Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

table2 - timestest - trian - twclt - twlesson - twtest
table2 computes a two way table from two-dimensional data.
tableN tableN returns a N way table for N-dimensional data.
tabular creates different tables to show the coefficients and their T-tests computed in the macro "relations".
taills Estimates the tail index of fat-tailed distributions
tan Returns the tangent in radian of the elements of an array.
tanh Returns the hyperbolic tangent of the elements of an array.

tdiff a difference operator for time series allow multiple differences and seaonal difference
tgarsim tgarsim is plotting the difference between option prices by Black/Scholes and using risk neutral, GARCH or Treshold GARCH models
timeplot plots a time series in multiple windows with user-specified maximum length per window
timesmain loads the libraries needed for the macros in times
timestest executes some tests for the macros defined in times.lib
tobit 2-step estimation of a Tobit model
tourasimov Computes a rotation matrix based on the paper by Asimov (1985).
tourlittle Computes a little tour rotation matrix.
tourrandom Computes a random rotation matrix.
tramo
trans trans transposes matrices. This function is equal to the operator '
transform Transforms the given dataset.
tree generates from a binary tree an output for plotting.

tri tri computes the triweight kernel, multivariate
trian trian computes the triangle kernel, multivariate
trimper trims a given percentage of a (binned) data matrix
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 box plots.
twaremain loads necessary quantlets in order to execute the teachware tware.lib.
twaretest Executes some tests for the quantlets defined in the teachware tware.lib.
twavefig
twavemain Starts the twave lesson when library("twave") is called and generates the global constant twavec which allows to jump immediately to a single task.
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).
twles1 Shows the functions approximation by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
twles2 Compares the data compression of wavelets with fourier basis. You can choose between different wavelet base, different number of father wavelet coefficients, different functions and different views to the mother wavelet coefficients.
twles3 Compares the approximation of sines with different frequencies by wavelets. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
twles4 Shows the approximation of a sine function which changes its frequency. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
twles5 Shows how a hard threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, hard threshold by hand and automatically.
twles6 Shows how a soft threshold behaves on the true function and the true function plus noise. You can choose between different wavelet base, different number of father wavelet coefficients, different functions different views to the mother wavelet coefficients, soft threshold by hand and automatically.
twles7 Shows how a hard threshold behaves on an image and an image plus noise. You can choose between different wavelet base, different number of father wavelet coefficients and different views to the mother wavelet coefficients.
twles8 Shows the father and mother wavelet for a given basis. You can choose between different wavelet base.
twles9 Shows in the left window the true function plus noise and in the right a translation invariant estimator with k=4*log_2(n) shifts.
twlesson Starts the twave lessons either interactively or a specific lesson.
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.
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.
twprint
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.
twskew teachware quantlet shows effects on skewness and kurtosis by contamination of a normal distribution
twtest teachware quantlet shows error type I and II in testing simple hypotheses

Keywords - Function groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 21.9.2000