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AlekseyPX
4 years ago
5

A bank features a savings account that has an annual percentage rate of r=5.2% with interest compounded quarterly. Marcus deposi

ts $8,500 into the account.
The account balance can be modeled by the exponential formula S(t)=P(1+rn)^nt, where S is the future value, P is the present value, r is the annual percentage rate written as a decimal, n is the number of times each year that the interest is compounded, and t is the time in years.

(A) What values should be used for P, r, and n?

P= _____ , r=______ , n=________

(B) How much money will Marcus have in the account in 7 years?
Answer = $______ .
Round answer to the nearest penny.
Mathematics
1 answer:
Aloiza [94]4 years ago
6 0

Answer:

Part A)

P=\$8,500\\ r=0.052\\n=4  

Part B) S(7)=\$12,203.47  

Step-by-step explanation:

we know that    

The compound interest formula is equal to  

S(t)=P(1+\frac{r}{n})^{nt}  

where  

S is the Future Value  

P is the Present Value  

r is the rate of interest  in decimal

t is Number of Time Periods  

n is the number of times interest is compounded per year

Part A)

in this problem we have  

P=\$8,500\\ r=5.2\%=5.2/100=0.052\\n=4  

Part B) How much money will Marcus have in the account in 7 years?

we have

t=7\ years\\ P=\$8,500\\ r=0.052\\n=4  

substitute in the formula above  

S(7)=8,500(1+\frac{0.052}{4})^{4*7}  

S(7)=8,500(1.013)^{28}  

S(7)=\$12,203.47  

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Answer:

sinθ = 5/13

cosθ = 12/13

tanθ = 5/12

Step-by-step explanation:

Get the remaining side(hypotenuse) first,

hypotenuse^2 = 12^2 + 5^2  (Pyth. theorem)

hypotenuse = 13

sinθ = 5/13

cosθ = 12/13

tanθ = 5/12

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An interior automotive supplier places several electrical wires in a harness.Apull test measures the force required to pull spli
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Answer:

a) For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assumin Normal distribution

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical dsitribution

The result is on the figure attached.

b) For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

Step-by-step explanation:

For this case we have the following data:

28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4

The quantile-quantile or q-q plot is a graphical procedure in order to check the validity of a distributional assumption for a data set. We just need to calculate "the theoretically expected value for each data point based on the distribution in question".

If the values are asusted to the assumed distribution, we will see that "the points on the q-q plot will fall approximately on a straight line"

For this case our distribution assumed is normal.

Part a

For this case we can use the following R code to construct the qq plot

> data<-c(28.8, 24.4, 30.1, 25.6, 26.4, 23.9, 22.1, 22.5, 27.6, 28.1, 20.8, 27.7, 24.4, 25.1, 24.6, 26.3, 28.2, 22.2, 26.3, 24.4)

# The above line is in order to store the data in a vector

> qqnorm(data, pch = 1, frame = FALSE)

# The line above is in order to calculate the quantiles from the data assuming Normal distribution (0,1)

> qqline(data, col = "steelblue", lwd = 2)

# The line above is in order to put a line for the theoretical distribution

The result is on the figure attached.

Part b

For this case as we can see on the figure attached the calculated quantiles are not far from the theorical quantiles given byt the straaigth blue line so then we can conclude that the distribution seems to be normal.

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