3. What is the surface area of this composite solid? Show your work.

Answer:

1/25

Step-by-step explanation:

4 over 100 = 1/25

If a negative relation exists between two variables, low scores on one variable will be associated with high scores on the other variable.

In a negative relationship between two variables, a decrease in one variable is accompanied by an increase in the other variable. This means that as scores on one variable decrease (i.e., low scores), the scores on the other variable tend to increase (i.e., high scores). The negative relationship implies an inverse pattern where the variables move in opposite directions.

The exact nature and strength of the negative relationship can vary, but the general trend is that low scores on one variable correspond to high scores on the other variable. This negative association provides insights into the behavior and interactions between the variables being examined.

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1st one is B

2nd one C

By applying the congruency of triangles, it can be concluded that ΔCDE ≅ ΔFDE by AAS theorem

Two triangles are said to be congruent if all three corresponding sides are equal and all three corresponding angles are equal in measure. The symbol of congruence is ≅.

There are some postulates used to prove congruency:

Now we take a look at the given picture, the take some notes:

∠C = ∠F ----> corresponding angles

∠DEC = ∠DEF  ----> corresponding angles

DE = DE  ----> adjacent sides

Thus ΔCDE ≅ ΔFDE by AAS postulate

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Using the definition of covariance, Cov(Y(1)Y(2))= 0. From the given information the two variables Y(1), and Y(2), are dependent and, p(y(1)y(2)) ≠ P(y(1))p(y(2)). So, it can be concluded that Uncorrelated variables need not be independent.

Given joint probability function of Y(1) and Y(2), is

p(Y(1)*Y(2)) = 1/3, for (y(1)*y(2)) = (- 1, 0), (0, 1), (1, 0)

So Y(1), takes random variable -1,0,1.

And Y(2) takes random variable 0,1.

Y(2)|Y(1)      -1               0             1

0                  1/3            0             1/3

1                   0               1/3           0

From the table:

The marginal probability function of Y(1) is;

P(Y(1)=y(1)) = ∑P(Y(1)=y(1)), y(1)= -1,0,1. That is;

Y(1)=y(1)                      -1                   0               1

P(Y(1)=y(1))                  1/3                 1/3             1/3

From the definition of expectation,

E[y(1)]= \(\Sigma_{y_{1}}\) y(1)P(Y(1)=y(1))

E[y(1)]= -1(1/3)+0(1/3)+1(1/3)

E[y(1)]= -1/3+0+1/3

E[y(1)]= 0

The marginal probability mass function of y(2) is

P(Y(2)=y(2)) = ∑P(y(1)y(2)), y(2)= 0,1. That is;

Y(2)=y(2)                    0               1

\(P_{Y(2)}\)(y(2))                  2/3             1/3

Now E[y(2)]= \(\Sigma_{y_{2}}\) y(2)P(Y(2)=y(2))

E[y(2)]= 0(2/3)+1(1/3)

E[y(2)]= 1/3

And

E[y(1)y(2)]= \(\Sigma_{y_{1}}\Sigma_{y_{2}}\) y(1)y(2)P(y(1)y(2))

E[y(1)y(2)]= -1(0)(1/3)+0(1)(1/3)+1(0)(1/3)

E[y(1)y(2)]= 0

From the definition of covariance.

The Cov(Y(1)Y(2))= E[Y(1)Y(2)]-E[Y(1)E[Y(2)]

Cov(Y(1)Y(2))= 0-1/3 (0)

Cov(Y(1)Y(2))= 0

The Cov(Y(1)Y(2))=0, implies that the two variables are uncorrelated. But from the given information the two variables Y(1), and Y(2), are dependent and, p(y(1)y(2)) ≠ P(y(1))p(y(2))

Hence, it can be concluded that Uncorrelated variables need not be independent.

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The phrase "number of values for which a certain condition holds" is referred to as multiplicity. The phrase, for instance, can be used to describe the magnitude of the totient valence function or the frequency with which a given polynomial equation has a root at a specific location.

Let z_0 be a root of a function f, and let n be the least positive integer n such that f^((n))(z_0)!=0. Then the power series of f about z_0 begins with the nth term,

f(z)=sum_(j=n)^infty1/(j!)(partial^jf)/(partialz^j)|_(z=z_0)(z-z_0)^j,

and f is said to have a root of multiplicity (or "order") n. If n=1, the root is called a simple root '

The multiplicity of a member of a multiset in mathematics is the number of times the member appears in the multiset. The multiplicity of a root, for instance, is how many times a given polynomial has a root at a particular point.

It's crucial to understand the concept of multiplicity in order to correctly count without mentioning exceptions (for example, double roots counted twice). Thus, "counted with multiplicity" is used.

This can be highlighted by counting the number of different elements, as in "the number of separate roots," if multiplicity is disregarded. However, multiplicity is always taken into account when a set (as opposed to a multiset) is established, therefore the word "different" is not necessary.

Hence ,Multiplicity means the quality or state of being multiple or various. and

the number of components in a system (such as a multiplet or a group of energy levels)

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Let z_0 be a root of a function f, and let n be the least positive integer n such that f^((n))(z_0)!=0. Then the power series of f about z_0 begins with the nth term,

f(z)=sum_(j=n)^infty1/(j!)(partial^jf)/(partialz^j)|_(z=z_0)(z-z_0)^j,

and f is said to have a root of multiplicity (or "order") n. If n=1, the root is called a simple root '

The multiplicity of a member of a multiset in mathematics is the number of times the member appears in the multiset. The multiplicity of a root, for instance, is how many times a given polynomial has a root at a particular point.

Answer:1.233

Step-by-step explanation:

Answer:

5^-18

Step-by-step explanation:

here is the answer please mark me as the brainliest

Answer:

30 feet

Step-by-step explanation:

The area is found by multiplying the base and the height. So to find the answer, you divide 1,800 by 60 to get 30.

The error term is a random variable with an expected value of zero.2. The error term is normally distributed

The assumptions underlying the simple linear regression model `y = Bo + B1x + e` are: 1.

The variance of the error term e is constant across all values of x.Thus, the assumptions that are underlying the simple linear regression model `y = Bo + B1x + e` are the second and the third options, which are "The error term is normally distributed." and "The variance of the error term e is constant across all values of x." respectively.

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(A) The probability that 34 jets on the given runaway total taxi and takeoff time will be between 275 and 320 minutes is 0.7490.

(B) The probability that 34 jets on a given runaway total taxi and takeoff time will be more than 275 minutes is 0.9278.

(C)  The probability that 34 Jets on the given runaway total taxi and take off time will be between 275 and 320 minutes0.7490

We are given that X, the total taxi and takeoff time for commercial jets, has a mean of μ = 8.9 and a standard deviation of σ = 3.5. We can use this information to answer the following questions:

The total taxi and takeoff time for 34 jets can be modeled as the sum of 34 independent and identically distributed random variables with mean μ = 8.9 and standard deviation σ = 3.5.

According to the central limit theorem, the distribution of this sum will be approximately normal with a mean of μn = 8.934 = 302.6 and a standard deviation of\(\sigma\sqrt{(n)} = 3.5\sqrt{t(34)} = 18.89.\)

Therefore, we want to find P(X < 320), where X ~ N(302.6, 18.89). Converting to standard units, we have:

z = (320 - 302.6) / 18.89 = 0.92.

Using a standard normal table or calculator, we find that P(Z < 0.92) = 0.8212.

Therefore, the probability that 34 jets on the runaway total taxi and takeoff time will be less than 320 minutes is 0.8212.

Again, the total taxi and takeoff time for 34 jets can be modeled as the sum of 34 independent and identically distributed random variables with mean μ = 8.9 and standard deviation σ = 3.5.

The distribution of this sum will be approximately normal with a mean of μn = 302.6 and a standard deviation of σsqrt(n) = 18.89.

Therefore, we want to find P(X > 275), where X ~ N(302.6, 18.89). Converting to standard units, we have:

z = (275 - 302.6) / 18.89 = -1.46

Using a standard normal table or calculator, we find that P(Z > -1.46) = 0.9278.

Therefore, the probability that 34 jets on a given runaway total taxi and takeoff time will be more than 275 minutes is 0.9278.

This probability can be found by subtracting the probability in part (A) from the probability in part (B):

P(275 < X < 320) = P(X < 320) - P(X < 275)

= 0.8212 - (1 - 0.9278)

= 0.7490.

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Hoping it helps...

Have a nice day!

It's B.

You can think of the action of multiplying matrix A onto any other matrix X as

• preserving the first row of X due to the +1 in the first column of the first row,

• negating the second row of X due to the -1 in the second column of the second row, and

• preserving the third row of X due to the +1 in the third column of the third row.

Answer: B.

Step-by-step explanation: I got this right on Edmentum.

The correct graph would be a straight line with a slope of 45 and passing through the origin (0, 0).

We have,

The graph that models the cost, y, in cents of making x minutes of international calls would be a linear graph, as the cost is directly proportional to the number of minutes.

The equation of the linear graph would be:

y = 45x

Here, y represents the cost in cents, and x represents the number of minutes.

Therefore,

The correct graph would be a straight line with a slope of 45 and passing through the origin (0, 0).

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The range of the function c = 50t + 200 is given by [200, ∞).

A linear equation is in the form:

y = mx + b

Where y, x are variables, m is the rate of change and b is the y intercept.

Let c represent the distance travelled in t hours, hence:

c = 50t + 200

The range of the function c = 50t + 200 is given by [200, ∞).

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To solve this problem, we'll use the concept of permutations.

First, we need to choose a woman for the position of president, then another woman for the position of vice president, and finally, a man for the treasurer position.

1. President: Since there are 20 women, we have 20 options for the president position.
2. Vice President: We're left with 19 women (since we already chose one for the president), so we have 19 options for the vice president position.
3. Treasurer: Since there are 17 men, we have 17 options for the treasurer position.

Now, multiply the number of options for each position together to find the total number of ways to form the committee:

20 (president) × 19 (vice president) × 17 (treasurer) = 6,460 ways.

So, there are 6,460 possible ways to choose three different members with women as president and vice president and a man as treasurer.

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Answer:

Yeah, math math wu wu

Step-by-step explanation:

Answer:

Step-by-step explanation:

We can solve this with some pretty simple substitution

First write that a=2b+8

and our reasoning for this is that we added 2b from the first equation

Then write

4(2b+8)+3b=21

After we figured out what A was in the previous part we are able to substitute that into our other equation

Then you solve for b from here

8b+32+3b=21

11b=-11

b= -1

If you have any questions leave them below

Answer:

5. 23

6. 26

7. 20

8. 45

9. 15

10. 120

11. 60 full triangle is 180 and since it is equilateral you divide it by 3 you get 60

12. It is not possible. if you use the pyagorean theorem (not sure on spelling of that?) you get 16 so its not possible for it to be 12.

Answer: 720

Step-by-step explanation:

there are 6 letters in "choice"

and they must be permuted into 6 word letters:

6P6 = 720 distinct possibilities

Because the answer to X is a negative number, and negative numbers are needed for negative answers

Answer: because it won’t make sense if it was 74y it has to have an equation or expression.

Step-by-step explanation: I’m not that smart and I have no friends on brainly but hope it helps and makes your day.

The price of the bond on the 12th of August 2025 is $1,100,973.88 and it is being trading at a premium.

The market value of a bond can be calculated using the following formula:

Bond Price = (C ÷ r) x (1 - (1 ÷ (1 + r) ^ n)) + F ÷ (1 + r) ^ n

Where,C = Coupon payment, r = market yield or interest rate, n = number of payment periods, F = Face value of the bond

Given:

Par value of the bond = $1,500,000, Number of years to maturity = 10, Coupon rate = 3.4%, Frequency of coupon payments = Quarterly.

The coupon payment at 3.4% per annum is paid quarterly, therefore:

Coupon payment = 3.4% ÷ 4 = 0.85% = $12,750 per coupon period

Since the coupons are paid quarterly, the number of coupon periods for 10 years is:

10 years x 4 quarters per year = 40 coupon periods

The market yield of the bond is 4.5% per annum, therefore, the interest rate for each coupon period is:

4.5% ÷ 4 = 1.125%

The price of the bond can now be calculated as follows:

Bond Price = (C ÷ r) x (1 - (1 ÷ (1 + r) ^ n)) + F ÷ (1 + r) ^ n=

($12,750 ÷ 1.125%) x (1 - (1 ÷ (1 + 1.125%) ^ 40)) + $1,500,000 ÷ (1 + 1.125%) ^ 40

= $1,100,973.88

Therefore, the price of the bond on August 12, 2025, is $1,100,973.88.

The bond is trading at a premium because the market yield is lower than the coupon rate, indicating that the bond is attractive to investors.

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Answer:50/100

Step-by-step explanation: 50/100 simplifies into 5/10, meaning they equal each other. 50 is half of 100, and 5 is half of 10.

The accurate answer is:

A. A=[0 0; -9 4], B=[0; 8], C=[1 0; 0 -3;], and D=[-9 0; 0 -3]

Explanation:

A matrix represents the coefficients of the state variables in the state-space equations. Based on the given state-variable models, we have x·1 = -9x1 + 4x2 and x·2 = -3x2 + 8u. Therefore, the matrix A would be [0 0; -9 4], representing the coefficients of x1 and x2 in the state equations.

B matrix represents the coefficients of the input variable (u) in the state-space equations. Based on the given state-variable models, we have x·1 = -9x1 + 4x2 and x·2 = -3x2 + 8u. Therefore, the matrix B would be [0; 8], representing the coefficient of u in the state equations.

C matrix represents the coefficients of the state variables in the output equation. Based on the given state-variable models, the outputs are x1 and x2. Therefore, the matrix C would be [1 0; 0 -3], representing the coefficients of x1 and x2 in the output equations.

D matrix represents the coefficients of the input variable (u) in the output equation. Based on the given state-variable models, the outputs are x1 and x2, and there is no direct dependence on the input u in the output equations. Therefore, the matrix D would be [0 0; 0 0], representing no direct dependence of u in the output equations.

Answer:

4 < x < 36;  values that apply are 5, 16, 20, 35

Step-by-step explanation:

use triangle inequality theorem:

the sum of any two sides is greater than the third

16 + x > 20;   x > 4

16 + 20 > x;   x < 36  

Coterminal angles are those whose ultimate positions are identical but whose number of full revolutions varies.

No, coterminal angles have the same angle measure. Two angles are coterminal if they have the same initial side and the same terminal side, meaning that they differ by an integer multiple of 360 degrees (or 2π radians) or by a multiple of 360 degrees plus or minus any number of full rotations (i.e., adding or subtracting 360 degrees or 2π radians any number of times).

For example, 30 degrees and 390 degrees are coterminal angles because 390 - 30 = 360, which is a multiple of 360 degrees. Similarly, 45 degrees and 405 degrees are coterminal because 405 - 45 = 360

However, two angles that differ by a non-integer multiple of 360 degrees are not coterminal. For example, 30 degrees and 390.5 degrees are not coterminal because they differ by 360.5 degrees, which is not a multiple of 360 degrees.

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Using the Laplace transform to solve the given integral equation  f(t) = t, 0 ≤ t < 4 0, t ≥ 4 is \(\frac{4(1-2e^-5s)/}{s}\)

explanation is given in the image below:

Laplace remodel is an crucial remodel approach that's particularly beneficial in fixing linear regular equations. It unearths very wide programs in var- areas of physics, electrical engineering, manipulate, optics, mathematics and sign processing.

The Laplace transform technique, the function inside the time domain is transformed to a Laplace feature within the frequency area. This Laplace function could be inside the shape of an algebraic equation and it may be solved without difficulty.

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The conclusion that is appropriate at a 5% level of significance is this:C. There are statistically significant differences in the mean time to recurrence of depression symptoms for patients in the three treatment groups. this suggests that there is a treatment effect.

The correct conclusion is that the result obtained from the analysis is statistically significant, so the null hypothesis can be rejected. This also means that there are 1 in 20 chances of obtaining an error.

So, for a study checking the relationship between the mean time to recurrence of depression symptoms, the 5% level of significance would demonstrate a relationship.

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Answer:

12 years

Step-by-step explanation: