500+400-15+40-5+500000-200+500

An investor holds $100,000 (par value) worth of TIPS currently trading at par. The coupon rate of 4% is paid semiannually, and t

poizon [28]

Answer:

coupon payment = $2025

so correct option is A) $2,025

Explanation:

given data

par value = $100,000

coupon rate = 4%

annual inflation rate = 2.5% = 0.025

so Semiannual rate = $\frac{0.025}{2}$ = 0.0125

to find out

coupon payment will the investor receive at the end of the first six months

solution

as we know principal would increase by the amount of inflation

so it will be = $100,000 ( 1 + 0.0125 )

so here coupon payment will as

coupon payment = $100,000 ( 1 + 0.0125 ) × $\frac{0.04}{2}$

solve it we get

coupon payment = $2025

so correct option is A) $2,025

6 0

4 years ago

Jim's Widgets creates mechanical tools that are used in factories. Use the table and the drop-down menus to answer these questio

Vladimir79 [104]

Answer:

The profit when the company makes five widgets is $30

To maximize profit, the company should produce 6 widgets per day

The company's profit would decrease by $17 if the company made seven widgets

Explanation:

i took the quiz.

4 0

3 years ago

Startups that find themselves trying to compete for value with large, established firms that have strong negotiating power often

Leokris [45]

Answer:

That statement is true.

Explanation:

Start ups tends to have overwhelmingly lesser capital compared to large/established firms. This means that The Large firms will be able to outperform the start ups in terms of marketing , advertising, and production efficiency.

This will make the start ups' product became less known and more expensive in the market.

Because of this, they tend to focus on the acquisition of intellectual property.

When a start up acquire the right of intellectual property, larger companies could not legally create a similar product and compete with the start up directly.

This will make the start up able to sell their products without having to worry about being outperformed by the larger companies.

6 0

3 years ago

mai has $36 to spend on movie tickets it's movie ticket cost $4.50 how much movie tickets can you buy ?

kenny6666 [7]

8 movie tickets i belive

5 0

3 years ago

Read 2 more answers

Your office network has been measured to stay working an average of 2,200 hours with a standard deviation of 285 hours. What is

JulsSmile [24]

We assume here that the probability for an office network to fail follows a normal distribution with a population mean of 2,200 hours and a population standard deviation of 285 hours.

Answer:

The probability that the network will stay up for 2,800 hours before it fails is about 1.743%.

Explanation:

According to the question that the office network "has been measured to stay working an average of 2,200 hours", we can conclude that, for normally distributed data, at this working time, the office network has a probability of failure of 50% and a probability of being working of 50%, too.

As the office network still operates, the probability of failure increases following a normal distribution. So, for 2,800 hours of operation, we need to calculate the probability of failure for this network.

For this, we need to determine the z-score for the raw value of x = 2,800 hours, to later consult a standard cumulative normal table and find the probability associated with this z-score. To calculate it, we can use the z-score formula:

$z\;score = \frac{x - \mu}{\sigma}$

Where

$\\ \mu\;is\;the\;population\;mean$

$\\ \sigma\;is\;the\;population\;standard\;deviation$

And x is the raw score or the 2,800 hours of operation for the office network.

Thus

$z = \frac{2800 - 2200}{285}$

$z = 2.105 \approx 2.11$

Having a z = 2.11 (approximately) and consulting a standard cumulative normal table, we have that P(z<2.11) = 0.98257.

In other words, for 2,800 hours of operation for the office network, there is a probability of about 98.257% that this network has failed by this time.

Therefore, the probability that the network will stay up for 2,800 hours is 1 - 0.98257 = 0.01743 or about 1.743% of being working before it fails (or for only about 1.743% of the cases, the office network stays working for 2,800 hours).

The graph below has the shaded area that represents this probability.

8 0

3 years ago

500+400-15+40-5+500000-200+500​

500+400-15+40-5+500000-200+500