethical reasoning, to draw conclusions from data. Programming is introduced with an emphasis on data analysis. Probability and algorithms are developed as tools for formal statistical modeling and inference, and for exploratory analysis and visualization of data.


A one term course in applied statistical methodology from an analysis-of-data viewpoint: Frequency distributions; measures of location; mean, median, mode; measures of dispersion; variance; graphic presentation; elementary probability; populations and samples; sampling distributions; one sample univariate inference problems, and two sample problems; categorical data; regression and correlation; and analysis of variance. Use of computers in data analysis. (4 Credits)


This course is an introduction to statistical methods and data analysis at the honors level, targeting advanced undergraduate students who are interested in a challenging introductory course. Definition and summary of univariate and bivariate data, distributions, correlation, and associated visualization techniques; randomization in comparative studies and in survey sampling; basic probability calculus, including conditional probabilities, concept of random variable and their properties; sampling distributions and the central limit theorem; statistical inference, including hypothesis test, confidence intervals; one sample and two sample problems with binary and continuous data, including nonparametric procedures; analysis of variance; simple and bivariate regression; simple design of experiments; chi-square and rank-based tests for association and independence. (4 Credits)


Statistical concepts are increasingly integrated into Artificial Intelligence applications, which often draw on a large amount of data received, transmitted, and generated by computers or networks of computers. This course introduces students to statistics and machine learning techniques such as deep neural networks, with application to analyzing text and image data.


An intermediate course in applied statistics which assumes knowledge of STATS 206/250/280 level material. Covers a range of topics in modeling and analysis of data including: review of simple linear regression, two-sample problems, one-way analysis of variance; multiple linear regression, diagnostics and model selection; two-way analysis of variance, multiple comparison, and other selected topics. (4 Credits)


This course introduces methods for planning, executing, and evaluating research studies based on experiments, surveys, and observational datasets. In addition to learning a toolset of methods, students will read and report on recent research papers to learn how study design and data analysis are handled in different fields. (4 Credits)


An introduction to probability theory; statistical models, especially sampling models; estimation and confidence intervals; testing statistical hypotheses; and important applications, including the analysis of variance and regression. (3 Credits)


This course covers the principles of data mining, exploratory analysis and visualization of complex data sets, and predictive modeling. The presentation balances statistical concepts (such as over-fitting data, and interpreting results) and computational issues. Students are exposed to algorithms, computations, and hands-on data analysis in the weekly discussion sessions. (4 Credits)


An introduction to theoretical statistics for students with a background in probability. Probability models for experimental and observational data, normal sampling theory, likelihood-based and Bayesian approaches to point estimation, confidence intervals, tests of hypotheses, and an introduction to regression and the analysis of variance. (3 Credits)


Review of probability theory; introduction to random walks; counting and Poisson processes; Markov chains in discrete and continuous time; equations for stationary distribution introduction to Brownian motion. Selected applications such as branching processes, financial modeling, genetic models, the inspection paradox, inventory and queuing problems, prediction, and/or risk analysis. (3 Credits)


Introduction to biostatistical topics: clinical trials, cohort and case-control studies; experimental versus observational data; issues of causation, randomization, placebos; case control studies; survival analysis; diagnostic testing; image analysis of PET and MRI scans; statistical genetics; longitudinal studies; and missing data. (3 Credits)


The course is an introduction to both principles and practice of Bayesian inference for data analysis. At the end of this course students will be familiar with the Bayesian paradigm, and will be able to analyze different classes of statistical models. The course gives an introduction to the computational tools needed for Bayesian data analysis and developes statistical modeling skills through a hands-on data analysis approach. Topics include: prior/posterior distributions, Bayes rule, Markov Chain Monte Carlo computations, linear and generalized linear models, mixed effect models, hierarchical models, analysis of spatial data, model selection and comparison, model checking. (3 credits)


Exploratory data analysis, error propagation, probability theory and statistics, curve fitting, regression, sequence and spectral analysis, multivariate analysis, and analysis of directional data. Pre: 250 and MATH 242 (or concurrent) or consent.


PSYC 9A - Introduction to Statistics and Data Analysis for the Behavioral Sciences4 units3 hours lecture, 3 hours labPrerequisite: PSYC 5 or PSYC 5H or SOCI 101 and MATH 73 or MATH 80 with a minimum grade of C in prerequisiteCredit, degree applicableTransfer CSU, UC*Students are taught standard descriptive and inferential statistics for summarizing sample data and estimating population parameters. All aspects of significance testing are emphasized: hypotheses, models, calculations, interpretations, and criticisms. Students are also taught to review scientific articles critically and to write APA-style manuscripts.Note: Psychology 9A is the same course as SOCI 109A . 041b061a72


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