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(a)/(5h+q)=t Find a (a)/ means fraction

Answers

Answer 1

Answer:

Answer:

Step-by-step explanation:

a/(5h+q) = t

multiply both sides by 5h + q

a / (5h + q) * (5h+q) = t * (5h + q)

a = t * (5h + q)

I would say this is your answer.

You could distribute the t

a = 5ht + tq

which could also be an answer.

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What number should I multiply 1 1/4 by to get 7/12

Answers

Let's take our given information and transform it into numbers. We will let x equal the "mystery" number we need to find. Here is our equation:
$1 (1)/(4) x= (7)/(12)$
Now, all we need to do is convert the mixed fraction into an improper fraction:
$(5)/(4) x= (7)/(12)$
Now, just multiply the reciprocal of 5/4 with 7/12, giving us:
$x=(4)/(5)* (7)/(12)$
Finally, just straight up multiply to get an answer of x = 28/60, which can be simplified down to x = 7/15. Therefore, the number you have to multiply 1 1/4 to get 7/12 is 7/15. Hope this helped!

1 and 1/4 is the same as 5/4 so essentially we are asking what do we multiple 5/4 by to get 7/12. Try to keep things as fractions initially to see if there is a fractional answer with whole numbers.

Let x represent the number:
x . 5/4 = 7/12
i.e. 5x/4 = 7/12
We want to get x on it's own so multiply both sides of equation by 4
5x = 4 . (7/12) = 28/12
now divide both sides by 5 to get x on its own
x = (28/12) / 5
This does not equate to a whole number fraction as 28 and 12 are not divisible by 5. So simply convert to decimal
x = 2.3333 / 5 = 0.46666

At the end of 2019, Mark owes $250,000 on the mortgage related to the 2016 purchase of his residence. When his daughter went to college in the fall of 2019, he borrowed $20,000 through a home equity loan on his house to help pay for her education. The interest expense on the main mortgage is $15,000, and the interest expense on the home equity loan is $1,500. How much of the interest is deductible as an itemized deduction?

Answers

Answer:

$15,000

Step-by-step explanation:

The $1500 interest on a home equity loan used for purposes other than home improvement is not deductible with other home loan interest as an itemized deduction.

However, the interest on a loan for qualified educational expenses may be considered an adjustment to income, within limits.

Only the $15,000 main mortgage interest can be an itemized deduction.

Final answer:

The total possible mortgage interest deduction for Mark in this scenario is $16,500. However, the actual amount he can deduct depends on his adjusted gross income and whether his itemized deductions exceed the standard deduction.

Explanation:

Under US tax law, taxpayers can deduct the interest on home mortgages and home equity loans, subject to some limitations. The interest expense on the main mortgage ($15,000) and the interest expense on the home equity loan ($1,500) can be combined for a total interest deduction of $16,500. However, the deduction may not be the full amount if there are other factors that would limit the amount of itemized deductions that Mark can claim. This can depend on his adjusted gross income and whether the total of his itemized deductions exceeds the standard deduction. It's also worth noting that the tax benefits of home ownership, such as the mortgage interest deduction, is a key reason why many people choose to buy rather than rent, as it can lead to significant financial savings.

Learn more about Mortgage Interest Deduction here:

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Lengths of time, in months for a tumor to recur after chemotherapy I need answers for questions 1-8

Answers

Answer: irdk the awnser but F on that tumor

Let the number of chocolate chips in a certain type of cookie have a Poisson distribution. We want the probability that a cookie of this type contains at least two chocolate chips to be greater than 0.99. Find the smallest value of the mean that the distribution can take.

Answers

Answer:

$\lambda \geq 6.63835$

Step-by-step explanation:

The Poisson Distribution is "a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event".

Let X the random variable that represent the number of chocolate chips in a certain type of cookie. We know that $X \sim Poisson(\lambda)$

The probability mass function for the random variable is given by:

$f(x)=(e^(-\lambda) \lambda^x)/(x!) , x=0,1,2,3,4,...$

And f(x)=0 for other case.

For this distribution the expected value is the same parameter $\lambda$

$E(X)=\mu =\lambda$

On this case we are interested on the probability of having at least two chocolate chips, and using the complement rule we have this:

$P(X\geq 2)=1-P(X<2)=1-P(X\leq 1)=1-[P(X=0)+P(X=1)]$

Using the pmf we can find the individual probabilities like this:

$P(X=0)=(e^(-\lambda) \lambda^0)/(0!)=e^(-\lambda)$

$P(X=1)=(e^(-\lambda) \lambda^1)/(1!)=\lambda e^(-\lambda)$

And replacing we have this:

$P(X\geq 2)=1-[P(X=0)+P(X=1)]=1-[e^(-\lambda) +\lambda e^(-\lambda)[]$

$P(X\geq 2)=1-e^(-\lambda)(1+\lambda)$

And we want this probability that at least of 99%, so we can set upt the following inequality:

$P(X\geq 2)=1-e^(-\lambda)(1+\lambda)\geq 0.99$

And now we can solve for $\lambda$

$0.01 \geq e^(-\lambda)(1+\lambda)$

Applying natural log on both sides we have:

$ln(0.01) \geq ln(e^(-\lambda)+ln(1+\lambda)$

$ln(0.01) \geq -\lambda+ln(1+\lambda)$

$\lambda-ln(1+\lambda)+ln(0.01) \geq 0$

Thats a no linear equation but if we use a numerical method like the Newthon raphson Method or the Jacobi method we find a good point of estimate for the solution.

Using the Newthon Raphson method, we apply this formula:

$x_(n+1)=x_n -(f(x_n))/(f'(x_n))$

Where :

$f(x_n)=\lambda -ln(1+\lambda)+ln(0.01)$

$f'(x_n)=1-(1)/(1+\lambda)$

Iterating as shown on the figure attached we find a final solution given by:

$\lambda \geq 6.63835$

Final answer:

The problem pertains to Poisson Distribution in probability theory, focusing on finding the smallest mean (λ) such that the probability of having at least two chocolate chips in a cookie is more than 0.99. This involves solving an inequality using the formula for Poisson Distribution.

Explanation:

This problem pertains to the Poisson Distribution, often used in probability theory. In particular, we're looking at the number of events (in this case, the number of chocolate chips) that occur within a fixed interval. Here, the interval under study is a single cookie. The question requires us to find the smallest value of λ (the mean value of the distribution) such that the probability of getting at least two chocolate chips in a cookie is more than 0.99.

Using the formula for Poisson Distribution, the probability of finding k copies of an event is given by:

P(X=k) = λ^k * exp(-λ) / k!

The condition here is that the probability of finding at least 2 copies is more than 0.99. Therefore, you formally need to solve the inequality:

P(X>=2) = 1 - P(X=0) - P(X=1) > 0.99

Substituting the values of P(X=0) and P(X=1) from our standard formula, you will need to calculate and find the smallest value of λ that satisfies this inequality.

Learn more about Poisson Distribution here:

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tonight's attendance was 100 more than double the attendance last night. If 500 attended tonight, how many attended last night?

Answers

Answer: 200

Step-by-step explanation: 200 doubled would be 400 and it says that tonights attendence was 100 more than the doubled which would add up and equal 500 (400+100=500) so 200 is your answer.

At a certain car dealership, 20% of customers who bought a new vehicle bought an SUV, and 3% of them bought a black SUV (that is 3% of customers bought a vehicle that was an SUV and in black color). Given that a customer bought an SUV, what is the probability that it was black?

Answers

Answer: 0.15

Step-by-step explanation:

As per given , the probability that customers who bought a new vehicle bought an SUV : P(SUV) = 0.20

The probability that customer bought a vehicle that was an SUV and in black color : P(SUV and black) =0.03

Now by suing conditional probability formula,

If we have given that a customer bought an SUV, then the probability that it was black will be :

$\text{P(Black}|\text{SUV})=\frac{\text{P(SUV and Black)}}{\text{P(SUV)}}$

$=(0.03)/(0.20)=(3)/(20)=0.15$

Hence, the required probability is 0.15.

The probability that a customer who bought an SUV also bought a black SUV is 0.006, or 0.6% (expressed as a percentage).

To find the probability that a customer who bought an SUV also bought a black SUV, you can use conditional probability.

Let's define the following events:

A: A customer bought an SUV.

B: A customer bought a black SUV.

You are given that P(B|A) is the probability that a customer who bought an SUV also bought a black SUV, which is 3% or 0.03.

You want to find P(B|A), the probability that a customer who bought an SUV also bought a black SUV. You can use the following formula for conditional probability:

P(B|A) = (P(A and B)) / P(A)

Here, P(A and B) is the probability that a customer bought both an SUV and a black SUV, and P(A) is the probability that a customer bought an SUV.

You know that P(B|A) = 0.03 and P(A) = 0.20.

Now, you need to find P(A and B), the probability that a customer bought both an SUV and a black SUV. You can rearrange the formula:

P(A and B) = P(B|A) * P(A)

P(A and B) = 0.03 * 0.20

P(A and B) = 0.006

for such more question on probability

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