tw1d | teachware quantlet tw1d shows a histogram of user defined data and offers interactive visual analysis of this data by means of box plots (for mean and median) and QQ-plots. Transformations may be applied to the data in order to study the change in distribution and and box plots. |
twaremain | loads necessary quantlibs in order to execute the teachware tware.lib. |
twaretest | Executes some tests for the quantlets defined in the teachware tware.lib. |
twboxcox | allows to find interactively the best parameter for your data for a Box-Cox transformation. |
twboxcoxintroduction | generates the introductory text for twboxcox |
twboxcoxloop | main loop for twboxcox |
twclt | teachware quantlet twclt shows a discrete four point distribution and simulates repeated sampling from this apparently non normal distribution. The variation of the observed mean values around the true mean value (standardized by scale) is shown in a plot. The user may interactively change the number of samples and thereby study the effect of the central limit theorem (CLT). |
twlinreg | teachware quantlet twlinreg gives visual insight into how least squares simple linear regression works, and the relationship between the regression of Y on X, X on Y, and total regression. The data are bivariate Gaussian, and a menu allows control of the number of data points, and the correlation.Intuitive understanding of least squares fitting is conveyed through interactive manipulation of a candidate fit line. A menu gives control over this process, through incremental adjustments that are selected by check boxes, followed by a push of the "OK" button. The main graphics window shows the data scatterplot, together with the least squares fit line. A text component shows the equation of the current line (which changes as the line is manipulated), together with the Residual Sum of Squares which gives a numerical summary of the goodness of fit. Very effective visual indication of what RSS means comes from the lower graphics part of this window, which represents the residuals as vertical lines. When the fit is poor (and hence the RSS is large), the residual plot shows why, and give a clear indication of how the line should be moved to improve the quality of the fit to the data. |
twnormalize | teachware quantlet twnormalize shows the distribution of binomials B(n1, p), B(n2, p) and B(n3, p) with increasing n1, n2, n3. One may shift the distribution by the mean value and divide by the standard deviation in order to study the normalizing effect. In addition a normal density may be graphically overlaid. |
twpearson | teachware quantlet twpearson gives a visual demonstration of the form of the Pearson correlation coefficient. In particular, it shows why the product moment gives a measure of "dependence", and why it is essential to "normalize", i.e. to subtract means, and divide by standard deviations, to preserve that property. |
twpvalue | teachware quantlet twpvalue computes the p-value of a B(n, p) distribution |
twrandomsample | teachware quantlet twrandomsample asks for a distribution of the numbers {1, 2, 3, 4} displays a bar chart of the entered values and calculates a test for H0: p{2,3} = 0.5, the hypothesis of uniform distribution. |