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NemiM [27]
3 years ago
6

If r=10 and s=31, find R. Round to the nearest tenth.

Mathematics
2 answers:
mars1129 [50]3 years ago
5 0

Answer:

C. R = 17.9 degrees

Step-by-step explanation:

We have a rectangle triangle with the adjacent side and the opposite side (neither of which are the hypotenuse).

The relation between those elements is the tangent:

tan(angle) = \frac{Opposite side}{Adjacent side}

So, to isolate the angle, we modify the formula as such:

angle = arctan(\frac{Opposite side}{Adjacent side}) = arctan(\frac{10}{31}) = arctan(0.3225) = 17.87

If we round 17.87 degrees to the tenth... we get 17.9 degrees.

Makovka662 [10]3 years ago
3 0

Answer:

The correct answer is option c.  17.9°

Step-by-step explanation:

From the figure we can see that a right angled triangle RST.

Right angled at T

<u>To find the value of R </u>

It is given that,

r=10 and s=31

Tan R = Opposite side/Adjacent side

Tan R = ST/SR = r/s = 10/31 = 0.3225

R = Tan⁻¹(0.3225) = 17.87 ≈  17.9°

Therefore the correct answer is option c.  17.9°

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5.2.14. For the negative binomial pdf p (k; p, r) = k+r−1 (1 − p)kpr, find the maximum likelihood k estimator for p if r is know
Volgvan

Answer:

\hat p = \frac{r}{\bar x +r}

Step-by-step explanation:

A negative binomial random variable "is the number X of repeated trials to produce r successes in a negative binomial experiment. The probability distribution of a negative binomial random variable is called a negative binomial distribution, this distribution is known as the Pascal distribution".

And the probability mass function is given by:

P(X=x) = (x+r-1 C k)p^r (1-p)^{x}

Where r represent the number successes after the k failures and p is the probability of a success on any given trial.

Solution to the problem

For this case the likehoof function is given by:

L(\theta , x_i) = \prod_{i=1}^n f(\theta ,x_i)

If we replace the mass function we got:

L(p, x_i) = \prod_{i=1}^n (x_i +r-1 C k) p^r (1-p)^{x_i}

When we take the derivate of the likehood function we got:

l(p,x_i) = \sum_{i=1}^n [log (x_i +r-1 C k) + r log(p) + x_i log(1-p)]

And in order to estimate the likehood estimator for p we need to take the derivate from the last expression and we got:

\frac{dl(p,x_i)}{dp} = \sum_{i=1}^n \frac{r}{p} -\frac{x_i}{1-p}

And we can separete the sum and we got:

\frac{dl(p,x_i)}{dp} = \sum_{i=1}^n \frac{r}{p} -\sum_{i=1}^n \frac{x_i}{1-p}

Now we need to find the critical point setting equal to zero this derivate and we got:

\frac{dl(p,x_i)}{dp} = \sum_{i=1}^n \frac{r}{p} -\sum_{i=1}^n \frac{x_i}{1-p}=0

\sum_{i=1}^n \frac{r}{p} =\sum_{i=1}^n \frac{x_i}{1-p}

For the left and right part of the expression we just have this using the properties for a sum and taking in count that p is a fixed value:

\frac{nr}{p}= \frac{\sum_{i=1}^n x_i}{1-p}

Now we need to solve the value of \hat p from the last equation like this:

nr(1-p) = p \sum_{i=1}^n x_i

nr -nrp =p \sum_{i=1}^n x_i

p \sum_{i=1}^n x_i +nrp = nr

p[\sum_{i=1}^n x_i +nr]= nr

And if we solve for \hat p we got:

\hat p = \frac{nr}{\sum_{i=1}^n x_i +nr}

And if we divide numerator and denominator by n we got:

\hat p = \frac{r}{\bar x +r}

Since \bar x = \frac{\sum_{i=1}^n x_i}{n}

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