This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: linear regression, generalized linear models, link function, deviance, and modeling.
This presentation is a part of a series of lessons on the Analysis of Categorical Data. This lecture covers the following: linear regression, generalized linear models, link function, deviance, and modeling.
Includes detailed PowerPoints for 20 lectures for topics including generalized linear models, logistic regression, and random effects models.
This is a graduate level introduction to statistics including topics such as probabilty/sampling distributions, confidence intervals, hypothesis testing, ANOVA, and regression. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This course covers methodology, major software tools and applications in data mining. By introducing principal ideas in statistical learning, the course will help students to understand conceptual underpinnings of methods in data mining. It focuses more on usage of existing software packages (mainly in R) than developing the algorithms by the students. The topics include statistical learning; resampling methods; linear regression; variable selection; regression shrinkage; dimension reduction; non-linear methods; logistic regression, discriminant analysis; nearest-neighbors; decision trees; bagging; boosting; support vector machines; principal components analysis; clustering. Perfect for students and teachers wanting to learn/acquire materials for this topic.
Statistics is often taught as though the design of the data collection and the data cleaning have already been done in advance. However, as most practicing statisticians quickly learn, typically problems that arise at the analysis stage, could have been avoided if the experimenter had consulted a statistician before the experiment was done and the data were conducted. This course is created to provide an understanding of how experiments should be designed so that when the data are collected, these shortcomings are avoided. Perfect for students and teachers wanting to learn/acquire materials for this topic.
This graduate level course offers an introduction into regression analysis. A researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis. STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation and is perfect for both students and teachers of statistics courses.
The JCCKit is a small library and flexible framework for creating scientific charts and plots works on java platform.
JFreeReport is a free Java report library. It has the following features: full on-screen print preview; data obtained via Swing's TableModel interface (making it easy to print data directly from your application); XML-based report definitions; output to the screen, printer or various export formats (PDF, HTML, CSV, Excel, plain text); support for servlets (uses the JFreeReport extensions) complete source code included (subject to the GNU Lesser General Public Licence); extensive source code documentation.
JFreeChart is a free Java class library for generating charts, including: pie charts (2D and 3D); bar charts (regular and stacked, with an optional 3D effect); line and area charts; scatter plots and bubble charts; time series, high/low/open/close charts and candle stick charts; combination charts; Pareto charts; Gantt charts; wind plots, meter charts and symbol charts; wafer map charts;
EXCITE is a collection of teaching materials developed by the Centers for Disease Control and Prevention (CDC) to introduce students to public health and epidemiology. Students will learn about the scientific method of inquiry, basic biostatistics, and outbreak investigation. EXCITE adapts readily to team teaching across a variety of subjects, including mathematics, social studies, history, and physical education.