All categories

3 years ago

Find the missing angles in the figure below.

Mathematics

2 answers:

vivado [14]3 years ago

8 0

Answer:

First option is right.

Step-by-step explanation:

w=78 (vertically opposite angles)

l=82 (vertically opposite angles)

M= 180-64=116 (linear pair)

sorry im not sure how to find y and z

SashulF [63]3 years ago

7 0

Answer: The answer is A. Just check the only answer with y=z.

You might be interested in

Simply

Digiron [165]

Answer:

A.6^1/12

Step-by-step explanation:

3√6/4√6

when dividing the same base, subtract the powers.

(6^1/3)/(6^1/4)

6^(1/3-1/4)

6^(4-3)/12

6^1/12

option A is collect

5 0

3 years ago

Here are two column vectors

Contact [7]

Answer:

$a = ( \frac{ - 6}{2} )$

Step-by-step explanation:

-2g=?

$- 2( \frac{3 }{ - 1} ) \\ \\ ( \frac{ - 6}{2} )$

8 0

3 years ago

-3x+4=-38 ANWSER QUICK

jolli1 [7]

Answer:

hkhkhkkhhkkhhkhkhhkhkhkhhkkhkhkhhkhkhk777777

Step-by-step explanation:

8 0

3 years ago

Suppose u1, u2, ..., un are independent random variables and for every i = 1, ..., n, ui has a uniform distribution over [0, 1].

sattari [20]

$Z=U_{(1)}=\min\{U_1,\ldots,U_n\}$

has CDF

$F_Z(z)=1-(1-F_{U_i}(z))^n$

where $F_{U_i}(u_i)$ is the CDF of $U_i$ . Since $U_i$ are iid. with the standard uniform distribution, we have

$F_{U_i}(u_i)=\begin{cases}0&\text{for }u_i$

and so

$F_Z(z)=1-(1-F_{U_i}(z))^n=\begin{cases}0&\text{for }z$

Differentiate the CDF with respect to $z$ to obtain the PDF:

$f_Z(z)=\dfrac{\mathrm dF_Z(z)}{\mathrm dz}=\begin{cases}n(1-z)^{n-1}&\text{for }0$

i.e. $Z$ has a Beta distribution $\beta(1,n)$ .

3 0

3 years ago

Using a linear approximation, estimate f(2.1), given that f(2) = 5 and f'(x) = √3x-1.

algol [13]

Answer:

$f\left( {2.1} \right) \approx 5.22360$ .

Step-by-step explanation:

The linear approximation is given by the equation

${f\left( x \right) \approx L\left( x \right) }={ f\left( a \right) + f^\prime\left( a \right)\left( {x - a} \right).}$

Linear approximation is a good way to approximate values of $f(x)$ as long as you stay close to the point $x= a$ , but the farther you get from $x=a$ , the worse your approximation.