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3 years ago

Factorise 3+81x³y³ step by step explanation

Mathematics

1 answer:

Delicious77 [7]3 years ago

7 0

Answer:

3(1+27x^3,y^3)

Step-by-step explanation:

3 is a GCF between the two terms, remove it and any other GCFs, in this case the term 3 doesn’t have any x’s or y’s. the only thing that can be removed from this equation is 3.

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Ill mark brainliest !!

Solnce55 [7]

Answer:

The answer is true.

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3 years ago

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Can someone explain how to get the answer if do 5 stars and have good rating

8_murik_8 [283]

Answer:

3:1

Step-by-step explanation:

We know that the number of students who prefer cheese AND vegetable pizza is 16 cheese + 2 vegetable = 18 combined

We also know that the number of students who prefer sausage AND pepperoni pizza is 3 sausage + 3 pepperoni = 6 combined

If we take the ratio between these figures, this ratio would be 18:6 (read as 18 to 6) which simplifies to 3:1 (read as 3 to 1).

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2 years ago

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A basket contains 20 green apples and 30 red apples. If a piece of fruit is selected at random from the basket, what is the prob

Dmitrij [34]

Answer:

40%

Step-by-step explanation:

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3 years ago

Lexy and Lonnel applied to the same university. They looked up the average SAT score of students admitted to that university. Le

Tomtit [17]

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Answer:

(a) ❘-270 - 30❘

Step-by-step explanation:

One score is -270 and the other is +30. The difference is either of ...

|30 -(-270)|

|-270 -30| . . . . . . matches choice A

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3 years ago

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 $a_i=0$ .

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3 years ago

Factorise 3+81x³y³ step by step explanation​

Factorise 3+81x³y³ step by step explanation