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Keywords: anova
(Complete Item Description)
- Abstract: Introduces statistical data analysis, concentrating on techniques used in management science and finance. Topics chosen from: statistical graphics, basics of sampling, estimation, hypothesis testing, linear and logistic regression, analysis of variance, contingency tables, forecasting, statistical quality control, principal components, and factor analysis. SAS or similar package used for data analysis. This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material.
- Subject:
Business, Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
MIT OpenCourseWare
(Complete Item Description)
- Abstract: Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. The textbook is also available in printed form from Qoop.com.
- Subject:
Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This is a custom collection (by R. Bloom) of homework and review problems to accompany Collaborative Statistics textbook custom collection by R. Bloom. Content is derived from Collaborative Statistics written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook by S. Dean and B. Illowsky was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. This custom version of their collection has been modified by R. Bloom for her classes at De Anza College.
- Subject:
Mathematics and Statistics
- Grade Level:
Secondary, Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: Collaborative Statistics was written by Barbara Illowsky and Susan Dean, faculty members at De Anza College in Cupertino, California. The textbook was developed over several years and has been used in regular and honors-level classroom settings and in distance learning classes. This textbook is intended for introductory statistics courses being taken by students at two– and four–year colleges who are majoring in fields other than math or engineering. Intermediate algebra is the only prerequisite. The book focuses on applications of statistical knowledge rather than the theory behind it. This custom textbook collection has been modified by R. Bloom for her classes at De Anza College; the homework content for the custom collection is now contained in a separate homework collection.
- Subject:
Mathematics and Statistics
- Grade Level:
Secondary, Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This module provides a solution sheet for use with the F Distribution and ANOVA chapter of the Collaborative Statistics textbook/collection.
- Subject:
Mathematics and Statistics
- Grade Level:
Secondary, Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This module states the factors associated with F Distributions and provides students with some examples to help further understand the concept. Students will be given the opportunity to see F Distributions in action through participation in an optional classroom exercise.
- Subject:
Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This module provides a brief introduction on handling hypothesis tests with two means by the one-way Analysis of Variance (ANOVA), F Distribution, and the Test of Two Variances statistical analysis.
- Subject:
Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This module describes the assumptions needed for implementing an ANOVA and how to set up the hypothesis test for the ANOVA.
- Subject:
Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This module provides the assumptions to be considered in order to calculate a Test of Two Variances and how to execute the Test of Two Variances. An example is provided to help clarify the concept.
- Subject:
Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: This module describes how to calculate the F Ratio and F Distribution based on the hypothesis test for the ANOVA.
- Subject:
Mathematics and Statistics
- Grade Level:
Post-secondary
- Collection:
Connexions
(Complete Item Description)
- Abstract: Descriptive and inferential statistics for the behavioral and neurological sciences are considered. Techniques such as t-tests, factorial analysis of (co)variance, correlation, multiple regression, and nonparametric tests are introduced. Subject provides an introductory overview of some advanced methods such as path analysis, factor analysis, discriminant analysis, and analysis of functional MRI data. Basic issues of research design and methodology intimately associated with data analysis are discussed.
- Subject:
Science and Technology
- Grade Level:
Post-secondary
- Collection:
MIT OpenCourseWare
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