3(x + 2) + 4(x - 5) = 10
solve x

3(x + 2) = 12
solve x

7(3 - x) = 8(4 - 2x)
solve x

8(x + 1) - 3(x + 4) = 7(2 - x)
solve x

7(x + 2) = 6(x + 5)
solve x

4(x + 2) = 48
siplfy
5x + 2(x - 3) = -2(x - 1)

Answer:

True

Step-by-step explanation:

With Sin and Cos, the rule is that the opposites are the same:

SinA=CosB

CosB=SinA

In this problem, it is showing SinA=cosB, so it is the same, it is true.

Hope this helps!

The probability that none of the packs are defective is 0.478. The probability that five packs are defective is 0.000455.The probability that all the packs are defective is 2.8243e-14.The probability that 7 or more packs are defective is 0.00416 (approx).

A shipment of 10,000 Karak tea packs is produced by the company Chai Karak. If 7% of the packs are defective, what is the probability that: none of the packs are defective, five packs are defective, all the packs are defective, and seven or more packs are defective?  The number of trials, n, is 10 and the probability of a defective tea pack is 0.07. Therefore, the number of successful trials, X, follows a binomial distribution. Formula for binomial distribution: P(X = k) = nCk × pk × (1 − p)n−kWhere nCk = number of combinations of n things taken k at a time = n! / (k! (n-k)!)a. The probability that none of the packs are defective P(X = 0):P(X = 0) = nC0 * p0 * (1-p)n-0= 10C0 * 0.07^0 * (1-0.07)^10= 1 * 1 * 0.478= 0.478Therefore, the probability that none of the packs are defective is 0.478.

The probability that 5 packs are defective:P(X = 5) = nC5 * p^5 * (1-p)n-5= 10C5 * 0.07^5 * (1-0.07)^5= 252 * 0.0000028 * 0.649= 0.000455Therefore, the probability that five packs are defective is 0.000455.

The probability that all the packs are defective:P(X = 10) = nC10 * p^10 * (1-p)n-10= 10C10 * 0.07^10 * (1-0.07)^0= 1 * 2.8243e-14 * 1= 2.8243e-14Therefore, the probability that all the packs are defective is 2.8243e-14.

The probability that 7 or more packs are defective: P(X ≥ 7) = P(X = 7) + P(X = 8) + P(X = 9) + P(X = 10)P(X = 7) = nC7 * p^7 * (1-p)n-7= 10C7 * 0.07^7 * (1-0.07)^3= 120 * 0.0000953677 * 0.657= 0.00416P(X = 8) = nC8 * p^8 * (1-p)n-8= 10C8 * 0.07^8 * (1-0.07)^2= 45 * 0.0000024969 * 0.859= 0.000011P(X = 9) = nC9 * p^9 * (1-p)n-9= 10C9 * 0.07^9 * (1-0.07)^1= 10 * 0.00000005 * 0.93= 4.65e-7P(X = 10) = nC10 * p^10 * (1-p)n-10= 10C10 * 0.07^10 * (1-0.07)^0= 1 * 2.8243e-14 * 1= 2.8243e-14P(X ≥ 7) = 0.00416 + 0.000011 + 4.65e-7 + 2.8243e-14= 0.00416Therefore, the probability that 7 or more packs are defective is 0.00416 (approx).

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Answer:

0.005719

Step-by-step explanation:

H0 : μ = 26

H1 : μ < 26

Given that:

Population mean, μ = 26

Sample mean, xbar = 25

Sample standard deviation, s = 2.5

Sample size, n = 40

The test statistic :

(xbar - μ) / s/sqrt(n)

Test statistic :

(25 - 26) / 2.5/sqrt(40)

-1 / 0.3952847

Test statistic = - 2.5298

Using a Pvalue calculator from Z test statistic:

Pvalue = 0.005719

Answer:

43 feet would be the correct answer.

Step-by-step explanation:

Answer:

a=1

Step-by-step explanation:

Answer:

poopy

Step-by-step explanation:

Answer:

h(t)=0.5at^2+v+5

Step-by-step explanation:

If you know how to do math you should know

Answer:

Step-by-step explanation:

c²  =  a² + b²

c   =  √(a² + b²)

    =  √(9² + 3²)

    =  √(81 + 9)  

c   =  √90             = √(9)(10)       =   3√10  simplified

Answer:

option c

Step-by-step explanation:

first use angle sum property, then use

linear pair axiom

Answer:

D: 121

Step-by-step explanation:

Step One: To do this problem, you must understand the rule that all angles in a triangle add up to 180 degrees.

Step Two: Now that we know the rule, we know this triangle equals 180 degrees. First, we must add the angles we DO know together: 26+33= 59.  

Step Three: We can subtract 59 from 180 to find the missing angle: 180-59= 121.

Answer:

There should be 18 blue marbles in the representative sample.

Step-by-step explanation:

Given that a bag contains 9 white and 54 blue marbles, if a representative sample contains 3 white marbles, to determine how many blue marbles would you expect to contain the following calculation must be performed:

9 = 3

54 = X

54 x 3/9 = X

162/9 = X

18 = X

Therefore, there should be 18 blue marbles in the representative sample.

The perimeter of the shape is approximately 44.00 feet, and the area is approximately 160.50 square feet.

To determine the perimeter of the shape, we add up the lengths of all its sides. The given sides are 4 ft, 16.5 ft, 4 ft, and 20 ft. Adding these lengths together, we get a perimeter of 44.5 ft. However, since we are asked to round to the nearest hundredth, the perimeter becomes approximately 44.00 feet.

To find the area of the shape, we need to know its specific shape. Since the given measurements do not provide enough information, it is not possible to accurately determine the area. In order to calculate the area, we need to know the shape's dimensions, angles, or additional side lengths. Without this information, we cannot determine the area accurately.

In conclusion, the perimeter of the shape is approximately 44.00 feet, but the area cannot be determined without further information.

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To calculate the given volume integral, 4(x^3 + xy^2) dV, over the solid E in the first octant beneath the paraboloid z = 1 - x^2 - y^2, we need to set up the integral in cylindrical coordinates. The integral will involve integrating over the appropriate limits and applying the volume element in cylindrical coordinates.

In cylindrical coordinates, we have x = r cos θ, y = r sin θ, and z = z.
The equation of the paraboloid, z = 1 - x^2 - y^2, can be expressed as z = 1 - r^2.
The given volume integral becomes 4(x^3 + xy^2) dV = 4(r^3 cos^3 θ + r^3 cos θ sin^2 θ) r dz dr dθ.
To determine the limits of integration, we need to consider the region of the solid E in the first octant. Since the solid lies beneath the paraboloid z = 1 - x^2 - y^2, the upper limit for z is given by z = 1 - r^2.
The limits for r and θ depend on the region in the first octant. We need to set appropriate limits to cover the desired region.Once we have the limits for r, θ, and z, we can set up the triple integral using the volume element in cylindrical coordinates.
By evaluating the integral with the corresponding limits, we can find the value of the given volume integral over the solid E in the first octant beneath the paraboloid.

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Answer:

18 in

Step-by-step explanation:

Answer:

exponential

Step-by-step explanation:

since the rate of our population growth is relative to the amount of people already on our planet, the numbers will grow exponentially

Answer: exponential

Step-by-step explanation: exponential function don’t grow at a constant rate, like the world population

(i) Estimate X given that Y = 0.5 and Z = -3 using an unbiased minimum variance estimator.

Estimate of X given that Y = 0.5 and Z = -3 can be obtained by applying the conditional expectation formula, E[X|Y=y, Z=z], where y=0.5 and z=-3.E[X|Y=y, Z=z] = E[X] + Cov[X,Y]/Var[Y] * (Y - E[Y]) + Co v[X,Z]/Var[Z] * (Z - E[Z])E[X|Y=0.5, Z=-3] = 0 + (0/10) * (0.5 - 1) + (1/2) * (-3 - 2) = -2, which is the unbiased minimum variance estimator.(ii) Determine the variance of the above estimator.

The variance of the unbiased minimum variance estimator is given by Var[X|Y=y, Z=z] = Var[X] - Cov[X,Y]^2/Var[Y] - Cov[X,Z]^2/Var[Z] + 2Cov[X,Y]Cov[X,Z]/(Var[Y]*Var[Z])Var[X|Y=0.5, Z=-3] = 10 - 0^2/10 - 1^2/2 + 2(0)(1)/(10*2) = 9.75 (b)

Intelli Moto is car manufacturer that produces vehicles equipped with a fault detection system that uses information from various sensors to inform the driver about possible faults in the braking system. The system diagnoses faults correctly with probability 99%, but gives false alarms with probability 2%. It is known that such faults occur with probability 0.05%. If the system diagnoses a fault, what is the probability a fault has actually occured?

The probability of a fault actually occurring is P(Fault) = 0.05%, which is the prior probability of a fault.

The probability of a correct diagnosis is P(Diagnosis | Fault) = 99%, which is the probability of a positive test result given that a fault has actually occurred.

The probability of a false alarm is P(Diagnosis | No Fault) = 2%, which is the probability of a positive test result given that no fault has actually occurred.

The probability of a positive test result isP(Diagnosis) = P(Fault)*P(Diagnosis | Fault) + P(No Fault)*P(Diagnosis | No Fault)= 0.05% * 99% + 99.95% * 2% = 2.039%.The probability of a fault given a positive test result can be obtained by Bayes' theorem,P(Fault | Diagnosis) = P(Diagnosis | Fault)*P(Fault)/P(Diagnosis)= 99% * 0.05% / 2.039% = 2.43%, which is the probability a fault has actually occurred given that the system diagnoses a fault.

The probability that a fault has actually occurred given that the system diagnoses a fault is 2.42%.

(i) To estimate X given that Y = 0.5 and Z = -3 using an unbiased minimum variance estimator, we need to determine the distribution of X | Y = 0.5, Z = -3 and use the formula for conditional expectation of a jointly normally distributed random variable. The distribution of X | Y = 0.5, Z = -3 is also normal since it is a conditional distribution of a jointly normally distributed random variable. To find the mean of the distribution, we use the formula for conditional expectation:
[tex]E[X | Y = 0.5, Z = -3] = E[X] + Cov[X, Y | Z = -3] (Y - E[Y | Z = -3]) / Var[Y | Z = -3] + Cov[X, Z | Y = 0.5] (Z - E[Z | Y = 0.5]) / Var[Z | Y = 0.5][/tex]

where Cov[X, Y | Z = -3] is the conditional covariance of X and Y given Z = -3,

E[Y | Z = -3] is the conditional mean of Y given Z = -3,

Var[Y | Z = -3] is the conditional variance of Y given Z = -3,

Cov[X, Z | Y = 0.5] is the conditional covariance of X and Z given Y = 0.5,

and E[Z | Y = 0.5] and Var[Z | Y = 0.5] are the conditional mean and variance of Z given Y = 0.5 respectively.

We are given that

E[X] = 0, E[Y] = 1, E[Z] = 2,

Var[X] = 10, Var[Y] = 2, Var[Z] = 1,

and Cov[X, Y] = Cov[X, Z] = Cov[Y, Z] = 0.

Also, Y = 0.5 and Z = -3.

Hence, we have:

[tex]Cov[X, Y | Z = -3] = Cov[X, Y] / Var[Z] = 0[/tex],

[tex]E[Y | Z = -3] = E[Y] =[/tex]1,

[tex]Var[Y | Z = -3] = Var[Y] = 2[/tex],

[tex]Cov[X, Z | Y = 0.5] = Cov[X, Z] / Var[Y] = 0[/tex].

The conditional mean of Z given Y = 0.5 is given by

[tex]E[Z | Y = 0.5] = E[Z] + Cov[Y, Z] (Y - E[Y]) / Var[Y] = 2 + 0.5 (0 - 1) / 2 = 1.5.[/tex]

The conditional variance of Z given Y = 0.5 is given by

[tex]Var[Z | Y = 0.5] = Var[Z] - Cov[Y, Z]^2 / Var[Y] = 1 - 0^2 / 2 = 1[/tex].

Hence, the mean of the distribution of X | Y = 0.5, Z = -3 is:
[tex]E[X | Y = 0.5, Z = -3] = 0 + 0 (0.5 - 1) / 2 + 0 (-3 - 1.5) / 1 = -0.75[/tex]

To find the variance of the unbiased minimum variance estimator, we use the formula for conditional variance of a jointly normally distributed random variable:
[tex]Var[X | Y = 0.5, Z = -3] = Var[X] - Cov[X, Y | Z = -3]^2 / Var[Y | Z = -3] - Cov[X, Z | Y = 0.5]^2 / Var[Z | Y = 0.5][/tex]

where Var[X], Cov[X, Y | Z = -3], and Cov[X, Z | Y = 0.5] are given above,

and Var[Y | Z = -3] and Var[Z | Y = 0.5] are calculated as follows:
[tex]Var[Y | Z = -3] = Var[Y] - Cov[X, Y]^2 / Var[Z] = 2 - 0^2 / 1 = 2Var[Z | Y = 0.5] = Var[Z] - Cov[Y, Z]^2 / Var[Y] = 1 - 0^2 / 2 = 1[/tex]

Hence, we have:
[tex]Var[X | Y = 0.5, Z = -3] = 10 - 0^2 / 2 - 0^2 / 1 = 10[/tex]

(ii) The variance of the unbiased minimum variance estimator is Var[X | Y = 0.5, Z = -3] = 10.

(b) Let A denote the event that a fault has actually occurred, D denote the event that the system diagnoses a fault,

P(A) = 0.05%, P(D | A) = 99%, and P(D | A') = 2%, where A' is the complement of A.

We need to find P(A | D), the probability that a fault has actually occurred given that the system diagnoses a fault.

By Bayes' theorem, we have:
[tex]P(A | D) = P(D | A) P(A) / P(D)[/tex]

where P(D) is the total probability of the system diagnosing a fault, which is:
[tex]P(D) = P(D | A) P(A) + P(D | A') P(A') = 0.99 (0.0005) + 0.02 (1 - 0.0005) = 0.0205[/tex]

Hence, we have:
[tex]P(A | D) = 0.99 (0.0005) / 0.0205 = 0.0242[/tex] or 2.42%

Therefore, the probability that a fault has actually occurred given that the system diagnoses a fault is 2.42%.

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The probability of getting a negative result is 0.175 or 17.5%.

To calculate the probability of getting a negative result, we need to consider the sensitivity and specificity of the diagnostic test.

Given that the prevalence of the condition is 12.5%, we can assume that 12.5% of the population has the condition, and the remaining 87.5% does not.

The sensitivity of the test is 80%, which means that it correctly identifies 80% of the individuals with the condition as positive.

The specificity of the test is 95%, which means that it correctly identifies 95% of the individuals without the condition as negative.

To calculate the probability of getting a negative result, we need to consider both the true negative rate (1 - sensitivity) and the proportion of individuals without the condition (1 - prevalence).

Probability of getting a negative result = (1 - sensitivity) × (1 - prevalence)

= (1 - 0.80) × (1 - 0.125)

= 0.20 * 0.875

= 0.175

Therefore, the probability of getting a negative result is 0.175 or 17.5%.

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In the given circuit, there are two resistors, R1 and R2, both with a resistance of 4 Ω. The circuit analysis and determination of various electrical quantities, such as current and voltage, can be performed based on this information.

The circuit consists of two resistors, R1 and R2, each with a resistance of 4 Ω. To analyze the circuit and determine various electrical quantities, we can apply principles such as Ohm's law and Kirchhoff's laws.

Firstly, we can calculate the total resistance in the circuit by combining the resistors in series or parallel based on their connection. If R1 and R2 are in series, their total resistance, RT, would be the sum of their individual resistances, i.e., RT = R1 + R2 = 8 Ω.

Once we know the total resistance, we can use Ohm's law (V = IR) to calculate the voltage across the resistors or the current flowing through them. If the circuit is connected to a power source or a given voltage, we can determine the current flowing through the resistors using Ohm's law and Kirchhoff's laws.

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Edge set of the graph with vertices F, G, H, I, J, K, and L that has an Euler trail, the degree of I is 1 and the degree of K is 3 is: {(F, H), (G, H), (G, I), (H, J), (I, K), (J, K), (K, L), (K, F)}.

To construct a simple graph with vertices F, G, H, I, J, K, L that has an Euler trail, the degree of I is 1 and the degree of K is 3 and the corresponding edge set, we can follow this method

1: Draw the vertices of the graph. We have 7 vertices, F, G, H, I, J, K, and L.

2: Draw edges between the vertices to form the graph. Since the degree of I is 1 and the degree of K is 3, we can connect I to some other vertex and K to three other vertices.

3: Check if the graph has an Euler trail. A graph has an Euler trail if all vertices have an even degree or if exactly two vertices have an odd degree. Here, the degree of I is 1 and the degree of K is 3, so the graph has two vertices with odd degrees. Therefore, the graph has an Euler trail.

4: Write down the edge set. The edge set of the graph can be written as follows: {(F, H), (G, H), (G, I), (H, J), (I, K), (J, K), (K, L), (K, F)}

5. Here's one possible graph that satisfies the conditions:

  I

   /

F--G--H--J

   \

    K--L

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Answer:

O-O

Step-by-step explanation:

XD i took the last comment

Answer:

its D

Step-by-step explanation:

The discount amount is $331.80. The price after discount is $616.20. The sales tax amount is $56.63. The final price is $672.83.

A TV originally priced at $948 is on sale for 35% off. We are to find the discount amount and the price after discount.

The original price of the TV = $948

The percentage discount = 35%.

Let X be the price after discount.

We can find X as follows:

Discount = 35% of original price

= 35% of 948= (35/100) × 948= $331.80

Price after discount (X) = Original price - Discount

= $948 - $331.80= $616.20

Therefore, the price after discount is $616.20.

Now we are to find the tax amount and the final price after including the discount and sales tax.

The sales tax is 9.2%.

We can find the tax amount as follows:

Tax amount = 9.2% of price after discount

= 9.2% of $616.20= (9.2/100) × 616.20= $56.63

Now, the final price after including the discount and sales tax = Price after discount + Tax amount

= $616.20 + $56.63= $672.83

Therefore, the final price after including the discount and sales tax is $672.83.

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Answer:

50% increase

Step-by-step explanation:

The percentage change in volume from cylinder A to cylinder B is 50% volume increased by 50% option (C) is correct.

In geometry, it is defined as the three-dimensional shape having two circular shapes at a distance called the height of the cylinder.

We know the formula for the volume of the cylinder is given by:

[tex]\rm V = \pi r^2h[/tex]

We have r = 10 inches and h = 5 inches

[tex]\rm V = \pi \times10^2\times5[/tex]

V = 500π cubic inches

Percent change in volume from cylinder A to cylinder B:

[tex]=\rm \frac{750\pi-500\pi}{500\pi} \times100[/tex]

= 50%

Thus, the percentage change in volume from cylinder A to cylinder B is 50% volume increased by 50% option (C) is correct.

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Answer:

3 to 4

Step-by-step explanation:

12÷4=3

16÷4=4

so that makes it 3 to 4

The answer is A. 2pi/3 radians.

Answer:

First Option

Explanation:

A)  2pi/3 radians

Hope this helps :)

Hi

The answer would be =

Why?

The `| |` symbols mean actual value, which makes every number inside of it positive. That would mean, following the rules of actual value, the equation would look like:

5 + 2 _____ 5 + 2

Both add up to 10, so both equations are equal.

Answer:

>

Step-by-step explanation:

(4)| -5 + (-2)|  ____ |5 + 2|

         (4)| -7|  ____ |7|

            (4)(7) ____ 7

                28     >   7

The null hypothesis is not rejected since the p-value is higher than or equal to the level of significance.

We assess the statistical conclusion based on the given data by comparing the p-value to the level of significance ().

Given: ZSTAT is 1.88 and p-value is 0.9699.

Level of significance () = 0.10

In a one-tail hypothesis test, the null hypothesis is only rejected in the lower tail if the p-value is less than the level of significance ().

The p-value is greater in this instance (0.9699) than the level of significance (0.1).

Therefore, the proper reaction is

C. The null hypothesis is not rejected since the p-value is higher than or equal to the level of significance.

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