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3 years ago

Which is an arithmetic sequence?

1 answer:

5 0

B) is an arithmetic sequence because you are adding by 2 each time. The other sequences don’t match this criteria.
I’m sorry I’m not sure what else to say I hope this somewhat helped :/
Have a great day :)

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I need help finishing a question. I used the explanation function on the program, but it didn't specify what to do to get the fi

leva [86]

I would compute sqrt(1 + 160pi^2) first to get approximately 39.75093337

Add this to 1 and we have 40.75093337

Then divide over 2pi to get a final approximate result of 6.48571248

So x = 6.48571248 is one approximate solution

In short, I computed $\frac{1+\sqrt{1+160\pi^2}}{2\pi}$ only focusing on the plus for now.

----------------------

If you were to compute $\frac{1-\sqrt{1+160\pi^2}}{2\pi}$ you should get roughly -6.167402596 as your other solution. Each solution can then be plugged into the original equation to check if you get 0 or not. You likely won't land exactly on 0 but you'll get close enough.

6 0

3 years ago

Read 2 more answers

Help i’ll give Brain :(

vesna_86 [32]

Answer:

1, 4 and 5

Step-by-step explanation:

5 0

3 years ago

Read 2 more answers

(easy) If ΔEFG ~ ΔLMN with a ratio of 3:1, which of the following is true?

alexandr402 [8]

Answer:

segment EG over segment LN equals segment FG over segment MN

Step-by-step explanation:

we know that

If two figures are similar, then the ratio of its corresponding sides is proportional and its corresponding angles are congruent

In this problem

The corresponding sides are

EF and LM

EG and LN

FG and MN

The corresponding angles are

∠E≅∠L

∠F≅∠M

∠G≅∠N

therefore

EF/LM=EG/LN=FG/MN=3/1

7 0

3 years ago

Read 2 more answers

Ratinal numbers are real numbers

tatuchka [14]

What's Ratinal? Do you mean rational? If so, you're right.

4 0

3 years ago

Let P = 0.50.30.50.7 be the transition matrix for a Markov chain with two states. Let x0 = 0.50.5 be the initial state vector fo

pav-90 [236]

Answer:

Probability distribution vector = $\left(\begin{array}{c}0.375\\ 0.625 \end{array} \right)$

Step-By-Step Explanation

If $P=\left(\begin{array}{cc}0.5&0.3\\ 0.5&0.7 \end{array} \right)$ is the transition matrix for a Markov chain with two states.

$x_{0}=\left(\begin{array}{c}0.5\\ 0.5 \end{array} \right)$ be the initial state vector for the population.

$X_{1}=P x_{0}=\left(\begin{array}{cc}0.5&0.3\\ 0.5&0.7 \end{array} \right) \left(\begin{array}{c}0.5\\ 0.5 \end{array} \right) =\left(\begin{array}{c}0.4\\ 0.6 \end{array} \right)$

$X_{2}=P^{2} x_{0}=\left(\begin{array}{c}0.38\\ 0.62 \end{array} \right)$

$X_{3}=P^{3} x_{0}=\left(\begin{array}{c}0.38\\ 0.62 \end{array} \right)$

$X_{30}=P^{30} x_{0}=\left(\begin{array}{c}0.37499\\ 0.625 \end{array} \right)$

In the long run, the probability distribution vector Xm approaches the probability distribution vector $\left(\begin{array}{c}0.375\\ 0.625 \end{array} \right)$ .

This is called the steady-state (or limiting,) distribution vector.

4 0

3 years ago