MHB Probability that a matrix is singular

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The discussion focuses on calculating the probability that a randomly formed $3 \times 3$ matrix, using elements from the set {-1, 1}, is singular. There are a total of $2^9$ possible matrices, and the approach involves counting the nonsingular matrices. By restricting the row vectors to unique representatives from pairs of vectors and considering combinations, the number of nonsingular matrices is determined to be $\binom{4}{3} \; 3! \; 2^3$. Consequently, the probability of a matrix being singular is given by the formula: 1 - $\frac{\binom{4}{3} \; 3! \; 2^3}{2^9}$. This calculation highlights the relationship between matrix formation and singularity probability in linear algebra.
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A $3 \times 3$ matrices are formed using the the elements of $\left\{-1,1\right\}$. Then the probability that it is Singular, is
 
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Re: probability

jacks said:
A $3 \times 3$ matrices are formed using the the elements of $\left\{-1,1\right\}$. Then the probability that it is Singular, is
There are $2^9$ matrices in all, which we are assume are equally likely. We would like to count those which are nonsingular.

To that end, note that there are $2^3$ possible row vectors. Each row vector $v$ has a negative $-v$ in the set of possible row vectors. Since a basis cannot include both a vector and its negative, let's restrict our attention to one vector $v$ from each of the pairs $v$ and $-v$. This leaves us with $(1/2) \; 2^3 = 4$ row vectors to consider. For example, we might choose the set $E = \{(1,1,1), (1,1,-1), (1,-1,1), (-1,1,1)\}$. Since the order of the row vectors does not affect the (non)singularity of a matrix, let's consider just the $\binom{4}{3} = 4$ subsets of size 3 taken from $E$. It's easy to check that each of the 4 3 by 3 matrices thus produced is nonsingular.

Taking into account the possible orderings of the three row vectors in a matrix, we must multiply by $3!$; and taking into account that each vector could be replaced by its negative, we must multiply by $2^3$. So all together, there are
$\binom{4}{3} \; 3! \; 2^3$
nonsingular matrices whose elements are -1 or 1.

So the probability that such a matrix is singular is

$$1 - \frac{\binom{4}{3} \; 3! \; 2^3}{2^9}$$.
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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