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KengaRu [80]
3 years ago
15

a certain steel beam weighs 18.4 lbs per foot of length. If this beam is 26.5 feet long, how many lbs does it weigh?

Mathematics
1 answer:
torisob [31]3 years ago
5 0
487.6 pounds, I believe. This is by multiplying 18.4 by 26.5.
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Can you guys help me out for my math test <br> on usa test prep
Ber [7]

Answer:

You need to type the question or take a screenshot of the question(s) you want to be answered so someone can help you out. Otherwise, we are unable to answer any of your questions.

4 0
3 years ago
John wants to build a corral next to his barn. He has 300 feet of fencing to enclose three sides of his rectangular yard.
Mandarinka [93]
Let's first define the variables:
 x = width
 300 - 2x = long
 The area will be:
 A = (x) * (300 - 2x)
 A = 300x - 2x²
 We look for the maximum area, for this, we derive:
 A '= 300 - 4x
 We match zero:
 0 = 300 - 4x
 x = 300/4 = 75
 Therefore, the width is:
 x = 75 feet
 The length is:
 300 - 2x = 300 - 2 (75) = 300-150
 150 feet
 Answer:
 Part A:
 
The maximum area will be:
 
A = (150) * (75) = 11250 square feet
 
Part B:
 
The dimensions are:
 
Length = 150 feet
 width = 75 feet
3 0
3 years ago
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}

4 0
3 years ago
What’s 6x-3y=18 in slope int? pls help lol
liraira [26]

Answer:

Using the Slope-intercept form, the slope is - 2.

8 0
3 years ago
You like math then here
Igoryamba

Answer:

-8b + 9 - 5k

Step-by-step explanation:

I am assuming the blue highlighted portion is your answer, and is not a part of the initial question

(-4b + 15 - 7k) - (6 + 4b - 2k)

Combine like terms

-4b - + 4b

Negative + Positive = Negative

-4b - 4b = -8b

15 - 6 = 9

-7k - - 2k

Negative + Negative = Positive

-7k + 2k = -5k

Put them all together:

0b + 9 - 5k

Simplify

-8b + 9 - 5k

I got the same answer as you (just in a different order)

5 0
3 years ago
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