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sergij07 [2.7K]
2 years ago
14

Which of the following expressions is not equivalent to -5?

Mathematics
2 answers:
Elena L [17]2 years ago
5 0

Answer:

-5/25

Step-by-step explanation:

25/(-5)=-5

-35/7=-5

-5/25=-1/5

35/(-7)=-5

hope I helped

slava [35]2 years ago
5 0

Answer:

-5÷25

Step-by-step explanation:

-5÷25 would equal -0.2

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Sean earned $31,000 last year. if the first $25,000 is taxed at 9% and income above is taxed at 15%, how much does sean owe in t
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$3150 because 9% of $25000 is $2250 and 15% of $6000 is 900 and 2250+ 900 is 3150
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Landon scored an 85% on his test. If he got 34 problems correct, how many were on the test?
Sergeu [11.5K]

Answer:

There are 40 problems on the test

Step-by-step explanation:

Let p = the number of problems on the test

The number of problems on the test times the percent correct is the number correct

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6 0
2 years ago
The answers for number 5
VladimirAG [237]
The answer is c because of y=mx+b

5 0
2 years ago
Help...me...plz..!!.....?
Montano1993 [528]

Answer:

\huge\boxed{-1}

Step-by-step explanation:

\frac{-5^2}{(-5)^2}

=> \frac{-25}{25}

=> -1

8 0
3 years ago
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on the asymptotic behavior of the sample estimates of eigenvalues and eigenvectors of covariance matrices
skelet666 [1.2K]

A complex mathematical topic, the asymptotic behavior of sequences of random variables, or the behavior of indefinitely long sequences of random variables, has significant ramifications for the statistical analysis of data from large samples.

The asymptotic behavior of the sample estimators of the eigenvalues and eigenvectors of covariance matrices is examined in this claim. This work focuses on limited sample size scenarios where the number of accessible observations is comparable in magnitude to the observation dimension rather than usual high sample-size asymptotic .

Under the presumption that both the sample size and the observation dimension go to infinity while their quotient converges to a positive value, the asymptotic behavior of the conventional sample estimates is examined using methods from random matrix theory.

Closed form asymptotic expressions of these estimators are obtained, demonstrating the inconsistency of the conventional sample estimators in these asymptotic conditions, assuming that an asymptotic eigenvalue splitting condition is satisfied.

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