| APPLET | SOURCE | DESCRIPTION |
|
| Detecting (Detective Testing for) Trend & Autocrrelation | A Collection of JavaScript E-labs Learning Objects (Arsham) | For series with up to 80 observations, calculates Runs and Durbin Watson statistic and gives conclusions for presence of trend and autocorrelation. more |
| Test for Stationary Time Series/Stationary Testing Process | A Collection of JavaScript E-labs Learning Objects (Arsham) | For series with up to 80 observations, calculates mean and variance for entire series, first half and second half, as well as one lag apart and two lag apart autocorrelation and provides conclusion about evidence for presence of trend or seasonality. more |
| Test for Seasonality of a Time Series | A Collection of JavaScript E-labs Learning Objects (Arsham) | Given seasonal indices, tests the hypothesis of no seasonality. more |
| Seasonal Index/Time-series Seasonal Index | A Collection of JavaScript E-labs Learning Objects (Arsham) | For up to 12 years (or weeks) of data, calculates seasonal indices. more |
| Classical Decomposition | Wessa.net | Calculates trend, seasonal and random components and displays several graphs including series and components for user-entered data. more |
| Time Series Forecasting | Interactive Statistics (Lind, Marchal, Wathen) | Shows how linear trend changes as users adjust or add points to the example series or for up to 20 observations of entered data. more |
| Regression Analysis Applet | Univ. of Saskatchewan, Dept. of Computer Science, Estimation and Forecasting Tutorials | Shows step-by-step computations for the regression line for user-entered data for a 6-period series or shows final calculations for default data. See also http://www.cs.usask.ca/content/resources/csconcepts/1999_6/Tutorial/index.html for a tutorial covering several Forecasting topics. more |