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table2 |
computes a two way table from two-dimensional data.
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tableN |
tableN returns a N way table for N-dimensional data.
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tabular |
creates different tables to show the coefficients and their
T-tests computed in the macro "relations".
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taills |
Estimates the tail index of fat-tailed distributions
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tan |
Returns the tangent in radian of the elements of an array.
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tanh |
Returns the hyperbolic tangent of the elements of an array.
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tdiff |
a difference operator for time series
allow multiple differences and seaonal
difference
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tgarsim |
tgarsim is plotting the difference between
option prices by Black/Scholes and using
risk neutral, GARCH or Treshold GARCH models
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timeplot |
plots a time series in multiple windows with
user-specified maximum length per window
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timesmain |
loads the libraries needed for the macros in times
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timestest |
executes some tests for the macros defined in times.lib
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tobit |
2-step estimation of a Tobit model
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tourasimov |
Computes a rotation matrix based on the paper by
Asimov (1985).
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tourlittle |
Computes a little tour rotation matrix.
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tourrandom |
Computes a random rotation matrix.
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tramo |
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trans |
trans transposes matrices. This function is equal to the operator '
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transform |
Transforms the given dataset.
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tree |
generates from a binary tree an output for plotting.
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tri |
tri computes the triweight kernel, multivariate
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trian |
trian computes the triangle kernel, multivariate
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trimper |
trims a given percentage of a (binned) data matrix
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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.
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twaremain |
loads necessary quantlets in order to execute
the teachware tware.lib.
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twaretest |
Executes some tests for the quantlets defined
in the teachware tware.lib.
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twavefig |
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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.
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twboxcox |
allows to find interactively the best parameter for your
data for a Box-Cox transformation.
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twboxcoxintroduction |
generates the introductory text for twboxcox
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twboxcoxloop |
main loop for twboxcox
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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).
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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twles8 |
Shows the father and mother wavelet for a given basis.
You can choose between different wavelet base.
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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.
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twlesson |
Starts the twave lessons either interactively
or a specific lesson.
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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.
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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.
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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.
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twprint |
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twpvalue |
teachware quantlet twpvalue computes the p-value
of a B(n, p) distribution
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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.
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twskew |
teachware quantlet
shows effects on skewness and kurtosis by contamination of a normal distribution
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twtest |
teachware quantlet
shows error type I and II in testing simple hypotheses
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