Linear Models

  • This site is a collection of data that contains the results of the Olympic track and field events. It contains the times not only of the winners, but of all the contestants.
    0
    No votes yet
  • JCommon is a collection of useful classes used by JFreeChart, JFreeReport and other projects. The library includes: text utilities, user interface classes for displaying information about applications, custom layout managers, a date chooser panel, serialization utilities, and XML parser support classes.

    0
    No votes yet
  • JFreeDesigner is a graphical report definition designer for JFreeReport. It allows the user to edit the xml source code of a report and to use a graphical WYSIWYG-editor to define the layout of the elements. This designer is currently in the early alpha state. At the moment most of the more advanced functionality is missing, but the current state allows you to edit the elements of a report.

    0
    No votes yet
  • This text document is a detailed index of the Against All Odds video series. This detailed index allows instructors to quickly find stories that can be used in the classroom. The author also includes the his ratings of which video segments are useful in the classroom. The actual videos are viewable online and are also indexed in CAUSEweb.
    0
    No votes yet
  • The JCCKit is a small library and flexible framework for creating scientific charts and plots works on java platform. 

    0
    No votes yet
  • JOpenChart is a free Java Toolkit and library for embedding charts into different kinds of applications, no matter if they are server side, desktop or web applications.

    0
    No votes yet
  • This activity is an advanced version of the "Keep your eyes on the ball" activity by Bereska, et al. (1999). Students should gain experience with differentiating between independent and dependent variables, using linear regression to describe the relationship between these variables, and drawing inference about the parameters of the population regression line. Each group of students collects data on the rebound heights of a ball dropped multiple times from each of several different heights. By plotting the data, students quickly recognize the linear relationship. After obtaining the least squares estimate of the population regression line, students can set confidence intervals or test hypotheses on the parameters. Predictions of rebound length can be made for new values of the drop height as well. Data from different groups can be used to test for equality of the intercepts and slopes. By focusing on a particular drop height and multiple types of balls, one can also introduce the concept of analysis of variance. Key words: Linear regression, independent variable, dependent variables, analysis of variance

    0
    No votes yet
  • Residual plots and other diagnostics are important to deciding whether or not linear regression is appropriate for a set of data. Many students might believe that if the correlation coefficient is strong enough, these diagnostic checks are not important. The data set included in this activity was created to lure students into a situation that looks on the surface to be appropriate for the use of linear regression but is instead based (loosely) on a quadratic function. Key words: regression, residuals
    0
    No votes yet
  • The Food and Drug Administration requires pharmaceutical companies to establish a shelf life for all new drug products through a stability analysis. This is done to ensure the quality of the drug taken by an individual is within established levels. The purpose of this out-of-class project or in-class example is to determine the shelf life of a new drug. This is done through using simple linear regression models and correctly interpreting confidence and prediction intervals. An Excel spreadsheet and SAS program are given to help perform the analysis. Key words: prediction interval, confidence interval, stability

    0
    No votes yet
  • This program has been written to explore the relationship between the data points and the error surface of the regression problem. On one hand you can learn how to represent a line in two different spaces ({x,y} and {k,d}), and on the other hand you see that solving the regression problem is nothing else than finding the minimum in the error surface.

    0
    No votes yet

Pages

register