If \\({\bf M}_{kk} ≠ 0\\), then:
- \\[
+ $$
\begin{align}
A_{kk} &= 1/M_{kk},\\\\
A_{ik} &= -M_{ik}/M_{kk} \text{ for } i ≠ k,\\\\
A_{kj} &= M_{kj}/M_{kk} \text{ for } j ≠ k,\\\\
A_{ij} &= M_{ij} - M_{ik}M_{kj}/M_{kk} \text{ for } i ≠ k \text{ and } j ≠ k.
\end{align}
- \\]
+ $$
If \\({\bf M}_{kk}\\) = 0, then:
- \\[
+ $$
\begin{align}
A_{ik} &= A_{ki} = 0, \\\\
A_{ij} &= M_{ij}, \text{ for } i ≠ k \text{ and } j ≠ k.
\end{align}
- \\]
+ $$
Given `M = {0, 1, 2; 3, 4, 5; 6, 7, 8}`, then (approximately):
count columns wide. Another format type may be substituted for
`F`.
-- `FORMAT=Fw`[`.d`] divides the input area into fixed-width fields
+- `FORMAT=Fw[.d]` divides the input area into fixed-width fields
with width `w`. The input area must be a multiple of `w` columns
wide. Another format type may be substituted for `F`. The
`READ` command disregards `d`.
Shapiro-Wilk test for each category. There are however a number of
provisos:
- All weight values must be integer.
-- The cumulative weight value must be in the range [3, 5000]
+- The cumulative weight value must be in the range \[3, 5000\].
The `COMPARE` subcommand is only relevant if producing boxplots, and
it is only useful there is more than one dependent variable and at least
Histograms are not created for string variables.
[^1]: The number of bins is chosen according to the Freedman-Diaconis
-rule: \\[2 \times IQR(x)n^{-1/3}\\] where \\(IQR(x)\\) is the
+rule: $$2 \times IQR(x)n^{-1/3}$$ where \\(IQR(x)\\) is the
interquartile range of \\(x\\) and \\(n\\) is the number of samples.
([`EXAMINE`](examine.md) uses a different algorithm to determine bin
sizes.)
procedure estimates.
By default, a constant term is included in the model. Hence, the
-full model is \\[{\bf y} = b_0 + b_1 {\bf x_1} + b_2 {\bf x_2} + \dots +
-b_n {\bf x_n}.\\]
+full model is $${\bf y} = b_0 + b_1 {\bf x_1} + b_2 {\bf x_2} + \dots +
+b_n {\bf x_n}.$$
Predictor variables which are categorical in nature should be listed
on the `/CATEGORICAL` subcommand. Simple variables as well as
procedure, and other parameters. The value of `CUT_POINT` is used in the
classification table. It is the threshold above which predicted values
are considered to be 1. Values of `CUT_POINT` must lie in the range
-[0,1]. During iterations, if any one of the stopping criteria are
+\[0,1\]. During iterations, if any one of the stopping criteria are
satisfied, the procedure is considered complete. The stopping criteria
are:
The Kendall test investigates whether an arbitrary number of related
samples come from the same population. It is identical to the
Friedman test except that the additional statistic W, Kendall's
-Coefficient of Concordance is printed. It has the range [0,1]—a value
+Coefficient of Concordance is printed. It has the range \[0,1\]—a value
of zero indicates no agreement between the samples whereas a value of
unity indicates complete agreement.