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konstantin123 [22]
4 years ago
12

Solve for the variable: 4x-28=16 A. 8 B. -3 C. 6 D. 11

Mathematics
1 answer:
sveta [45]4 years ago
6 0

4x-28=16

move -28 to the other side

sign changes from -28 to +28

4x-28+28=16+28

4x=16+28

4x=44

divide both sides by 4 to get x by itself

4x/4=44/4

x=11

Answer:

D. 11

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wlad13 [49]
37.6 because the fist part is times 2 so 14•2
5 0
4 years ago
HH represents the height of the tree (in centimeters), t tt years since Renata moved in. H = 210 + 33 t H=210+33tH, equals, 210,
Jet001 [13]

Answer:

Therefore the rate of growth of the tree is 33 cm/ yr.

Step-by-step explanation:

Given that,

The height of the tree is

H= 210 +33t

Here H represent the height of tree in cm and t is time in year.

The rate of growth is the first order derivative of the H with respect to time.

i.e The rate of growth  =\frac{dH}{dt}

∴H= 210 +33t

Differentiating with respect to t

\frac{dH}{dt} =0 +33

\Rightarrow \frac{dH}{dt}=33

Therefore the rate of growth of the tree is 33 cm/ yr.

7 0
3 years ago
The growth of an apple tree was measured. The tree grew 15 inches per year.
pogonyaev

Answer:

1.04 mill

Step-by-step explanation:

15 inches to mill = 381

365\sqrt{381} = 1.04 ( rounded )

8 0
3 years ago
write the equation for the nth term of the sequence and then find the value of the 24th term: 4, 13, 22, 31, ...
julia-pushkina [17]

Answer :

nth term

an = a1 + (n-1) d

an = 4 + (9-1) 9

an = 4 + (8)9

an = 4 + 72

an = 82

24th term

an = 4 + (24-1) 9

an = 4 + (23) 9

an = 4 + 207

an = 211 is the answer

Hope it helped

4 0
3 years ago
What is the likelihood that a fair coin will land heads or tails?
Marina CMI [18]

Answer:

I believe it is 0.5

Step-by-step explanation:

If you flip a normal coin (called a “fair” coin in probability parlance), you normally have no way to predict whether it will come up heads or tails. Both outcomes are equally likely. There is one bit of uncertainty; the probability of a head, written p(h), is 0.5 and the probability of a tail (p(t)) is 0.5. The sum of the probabilities of all the possible outcomes adds up to 1.0, the number of bits of uncertainty we had about the outcome before the flip. Since exactly one of the four outcomes has to happen, the sum of the probabilities for the four possibilities has to be 1.0. To relate this to information theory, this is like saying there is one bit of uncertainty about which of the four outcomes will happen before each pair of coin flips. And since each combination is equally likely, the probability of each outcome is 1/4 = 0.25. Assuming the coin is fair (has the same probability of heads and tails), the chance of guessing correctly is 50%, so you'd expect half the guesses to be correct and half to be wrong. So, if we ask the subject to guess heads or tails for each of 100 coin flips, we'd expect about 50 of the guesses to be correct. Suppose a new subject walks into the lab and manages to guess heads or tails correctly for 60 out of 100 tosses. Evidence of precognition, or perhaps the subject's possessing a telekinetic power which causes the coin to land with the guessed face up? Well,…no. In all likelihood, we've observed nothing more than good luck. The probability of 60 correct guesses out of 100 is about 2.8%, which means that if we do a large number of experiments flipping 100 coins, about every 35 experiments we can expect a score of 60 or better, purely due to chance.

6 0
3 years ago
Read 2 more answers
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