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Communicating With Data, Summer 2003
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CC BY-NC-SA
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Introduces students to the basic tools in using data to make informed management decisions. Covers introductory probability, decision analysis, basic statistics, regression, simulation, and linear and nonlinear optimization. Computer spreadsheet exercises and examples drawn from marketing, finance, operations management, and other management functions. Restricted to Sloan Fellows.

Subject:
Business
Finance
Marketing
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Carroll, John S.
Date Added:
01/01/2003
How to Process, Analyze and Visualize Data, January IAP 2012
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CC BY-NC-SA
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This course is an introduction to data cleaning, analysis and visualization. We will teach the basics of data analysis through concrete examples. You will learn how to take raw data, extract meaningful information, use statistical tools, and make visualizations. This was offered as a non-credit course during the Independent Activities Period (IAP), which is a special 4-week term at MIT that runs from the first week of January until the end of the month.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Adam Marcus
Eugene Wu
Date Added:
01/01/2012
Introductory Business Statistics
Unrestricted Use
CC BY
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The book "Introductory Business Statistics" by Thomas K. Tiemann explores the basic ideas behind statistics, such as populations, samples, the difference between data and information, and most importantly sampling distributions. The author covers topics including descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics. Using real-world examples throughout the text, the author hopes to help students understand how statistics works, not just how to "get the right number."

Subject:
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Thomas K. Tiemann
Date Added:
08/13/2020
Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition
Unrestricted Use
CC BY
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"Introductory Business Statistics with Interactive Spreadsheets - 1st Canadian Edition" is an adaptation of Thomas K. Tiemann's book, "Introductory Business Statistics". In addition to covering basics such as populations, samples, the difference between data and information, and sampling distributions, descriptive statistics and frequency distributions, normal and t-distributions, hypothesis testing, t-tests, f-tests, analysis of variance, non-parametric tests, and regression basics, the following information has been added: the chi-square test and categorical variables, null and alternative hypotheses for the test of independence, simple linear regression model, least squares method, coefficient of determination, confidence interval for the average of the dependent variable, and prediction interval for a specific value of the dependent variable. This new edition also allows readers to learn the basic and most commonly applied statistical techniques in business in an interactive way -- when using the web version -- through interactive Excel spreadsheets. All information has been revised to reflect Canadian content.

Subject:
Business
Mathematics
Statistics and Probability
Material Type:
Textbook
Provider:
BCcampus
Provider Set:
BCcampus Open Textbooks
Author:
Mohammad Mahbobi, Thompson Rivers University; Thomas K. Tiemann, Elon University
Date Added:
04/19/2016
Models, Data and Inference for Socio-Technical Systems, Spring 2007
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In this class, students use data and systems knowledge to build models of complex socio-technical systems for improved system design and decision-making. Students will enhance their model-building skills, through review and extension of functions of random variables, Poisson processes, and Markov processes; move from applied probability to statistics via Chi-squared t and f tests, derived as functions of random variables; and review classical statistics, hypothesis tests, regression, correlation and causation, simple data mining techniques, and Bayesian vs. classical statistics. A class project is required.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Frey, Daniel
Date Added:
01/01/2007
Networks for Learning: Regression and Classification, Spring 2001
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The course focuses on the problem of supervised learning within the framework of Statistical Learning Theory. It starts with a review of classical statistical techniques, including Regularization Theory in RKHS for multivariate function approximation from sparse data. Next, VC theory is discussed in detail and used to justify classification and regression techniques such as Regularization Networks and Support Vector Machines. Selected topics such as boosting, feature selection and multiclass classification will complete the theory part of the course. During the course we will examine applications of several learning techniques in areas such as computer vision, computer graphics, database search and time-series analysis and prediction. We will briefly discuss implications of learning theories for how the brain may learn from experience, focusing on the neurobiology of object recognition. We plan to emphasize hands-on applications and exercises, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Subject:
Business
Finance
Psychology
Social and Behavioral Sciences
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Poggio, Tomaso
Date Added:
01/01/2001
Presentation: 10.1 Simple Linear Regression
Unrestricted Use
CC BY
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In this section, you will learn the introductory concepts of a simple linear regression analysis including a discussion on the explanatory and response variables in the study. You will learn how to analyze the strength and predictability of regression models.

Subject:
Mathematics
Statistics and Probability
Material Type:
Lecture Notes
Lesson
Author:
Kim Massaro
Date Added:
05/11/2022
Presentation: 10.2 Regression Statistics and Test
Unrestricted Use
CC BY
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In this section, you will learn how to analyze the strength and predictability of regression models and how to perform a significance test to statistically see if a linear relationship exists between the explanatory and response variables.

Subject:
Mathematics
Statistics and Probability
Material Type:
Lecture Notes
Lesson
Author:
Kim Massaro
Date Added:
05/11/2022
Psychology
Unrestricted Use
CC BY
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Psychology is designed to meet scope and sequence requirements for the single-semester introduction to psychology course. The book offers a comprehensive treatment of core concepts, grounded in both classic studies and current and emerging research. The text also includes coverage of the DSM-5 in examinations of psychological disorders. Psychology incorporates discussions that reflect the diversity within the discipline, as well as the diversity of cultures and communities across the globe.Senior Contributing AuthorsRose M. Spielman, Formerly of Quinnipiac UniversityContributing AuthorsKathryn Dumper, Bainbridge State CollegeWilliam Jenkins, Mercer UniversityArlene Lacombe, Saint Joseph's UniversityMarilyn Lovett, Livingstone CollegeMarion Perlmutter, University of Michigan

Subject:
Psychology
Social and Behavioral Sciences
Material Type:
Full Course
Provider:
Rice University
Provider Set:
OpenStax College
Date Added:
08/12/2021
Psychology, Personality, Freud and the Psychodynamic Perspective
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CC BY-NC
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By the end of this section, you will be able to:Describe the assumptions of the psychodynamic perspective on personality developmentDefine and describe the nature and function of the id, ego, and superegoDefine and describe the defense mechanismsDefine and describe the psychosexual stages of personality development

Subject:
Psychology
Material Type:
Module
Author:
OER Librarian
Date Added:
08/12/2021
Quantitative Reasoning & Statistical Methods for Planners I, Spring 2009
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CC BY-NC-SA
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" This course develops logical, empirically based arguments using statistical techniques and analytic methods. Elementary statistics, probability, and other types of quantitative reasoning useful for description, estimation, comparison, and explanation are covered. Emphasis is on the use and limitations of analytical techniques in planning practice."

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Glenn, Ezra Haber
Date Added:
01/01/2009
Regional Energy-Environmental Economic Modeling, Spring 2007
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CC BY-NC-SA
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This subject is on regional energy-environmental modeling rather than on general energy-environmental policies, but the models should have some policy relevance. We will start with some discussion of green accounting issues; then, we will cover a variety of theoretical and empirical topics related to spatial energy demand and supply, energy forecasts, national and regional energy prices, and environmental implications of regional energy consumption and production. Where feasible, the topics will have a spatial dimension. This is a new seminar, so we expect students to contribute material to the set of readings and topics covered during the semester.

Subject:
Economics
Social and Behavioral Sciences
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Karen Polenske
Date Added:
01/01/2007
Statistical Learning Theory and Applications, Spring 2006
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CC BY-NC-SA
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This course focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. It develops basic tools such as Regularization including Support Vector Machines for regression and classification. It derives generalization bounds using both stability and VC theory. It also discusses topics such as boosting and feature selection and examines applications in several areas: Computer Vision, Computer Graphics, Text Classification and Bioinformatics. The final projects and hands-on applications and exercises are planned, paralleling the rapidly increasing practical uses of the techniques described in the subject.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Poggio, Tomaso
Date Added:
01/01/2006
Statistics for Applications, Spring 2015
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CC BY-NC-SA
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This course is a broad treatment of statistics, concentrating on specific statistical techniques used in science and industry. Topics include: hypothesis testing and estimation, confidence intervals, chi-square tests, nonparametric statistics, analysis of variance, regression, correlation, decision theory, and Bayesian statistics.

Subject:
Mathematics
Statistics and Probability
Material Type:
Full Course
Provider:
MIT
Provider Set:
MIT OpenCourseWare
Author:
Dr. Peter Kempthorne
Date Added:
01/01/2009