-2+ 3(1-4)-2 what to do ???​

Z-score is used to test the hypothesis that the part is coming out longer than usual. Z-score is defined as the number of standard deviations away from the mean.

It is calculated using the formula z = (x - μ) / σ, where x is the sample mean, μ is the population mean, and σ is the population standard deviation. In the given problem, the population mean is μ = 12.05 cm and the population standard deviation is σ = 0.119 cm. The sample mean is x = 12.25 cm. The sample size is n = 19.

Therefore, the formula to calculate the z-score is Z = (x - μ) / (σ / √n) Substituting the values, we get z = (12.25 - 12.05) / (0.119 / √19) ≈ 6.586Therefore, the z-score which would be used to test the hypothesis that the part is coming out longer than usual is approximately 6.586.

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Answer:

1.8 inches

Step-by-step explanation:

1. find the # of inches that represents 1 foot.

3/25 = 0.12in

2. to find the length of the line segment that represents 15 feet, multiply 0.12 inches by 15.

0.12*15 = 1.8in

A net income forecast for Excellence Corporation is made by an analyst for the next fiscal year. Low-end estimate of net income is $261,000High-end estimate is $312,000. Net income follows a continuous uniform distribution. To find: The probability that net income will be greater than or equal to $299,500. In order to solve the problem, we need to calculate the probability that net income will be between $299,500 and $312,000.

Then, we will subtract it from the probability that net income will be between $261,000 and $312,000. This difference will give us the required probability. P(261000 <= X <= 312000) = (312000-261000) / (312000-261000) = 0.2P(299500 <= X <= 312000) = (312000-299500) / (312000-261000) = 0.55P(X >= 299500) = P(299500 <= X <= 312000) - P(261000 <= X <= 312000) = 0.55 - 0.2= 0.35 or 35%.

Thus, the probability that net income will be greater than or equal to $299,500 is 35%. Therefore, the answer is: 24.5%.

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Answer:

19)

20)

21)

22)

23)

24)

Answer:

360

Step-by-step explanation:

Answer:

the eanswer should be 12

Step-by-step explanation:

i took the test

Answer:

The length is 86 yards.

Step-by-step explanation:

63 is the width so,

63 + 63 = 126

Now subtract this from the total perimeter,

298 - 126 = 172

and divide by 2 because there are sides with the same length

172/2 = 86

The length is 86.

Answer:

x = 4

Step-by-step explanation:

Given

[tex]\frac{x-2}{4}[/tex] = [tex]\frac{3x-7}{10}[/tex] ( cross- multiply )

4(3x - 7) = 10(x - 2) ← distribute parenthesis on both sides )

12x - 28 = 10x - 20 ( subtract 10x from both sides )

2x - 28 = - 20 ( add 28 to both sides )

2x = 8 ( divide both sides by 2 )

x = 4

Approximately 48 subjects are needed to estimate the mean number of books read the previous year within six books with 90% confidence.

To estimate the mean number of books read the previous year within a certain margin of error, we need to determine the sample size required. In this case, we want to estimate the mean with a 90% confidence level and a margin of error of ±6 books.

The formula to calculate the required sample size is given by:

Since the standard deviation is unknown, we can use the initial survey result, s = 13.6 books, as an estimate for σ. However, this may result in a larger sample size than necessary.

Referring to the critical values table, we find the z-value corresponding to a 90% confidence level is approximately 1.645. Plugging in the values into the formula:

Since the sample size must be a whole number, we round up to the nearest subject. Therefore, approximately 48 subjects are needed to estimate the mean number of books read the previous year within six books with 90% confidence.

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Answer:

The distance between corner to corner is equal to √10 times the width.

D = √10*W

Step-by-step explanation:

For a rectangle of length L and width W, the distance between two opposite corners can be calculated if we use the Pythagorean's theorem, where we can think on the length as one cathetus, the width as another cathetus and the diagonal as the hypotenuse.

Then the length of the diagonal is:

D^2 = L^2 + W^2

D = √( L^2 + W^2)

In this case we know that the length is 3 times the width, then:

L = 3*W

Replacing this in the equation for the diagonal we have:

D = √( (3*W)^2 + W^2) = √( 9*W^2 + W^2)

D = √( 10*W^2) = √10*√W^2 = √10*W

D = √10*W

The distance between corner to corner is equal to √10 times the width.

Answer:

its 2

Step-by-step explanation:

I have consulted many nice people ( my idiot friends) they all say 2 good luck

Answer:

f(x) = 76

Step-by-step explanation:

All you do is plug-in for x: (x = 9)

f(x) = 8x + 4

f(x) = 8(9) + 4

f(x) = 72 + 4

f(x) = 76

To prove that the set S = 2Z = {2x : x ∈ Z}, the set of even integers, is equicardinal with Z, the set of all integers, we need to show that there exists a one-to-one correspondence (bijection) between the two sets.

A one-to-one correspondence between two sets exists if every element of one set is paired with a unique element from the other set, and vice versa.

Let's define a function f: Z -> S such that f(x) = 2x for every x ∈ Z.

This function assigns each integer x from Z to its corresponding even integer 2x in S.

To prove that f is a bijection, we need to show that it is both injective (one-to-one) and surjective (onto).

Injectivity: Suppose f(a) = f(b) for some a, b ∈ Z. Then, 2a = 2b. Dividing both sides by 2, we get a = b. Hence, f is injective.

Surjectivity: For every y ∈ S, y = 2x for some x ∈ Z. Choosing x = y/2, we have f(x) = 2x = y. Therefore, f is surjective.

Since f is both injective and surjective, it is a bijection. Thus, S and Z are equicardinal sets.

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(a) If you measure a wound to be 2.5 inches long, the corresponding length in millimeters is 63.5 mm.

(b) If you measure the depth of a wound to be 0.25 inches, the corresponding length in millimeters is 6.35 mm.

If you measure a wound to be 2.5 inches long, the corresponding length in millimeters is calculated as follows;

1 in = 25.4 mm

2.5 in = ?

? = 2.5 x 25.4 mm

? = 63.5 mm

If you measure the depth of a wound to be 0.25 inches, the corresponding length in millimeters is calculated as follows;

1 in = 25.4 mm

0.25 in = ?

? = 0.25 x 25.4 mm

? = 6.35 mm

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Answer:

C)  6

Step-by-step explanation:

use slope formula:

(-4 - 3) / (-1 - x) = 1

cross-multiply:

-7 = -1 - x

-6 = -x

6 = x

Answer:

x = [-b ±√(b² - 4ac)]/2a

Step-by-step explanation:

ax² + bx + c = 0

dividing through by a, we have

ax²/a + bx/a + c/a = 0

x² + bx/a + c/a = 0

x² + bx/a = -c/a

we now add th square of half the coefficient of x to both sides, thus

x² + bx/a + (b/2a)² = -c/a +  (b/2a)²

simplifying the left hand side and right hand side, we have

(x + b/2a)² = -c/a + b²/4a²

re-arranging, we have

(x + b/2a)² = b²/4a² - c/a

taking L.C.M of the right hand side, we have

(x + b/2a)² = (b² - 4ac)/4a²

taking square-root of both sides, we have

√(x + b/2a)² = ±√[(b² - 4ac)/4a²]

x + b/2a = ±√(b² - 4ac)/2a

So, x = -b/2a ±√(b² - 4ac)/2a

taking the L.C.M of the right hand side, we have

x = [-b ±√(b² - 4ac)]/2a

a. P(1) = 0.3556, P(2) = 0.3111, P(3) = 0.0444; Py(1) = 0.5333, Py(2) = 0.4444, Py(3) = 0.1222 b. P(N = 1 | M = 2) ≈ 0.2574 c.P(M = 2, N = 3) ≈ 0.038 d.  Pr(M>N) = 0.5333.

a. The marginal probability function of m is given by P(m) = Σn P(m, n) and that of n is given by P(n) = Σm P(m, n).

Thus, the marginal PDFs are: P(1) = 1/5 + 8/45 + 2/15 = 0.3556 P(2) = 7/45 + 4/45 + 1/15 = 0.3111

P(3) = 1/9 + 2/45 + 1/45 = 0.0444 P(1) + P(2) + P(3) = 1 Py(1) = 1/5 + 7/45 + 2/15 = 0.5333

Py(2) = 8/45 + 4/45 + 1/15 = 0.4444 Py(3) = 2/15 + 1/15 + 1/45 = 0.1222 Py(1) + Py(2) + Py(3) = 1.

b. We need to find P(N = 1 | M = 2).

From the joint probability distribution table, we can see that P(N = 1, M = 2) = 8/45. P(M = 2) = 0.3111.

Using the conditional probability formula, P(N = 1 | M = 2) = P(N = 1, M = 2)/P(M = 2) = 8/45 ÷ 0.3111 ≈ 0.2574

c. We need to find the probability that M = E and N = N.

Since the two random variables are independent, we can simply multiply their probabilities: P(M = E, N = N) = P(M = E) × P(N = N).

The probability distribution of M is given by: M=1 with probability 0.3556 M=2 with probability 0.3111 M=3 with probability 0.0444

The probability distribution of N is given by: N=1 with probability 0.5333 N=2 with probability 0.4444 N=3 with probability 0.1222

Therefore, P(M = 2, N = 3) = P(M = 2) × P(N = 3) = 0.3111 × 0.1222 ≈ 0.038

d. We need to find P(M > N).

There are three possible pairs of values for (M, N) such that M > N: (M = 2, N = 1), (M = 3, N = 1), and (M = 3, N = 2).

The probabilities of these pairs of values are: P(M = 2, N = 1) = 1/5 P(M = 3, N = 1) = 1/9 P(M = 3, N = 2) = 1/15

Therefore, P(M > N) = P(M = 2, N = 1) + P(M = 3, N = 1) + P(M = 3, N = 2) = 1/5 + 1/9 + 1/15 = 0.5333.

Answer: a. P(1) = 0.3556, P(2) = 0.3111, P(3) = 0.0444; Py(1) = 0.5333, Py(2) = 0.4444, Py(3) = 0.1222 b. 0.2574 c. 0.038 d. 0.5333

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Answer:

Step-by-step explanation:

To find each probability you will state the probability of the first event happening and multiply it by the chance of the second event happening.  

P(2 Nickels):  6/10 x 5/9 = 30/90 (1/3 chance)

P(2 Dimes):  4/10 x 3/9 = 12/90 (4/30 chance)

P(1 nickel and 1 dime): 6/10 x 4/9 = 24/90 (8/30 chance)

I hope this helps :D

Answer: 4x-3

Step-by-step explanation: use the slope formula to calculate the slope, then add the y-int to the equation and ur done hope this helps

Answer:

Steps:

SA = 2LW + 2WH + 2LH

SA = 2(9×2) + 2(2×11/2) + 2(9×11/2)

SA = 2×18 + 2×22/2 + 2×99/2

SA = 36 + 22 + 99

SA = 157 in²

Answer:

3.14152628283829299348473929

Answer: It’s 3

Step-by-step explanation:

Hoped I help :)

The Europay, Mastercard and Visa (EMV) for not purchase the snow removal machine is $31,000.

The expected monetary value (EMV) for not purchasing the snow removal machine is $31,000. This value is calculated by multiplying the probabilities of each winter condition by the corresponding profits and summing them up. The probabilities for a mild winter, typical winter, and severe winter are 0.3, 0.5, and 0.2, respectively.

The profits for each condition without the machine are $20,000, $530,000, and $50,000. By multiplying each profit by its probability and adding them together, we get the EMV of $31,000 for not purchase the machine.

In detail, the EMV is calculated as follows:

EMV = (Probability of Mild Winter * Profit for Mild Winter) + (Probability of Typical Winter * Profit for Typical Winter) + (Probability of Severe Winter * Profit for Severe Winter)

EMV = (0.3 * $20,000) + (0.5 * $530,000) + (0.2 * $50,000)

EMV = $6,000 + $265,000 + $10,000

EMV = $31,000

Therefore, the EMV for not purchasing the snow removal machine is $31,000.

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Answer:

H

Step-by-step explanation:

The rectangle has a length of (2x + 1) and a width of (5x - 4).

And we want to select the expression that represents the rectangle's area.

Recall that a rectangle's area is simply its length multiplied by its width. Thus:

[tex]A=(2x+1)(5x-4)[/tex]

Expand by distributing:

[tex]A=(2x+1)(5x)+(2x+1)(-4)[/tex]

Distribute:

[tex]A=(10x^2+5x)+(-8x-4)[/tex]

Simplify:

[tex]A=10x^2-3x-4[/tex]

Hence, our answer is H.

Answer:

I don't know.

Step-by-step explanation:

Answer:

17.7 cm

Step-by-step explanation:

One of the legs of a right triangle measures 12 cm and the other leg measures 13 cm find the measure of the hypotenuse if necessary round the nearest 10th

To find the Hypotenuse of a right angle triangle, we solve using Pythagoras Theorem

Hypotenuse ² = Opposite ² + Adjacent ²

Hypotenuse = √Opposite ² + Adjacent ²

Opposite = 12 cm

Adjacent = 13 cm

Hence,

Hypotenuse = √12² + 13²

= √144 + 169

= 17.691806013 cm

Approximately = 17.7 cm

Therefore, the measure of the Hypotenuse is 17.7 cm

In order to test if we have adopted the wrong functional forms of independent variables in a regression model Y = α + βX + u, we can use diagnostic plots of the residuals.

Residuals are the differences between the actual values of the dependent variable and the predicted values of the dependent variable. We can plot the residuals against the independent variables to check if there is any functional form that has not been captured by the model, which would indicate that the model has adopted the wrong functional form of independent variables.

If there is no pattern in the residuals when plotted against the independent variables, then it can be concluded that the model has captured the correct functional form of the independent variables. However, if there is a pattern in the residuals, it would indicate that there is some functional form that has not been captured by the model, and we would need to explore different functional forms of the independent variables to see which one fits the data better. This can be done by using different functional forms of the independent variables, such as polynomial, logarithmic, exponential, etc., and comparing the results using various statistical tests.

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If the residuals are normally distributed, then it suggests that the functional form of the independent variable is correct. If they are not, then it suggests that the functional form of the independent variable is incorrect.Overall, it is important to test for the functional forms of independent variables to ensure that the results of the regression model are unbiased and accurate.

In a regression model, it is important to make sure that the functional forms of the independent variables are correct. If they are not, the results of the model may be biased and incorrect. There are several ways to test if the functional forms are correct or not. Below are some of the methods:Plotting the residuals against the independent variable: If the functional form of the independent variable is correct, then the residuals should be randomly distributed around zero, without showing any patterns. If there are patterns in the residuals, then it suggests that the functional form is incorrect. Testing for linearity: One way to test for linearity is by including the square or cubic terms of the independent variable in the model and testing their significance. If the squared or cubic term is significant, then it suggests that the functional form of the independent variable is incorrect. Testing for additivity: One way to test for additivity is by including interaction terms between the independent variables and testing their significance. If the interaction term is significant, then it suggests that the functional form of the independent variable is incorrect. Testing for normality: One way to test for normality is by examining the normal probability plot of the residuals.

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