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3 years ago

25 POINT GIVEAWAY MONKE GANG

Mathematics

2 answers:

kobusy [5.1K]3 years ago

7 0

Answer:

thank you for the points.

german3 years ago

3 0

Answer:

Lol I love EDP memes but it dint hit the same I'll still support him tho but it's just gonna be weird or atleast if he posts again

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0.8% of 150 is what numbers and how do you write the equation with a variable in it.

Tanzania [10]

Answer:

1.2

Step-by-step explanation:

1. First, we have to write an equation with a variable! Given 0.8%, we know that it's equal to (0.8/100), and "of" means to multiply. Therefore, the equation will looks like this: $\frac{0.8}{100} * 150 = x$

2. Now, let's solve for x!

$\frac{0.8}{100} * 150 = (0.008)(150)$
$(0.008)(150) = \frac{6}{5}$
$\frac{6}{5} = 1\frac{1}{5}$
$1\frac{1}{5} = 1.2$
$1.2$

Therefore, 0.8% of 150 is 1.2!

6 0

3 years ago

How do I express 540 as a product of prime factors

aksik [14]

540=
54*10=
6*9*2*5=
2*3*3*3*2*5=
2*2*3*3*3*5 or in exponential form
(2²)(3³)(5)

6 0

4 years ago

Read 2 more answers

Consider the system:

PolarNik [594]

Answer:

An exponent value in a would make the system inconsistent because it will either gain or lose height over time.

Any real number without an exponent value will make the system consitent.

Any infintite/repeating number will make the system both consistent and inconsistent. it is consisent because it will stay at a constant rate but also inconsistent because it is repeating and will never end.

7 0

3 years ago

Read 2 more answers

All vectors are in Rn. Check the true statements below:

Oduvanchick [21]

Answer:

A), B) and D) are true

Step-by-step explanation:

A) We can prove it as follows:

$Proy_{cv}y=\frac{(y\cdot cv)}{||cv||^2}cv=\frac{c(y\cdot v)}{c^2||v||^2}cv=\frac{(y\cdot v)}{||v||^2}v=Proy_{v}y$

B) When you compute the product Ax, the i-th component is the matrix of the i-th column of A with x, denote this by Ai x. Then, we have that $||Ax||=\sqrt{(A_1 x)^2+\cdots (A_n x)^2}$ . Now, the colums of A are orthonormal so we have that (Ai x)^2=x_i^2. Then $||Ax||=\sqrt{(x_1)^2+\cdots (x_n)^2}=||x||$ .

C) Consider $S=\{(0,2),(2,0)\}\subseteq \mathbb{R}^2$ . This set is orthogonal because $(0,2)\cdot(2,0)=0(2)+2(0)=0$ , but S is not orthonormal because the norm of (0,2) is 2≠1.

D) Let A be an orthogonal matrix in $\mathbb{R}^n$ . Then the columns of A form an orthonormal set. We have that $A^{-1}=A^t$ . To see this, note than the component $b_{ij}$ of the product $A^t A$ is the dot product of the i-th row of $A^t$ and the jth row of $A$ . But the i-th row of $A^t$ is equal to the i-th column of $A$ . If i≠j, this product is equal to 0 (orthogonality) and if i=j this product is equal to 1 (the columns are unit vectors), then $A^t A=I$

E) Consider S={e_1,0}. S is orthogonal but is not linearly independent, because 0∈S.

In fact, every orthogonal set in R^n without zero vectors is linearly independent. Take a orthogonal set $\{u_1,u_2\cdots u_p\}$ and suppose that there are coefficients a_i such that $a_1u_1+a_2u_2\cdots a_nu_n=0$ . For any i, take the dot product with u_i in both sides of the equation. All product are zero except u_i·u_i=||u_i||. Then $a_i||u_i||=0$ then $a_i=0$ .