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Ilya [14]
3 years ago
11

Two different cars each depreciate to 60% of their respective original values. The first car depreciates at an annual rate of

Mathematics
2 answers:
Eddi Din [679]3 years ago
8 0

Answer: A) 1.7 Years

Step-by-step explanation:

Grace [21]3 years ago
7 0

Answer:

A. 1.7 years

Step-by-step explanation:

Let P_1 be the original value of first car,

Since, the car depreciates at an annual rate of  10%,

Let after t_1 years the value of car is depreciated to 60%,

That is,

P_1(1-\frac{10}{100})^{t_1}=60\%\text{ of }P_1

P_1(1-0.1)^{t_1}=0.6P_1

0.9^{t_1}=0.6

Taking ln on both sides,

t_1ln(0.9) = ln(0.6)

\implies t_1=\frac{ln(0.6)}{ln(0.9)}

Now, let P_2 is the original value of second car,

Since, the car depreciates at an annual rate of 15%

Suppose after t_2 years it is depreciated to 60%,

P_2(1-\frac{15}{100})^{t_2}=60\%\text{ of }P_2

P_2(1-0.15)^{t_2}=0.6P_2

0.85^{t_2}=0.6

Taking ln on both sides,

t_2ln(0.85) = ln(0.6)

\implies t_2=\frac{ln(0.6)}{ln(0.85)}

\because t_1-t_2=\frac{ln(0.6)}{ln(0.90)}-\frac{ln(0.6)}{ln(0.85)}

=1.70518303046

\approx 1.7

Hence, the approximate difference in the ages of the two cars is 1.7 years,

Option 'A' is correct.

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Answer:

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Step-by-step explanation:

The Graph of Normal Distribution is like a bell-shaped. Here the value of y is less for the lower value of x and then the value of y is increased for a larger value of x, but after some time value of y is again getting decrease as the value of x increases. For the Histogram to appear to a normal distribution, the graph of histogram must have the same nature. Thus option B is only the correct option.

The histogram is made up of columns bar with no gaps between bars with different labels of numeric data of different heights shows the size of the group of different labels.

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Jill’s bowling scores are approximately normally distributed with mean 170 and standard deviation 20, while Jack’s scores are ap
miss Akunina [59]

Answer:

a) The probability of Jack scoring higher is 0.3446

b) They probability of them scoring above 350 is 0.2119

Step-by-step explanation:

Lets call X the random variable that determines Jill's bowling score and Y the random variable that determines jack's. We have

X \simeq N(170,400)\\Y \simeq N(160,225)

Note that we are considering the variance on the second entry, the square of the standard deviation.

If we have two independent Normal distributed random variables, then their sum is also normally distributed. If fact, we have this formulas:

N(\lambda_1, \sigma^2_1) + N(\lambda_2, \sigma^2_2) = N(\lambda_1 + \lambda_2,\sigma^2_1 + \sigma^2_2) \\r* N(\lambda_1, \sigma^2_1) = N(r\lambda_1,r^2\sigma^2_1)  

for independent distributions N(\lambda_1, \sigma^2_1) , N(\lambda_2, \sigma^2_2) , and a real number r.

a) We define Z to be Y-X. We want to know the probability of Z being greater than 0. We have

Z = Y-X = N(160,225) - N(170,400) = N(160,225) + (N(-170,(-1)^2 * 400) = N(-10,625)

So Z is a normal random variable with mean equal to -10 and vriance equal to 625. The standard deviation of Z is √625 = 25.

Lets work with the standarization of Z, which we will call W. W = (Z-\mu)/\sigma = (Z+10)/25. W has Normal distribution with mean 0 and standard deviation 1. We have

P(Z > 0) = P( (Z+10)/25 > (0+10)/25) = P(W > 0.4)

To calculate that, we will use the <em>known </em>values of the cummulative distribution function Φ of the standard normal distribution. For a real number k, P(W < k) = Φ(k). You can find those values in the Pdf I appended below.

Since Φ is a cummulative distribution function, we have P(W > 0.4) = 1- Φ(0.4)

That value of Φ(0.4) can be obtained by looking at the table, it is 0.6554. Therefore P(W > 0.4) = 1-0.6554 = 0.3446

As a result, The probability of Jack's score being higher is 0.3446. As you may expect, since Jack is expected to score less that Jill, the probability of him scoring higher is lesser than 0.5.

b) Now we define Z to be X+Y Since X and Y are independent Normal variables with mean 160 and 170 respectively, then Z has mean 330. And the variance of Z is equal to the sum of the variances of X and Y, that is, 625. Hence Z is Normally distributed with mean 330 and standard deviation rqual to 25 (the square root of 625).

We want to know the probability of Z being greater that 350, for that we standarized Z. We call W the standarization. W is s standard normal distributed random variable, and it is obtained from Z by removing its mean 330 and dividing by its standard deviation 25.

P(Z > 350) = P((Z  - 330)/25 > (350-330)/25) = P(W > 0.8) = 1-Φ(0.8)

The last equality comes from the fact that Φ is a cummulative distribution function. The value of Φ(0.8) by looking at the table is 0.7881, therefore P(X+Y > 350) = 1 - Φ(0.8) = 0.2119.

As you may expect, this probability is pretty low because the mean value of the sum of their combined scores is quite below 350.

I hope this works for you!

Download pdf
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nydimaria [60]

Answer:

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Step-by-step explanation:

800-230=570

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