Load the sample data.

The column vector, `species`

consists of iris
flowers of three different species: setosa, versicolor, and virginica.
The double matrix `meas`

consists of four types of
measurements on the flowers: the length and width of sepals and petals
in centimeters, respectively.

Store the data in a table array.

Fit a repeated measures model, where the measurements
are the responses and the species is the predictor variable.

Compute group counts, mean, and standard deviation with
respect to species.

ans =
species GroupCount mean std
____________ __________ ______ ______
'setosa' 200 2.5355 1.8483
'versicolor' 200 3.573 1.7624
'virginica' 200 4.285 1.9154

Now, compute the range of data and 95% confidence intervals
for the group means for the factor species. Also display the group
name.

ans =
species gname GroupCount range predci
____________ ____________ __________ _____ ____________________
'setosa' 'setosa' 200 5.7 -1.1185 6.1895
'versicolor' 'versicolor' 200 6 0.088976 7.057
'virginica' 'virginica' 200 6.5 0.4985 8.0715

Load the sample data.

The table `between`

includes the between-subject
variables age, IQ, group, gender, and eight repeated measures *y*1
through *y*8 as responses. The table `within`

includes
the within-subject variables *w*1 and *w*2.
This is simulated data.

Fit a repeated measures model, where the repeated measures *y*1
through *y*8 are the responses, and age, IQ, group,
gender, and the group-gender interaction are the predictor variables.
Also specify the within-subject design matrix.

Compute group counts, mean, standard deviation, skewness,
and kurtosis of data grouped by the factors `Group`

and `Gender`

.

GS =
Group Gender GroupCount mean std skewness kurtosis
_____ ______ __________ _______ ______ ________ ________
A Female 40 16.554 21.498 0.35324 3.7807
A Male 40 9.8335 20.602 -0.38722 2.7834
B Female 40 11.261 25.779 -0.49177 4.1484
B Male 40 3.6078 24.646 0.55447 2.7966
C Female 40 -11.335 27.186 1.7499 6.1429
C Male 40 -14.028 31.984 1.7362 5.141