Break down 46+18 in tens

Show that if A is invertible, then det Upper A Superscript negative 1det A−1equals=StartFraction 1 Over det Upper A EndFraction

netineya [11]

Answer:

Therefore

$det(A)*det(A^{-1}) = 1$ and $det(A^{-1}) = 1/det(A)$

Step-by-step explanation:

Remember that if

$I$ = Identity Matrix

then $det(I)=1$ .

Also remember that for any invertible matrix

$A*A^{-1} = I =$ Identity Matrix.

Therefore

$det(A*A^{-1}) = det(A)*det(A^{-1}) = det(I) = 1$

Therefore

$det(A)*det(A^{-1}) = 1$ and $det(A^{-1}) = 1/det(A)$

7 0

3 years ago

What's the square root of 5

QveST [7]

The answer is 25 because
5*5=25

5 0

3 years ago

Read 2 more answers

Emil is itemizing deductions on his federal income tax return and had $1900 in non-reimbursed work expenses last year. If his AG

a_sh-v [17]

Thank you for posting your question here at brainly. I hope the answer will help you. Feel free to ask more questions.

Below are the choices that can be found from other source:

A.$340
B.$1560 
C.$38 
D.$1862

Below is the solution:

78000 x .0.02=1560
1900 - 1560=340

5 0

4 years ago

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If a random sample of size nequals=6 is taken from a population, what is required in order to say that the sampling distributio

goldenfox [79]

Answer:

$\bar X \sim N(\mu , \frac{\sigma}{\sqrt{n}})$

In order to satisfy this distribution we need that each observation on this case comes from a normal distribution, because since the sample size is not large enough we can't apply the central limit theorem.

Step-by-step explanation:

For this case we have that the sample size is n =6

The sample man is defined as :

$\bar X = \frac{\sum_{i=1}^n X_i}{n}$

And we want a normal distribution for the sample mean

$\bar X \sim N(\mu , \frac{\sigma}{\sqrt{n}})$

In order to satisfy this distribution we need that each observation on this case comes from a normal distribution, because since the sample size is not large enough we can't apply the central limit theorem.

So for this case we need to satisfy the following condition:

$X_i \sim N(\mu , \sigma), i=1,2,...,n$