3) angle of an isosceles triangle is 70°, find the value of If one remaining angles. a) 40°, 40° b) 45°, 45° c) 40°70° d)50⁰, 50⁰​

(a) The solutions to the equation 2sin(3θ) + 1 = 0 are θ = (π/9) + (2πk/3) or θ = (8π/9) + (2πk/3), where k is any integer.

(b) The solutions in the interval [0, 2π) are θ = π/9, 5π/9.

The given equation is 2sin(3θ) + 1 = 0. To solve for θ, we can start by isolating sin(3θ) by subtracting 1 from both sides and dividing by 2, which gives sin(3θ) = -1/2.

Using the unit circle or a trigonometric table, we can find the solutions of sin(3θ) = -1/2 in the interval [0, 2π) to be θ = π/9 + (2π/3)k or θ = 5π/9 + (2π/3)k, where k is any integer. These are the solutions for part (a).

For part (b), we are asked to find the solutions in the interval [0, 2π). To do this, we simply plug in k = 0, 1, and 2 to the solutions we found in part (a), and discard any values outside the interval [0, 2π).

Thus, the solutions in the interval [0, 2π) are θ = π/9 and θ = 5π/9.

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None of the sets of measurements given could be the side lengths of a right triangle.

A right triangle is a type of triangle that has a 90-degree angle. The side opposite the right angle is called the hypotenuse, while the other two sides are called the legs.

To determine whether a set of measurements could be the side lengths of a right triangle, we can use the Pythagorean Theorem, which states that the sum of the squares of the two legs of a right triangle is equal to the square of the hypotenuse.

In other words, a² + b² = c², where a and b are the lengths of the legs, and c is the length of the hypotenuse. Using this theorem, we can check which set of measurements could form the sides of a right triangle.

Let's check each option:

5, 8, 12

a = 5,

b = 8,

c = 12

a² + b² = 5² + 8²

= 25 + 64

= 89

c² = 12²

= 14489 ≠ 144

∴ 5, 8, 12 are not the side lengths of a right triangle

14, 48, 50

a = 14,

b = 48,

c = 50

a² + b² = 14² + 48²

= 196 + 2304

= 2508

c² = 50²

= 250089 ≠ 2500

∴ 14, 48, 50 are not the side lengths of a right triangle

3, 5, 6

a = 3,

b = 5,

c = 6

a² + b²

= 3² + 5²

= 9 + 25

= 34

c² = 6²

= 3634 ≠ 36

∴ 3, 5, 6 are not the side lengths of a right triangle

8, 13, 15

a = 8,

b = 13,

c = 15

a² + b² = 8² + 13²

= 64 + 169

= 233

c² = 15²

= 225233 ≠ 225

∴ 8, 13, 15 are not the side lengths of a right triangle

Therefore, none of the sets of measurements given could be the side lengths of a right triangle.

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Yes, the least squares line can be used to predict the yield for a pH of 5.5. To predict the yield using the least squares method, follow these steps:

1. Obtain the data points (pH and yield) and calculate the mean values of pH and yield.
2. Calculate the differences between each pH value and the mean pH value, and each yield value and the mean yield value.
3. Multiply these differences and sum them up.
4. Calculate the squares of the differences in pH values and sum them up.
5. Divide the sum of the products from step 3 by the sum of the squared differences from step 4. This gives you the slope of the least squares line.
6. Calculate the intercept of the least squares line using the formula: intercept = mean yield - slope * mean pH.
7. Finally, use the equation of the least squares line (y = intercept + slope * x) to predict the yield at a pH of 5.5.

Please note that you'll need the specific data points to complete these steps and make an accurate prediction for the yield at pH 5.5.

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The length of the pathway that runs along the diagonal of the play area is approximately 36 meters.

Given: Length of the rectangular play area = 30 meters Width of the rectangular play area = 20 meters To find: The length of a pathway that runs along the diagonal of the play area.

Formula to find diagonal of rectangle is as follows:d = √(l² + w²)Where,d = diagonal of the rectangular play areal = length of the rectangular play areaw = width of the rectangular play area.

Substituting the given values in the above formula,d = √(30² + 20²)d = √(900 + 400)d = √1300d = 36.0555 m (approx)

Therefore, the length of the pathway that runs along the diagonal of the play area is approximately 36 meters (rounded to the nearest meter).

Note: Here, we use the square root of 1300 in a calculator to find the exact value of the diagonal and rounded it off to the nearest meter.

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The length of the pathway along the diagonal of the play area is approximately 36 meters.

The length of the pathway that runs along the diagonal of the play area can be found using the Pythagorean theorem. The Pythagorean theorem states that in a right triangle, the square of the hypotenuse (the side opposite the right angle) is equal to the sum of the squares of the other two sides. In this case, the length is the hypotenuse, while the 30-meter side and the 20-meter side are the other two sides.

Applying the Pythagorean theorem, we have:

a2 + b2 = c2

where a = 30 meters and b = 20 meters. Solving for c, the length of the pathway:

c2 = a2 + b2

c2 = 302 + 202

c2 = 900 + 400

c2 = 1300

Next, we take the square root of both sides to find the length of the pathway:

c = √1300

c ≈ √1296

c ≈ 36 meters

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The point of intersection is (0, 4, 4).

To find the point at which the line passing through the points P(1, 0, 6) and Q(5, -1, 5) intersects the plane x*y - z = 7, we can first find the equation of the line and then substitute its coordinates into the equation of the plane to solve for the point of intersection.

The direction vector of the line passing through P and Q is given by:

d = <5-1, -1-0, 5-6> = <4, -1, -1>

So the vector equation of the line is:

r = <1, 0, 6> + t<4, -1, -1>

where t is a scalar parameter.

To find the point of intersection of the line and the plane, we need to solve the system of equations given by the line equation and the equation of the plane:

x*y - z = 7

1 + 4t*0 - t*1 = x   (substitute r into x)

0 + 4t*1 - t*0 = y   (substitute r into y)

6 + 4t*(-1) - t*(-1) = z   (substitute r into z)

Simplifying these equations, we get:

x = -t + 1

y = 4t

z = 7 - 3t

Substituting the value of z into the equation of the plane, we get:

x*y - (7 - 3t) = 7

x*y = 14 + 3t

(-t + 1)*4t = 14 + 3t

-4t^2 + t - 14 = 0

Solving this quadratic equation for t, we get:

t = (-1 + sqrt(225))/8 or t = (-1 - sqrt(225))/8

Since t must be non-negative for the point to be on the line segment PQ, we take the solution t = (-1 + sqrt(225))/8 = 1 as the point of intersection.

Therefore, the point of intersection of the line passing through P and Q and the plane x*y - z = 7 is:

x = -t + 1 = 0

y = 4t = 4

z = 7 - 3t = 4

So the point of intersection is (0, 4, 4).

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Answer:36

Step-by-step explanation:

im done typing the explanations lol

there are good pythagorean theorem calculators, just search for them

the width of the cooler is approximately 18 inches,To find the width of the cooler, we can use the formula for the volume of a rectangular prism:

Volume = Length × Width × Height

Given:
Volume = 7200 in³
Length = 32 in
Height = 12 1/2 in

Let's substitute the given values into the formula and solve for the width:

7200 = 32 × Width × 12.5

To isolate the width, divide both sides of the equation by (32 × 12.5):

Width = 7200 / (32 × 12.5)

Width ≈ 18

Therefore, the width of the cooler is approximately 18 inches, not 120 as mentioned in the question.

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The value of P(X=0, Y=1) is 0.64.

The conditional probability P(X=1 | Y=0) is given as 0.2.

Conditional probability is calculated using the formula:

P(A | B) = P(A and B) / P(B)

We can rearrange the formula to solve for P(X=1 and Y=0).

P(X=1 and Y=0) = P(X=1 | Y=0) * P(Y=0)

We don't have the exact value for P(Y=0), but we can find it by subtracting P(Y=1) from 1, since there are only two possible values for Y (0 or 1) and they are mutually exclusive.

P(Y=0) = 1 - P(Y=1)

We have, P(X=0, Y=0) = 0.2 and P(X=1, Y=1) = 0.1,

we can calculate P(Y=1) as follows:

P(Y=1) = 1 - P(X=0, Y=0) - P(X=1, Y=1)

= 1 - 0.2 - 0.1

= 0.7

Now, we can substitute the values into the formula:

P(X=1 and Y=0) = P(X=1 | Y=0) x P(Y=0)

= 0.2 x (1 - P(Y=1))

= 0.2 x (1 - 0.7)

= 0.2 x 0.3

= 0.06

So, P(X=0, Y=1)

= 0.7- 0.06

= 0.64

Therefore, the value of P(X=0, Y=1) is 0.64.

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To find the singular value decomposition (SVD) of matrix A, we need to find its singular values, left singular vectors, and right singular vectors.

Given matrix A:

A = [1 1 -1; 1 1 -1]

To find the singular values, we first calculate AA':

AA' = [1 1 -1; 1 1 -1] * [1 1; 1 1; -1 -1]

= [3 -1; -1 3]

The singular values of A are the square roots of the eigenvalues of A*A'. Let's find the eigenvalues:

det(A*A' - λI) = 0

(3 - λ)(3 - λ) - (-1)(-1) = 0

(λ - 2)(λ - 4) = 0

λ = 2, 4

The singular values σ1 and σ2 are the square roots of these eigenvalues:

σ1 = √2

σ2 = √4 = 2

To find the left singular vectors u, we solve the equation A'u = σv:

(A*A' - λI)u = 0

For λ = 2:

(1 - 2)x + (-1)x = 0

-1x = 0

x = 0

For λ = 4:

(-1)x + (1 - 4)x = 0

-3x = 0

x = 0

Since both equations result in x = 0, we can choose any non-zero vector as the left singular vector.

Let's choose u1 = [1; 1] as the first left singular vector.

To find the right singular vectors v, we solve the equation Av = σu:

(A*A' - λI)v = 0

For λ = 2:

(1 - 2)y + (1 - 2)y - (-1)y = 0

-2y + 2y + y = 0

y = 0

For λ = 4:

(-1)y + (1 - 4)y - (-1)y = 0

-1y - 3y + y = 0

-3y = 0

y = 0

Again, we have y = 0 for both equations, so we choose any non-zero vector as the right singular vector.

Let's choose v1 = [1; -1] as the first right singular vector.

Now, we can calculate the second left and right singular vectors:

For λ = 2:

(1 - 2)x + (-1)x = 0

-1x = 0

x = 0  For λ = 4:

(-1)x + (1 - 4)x = 0

-3x = 0

x = 0

Again, we have x = 0 for both equations.

Let's choose u2 = [1; -1] as the second left singular vector. For λ = 2:

(1 - 2)y + (1 - 2)y - (-1)y = 0

-2y + 2y + y = 0

y = 0   For λ = 4:

(-1)y + (1 - 4)y - (-1)y = 0

-1y - 3y + y = 0

-3y = 0

y = 0

We have y = 0 for both equations.

Let's choose v2 = [1; 1] as the second right singular vector.

Finally, we can write the singular value decomposition of matrix

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The integral [tex]$\int_0^5 5(x - x^3) dx$[/tex] represents the area under the curve [tex]$y = x - x^3$[/tex] from 0 to 5 i.e., the given statement is true.

In the given definite integral, the integrand [tex]$5(x - x^3)$[/tex] represents the height of infinitesimally small rectangles that are used to approximate the area under the curve. The integral sums up the areas of these rectangles over the interval from 0 to 5, giving us the total area.

To see why this integral represents the area, we can break down the integrand [tex]$5(x - x^3)$[/tex] into two parts: the constant factor 5, which scales the height, and the expression [tex]$(x - x^3)$[/tex], which represents the difference between the function value and the x-axis.

The term [tex]$x - x^3$[/tex] gives us the height of each rectangle, and multiplying it by 5 scales the height uniformly.

By integrating this expression over the interval from 0 to 5, we effectively sum up the areas of these rectangles and obtain the total area under the curve.

Thus, the statement is true, and the integral [tex]$\int_0^5 5(x - x^3) , dx$[/tex] represents the area under the curve [tex]$y = x - x^3$[/tex] from 0 to 5.

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You need at least two umbrellas at each location to keep the probability of getting wet below 0.1 when the probability of rain is 0.6. To calculate the probability that you get wet, we need to consider all possible scenarios. Let's use H to represent the umbrella being at home, O to represent the umbrella being in the office, and R to represent rain.

1. If one umbrella is at home and one is in the office, then you will always have an umbrella with you and won't get wet. This scenario occurs with probability (1-p)*p + p*(1-p) = 2p(1-p).
2. If all four umbrellas are in one place, then you will get wet if it rains and you are at the other location. This scenario occurs with probability p*(1-p)^3 + (1-p)*p^3 = 4p(1-p)^3.
3. If two umbrellas are at one location and none are at the other, then you will get wet if it rains and you are at the location without an umbrella. This scenario occurs with probability 2p^2(1-p)^2.
4. If three umbrellas are at one location and one is at the other, then you will get wet if it rains and you are at the location without an umbrella. This scenario occurs with probability 3p^3(1-p).

To find the total probability of getting wet, we add up the probabilities of scenarios 2, 3, and 4:

P(wet) = 4p(1-p)^3 + 2p^2(1-p)^2 + 3p^3(1-p)

Now we can solve for the number of umbrellas needed to keep the probability of getting wet below 0.1:

4p(1-p)^3 + 2p^2(1-p)^2 + 3p^3(1-p) < 0.1

Using p = 0.6, we can solve for the minimum number of umbrellas using trial and error or a calculator:

4(0.6)(0.4)^3 + 2(0.6)^2(0.4)^2 + 3(0.6)^3(0.4) ≈ 0.153

This means that you need at least two umbrellas at each location to keep the probability of getting wet below 0.1 when the probability of rain is 0.6.

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Answer: 9 hours

Step-by-step explanation: divide 450 total miles by how many miles you drive per hour (50).

Answer: Therefore, the solution to the integral is:

∫3x^2 / (2(x^2 - 2x)^2) dx = -3/(2(x^2 - 2x)) + C

Step-by-step explanation:

To evaluate the integral, we can start by simplifying the integrand:

3x^2 / (2(x^2 - 2x)^2)

We can then use a substitution to simplify this expression further. Let u = x^2 - 2x, so that du/dx = 2x - 2 and dx = du/(2x - 2).

Substituting for u and dx, we get:

3/2 ∫du/u^2

Integrating this expression, we get:

-3/(2u) + C

Substituting back for u, we get:

-3/(2(x^2 - 2x)) + C

Therefore, the solution to the integral is:

∫3x^2 / (2(x^2 - 2x)^2) dx = -3/(2(x^2 - 2x)) + C

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The coefficient matrix for the system is

[ 2  2 ]

[-4 -2 ]

The characteristic equation is

det(A - lambda*I) = 0

where A is the coefficient matrix, I is the identity matrix, and lambda is the eigenvalue. Substituting the values of A and I gives

| 2-lambda    2      |

|-4           -2-lambda| = 0

Expanding the determinant gives

(2-lambda)(-2-lambda) + 8 = 0

Simplifying, we get

lambda^2 - 6lambda + 12 = 0

Using the quadratic formula, we find that the eigenvalues are

lambda1 = 3 + i*sqrt(3)

lambda2 = 3 - i*sqrt(3)

To find the eigenvectors, we need to solve the system

(A - lambda*I)*v = 0

where v is the eigenvector. For lambda1, we have

[ -sqrt(3)   2      ][v1]   [0]

[ -4          -5-sqrt(3)][v2] = [0]

Solving this system, we get the eigenvector

v1 = 2 + sqrt(3)

v2 = 1

For lambda2, we have

[ sqrt(3)   2     ][v1]   [0]

[ -4         -5+sqrt(3)][v2] = [0]

Solving this system, we get the eigenvector

v1 = 2 - sqrt(3)

v2 = 1

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Therefore, the first three nonzero terms of the Maclaurin series for f are: f(x) = 0 + 0x + (0/2!)x^2 + (2/3!)x^3 + ...

The Maclaurin series for a function f is given by:

f(x) = f(0) + f'(0)x + (f''(0)/2!)x^2 + (f'''(0)/3!)x^3 + ...

Since f'(x) = sin(x^2), we can find the higher derivatives of f by applying the chain rule repeatedly:

f''(x) = d/dx (sin(x^2)) = cos(x^2) * 2x

f'''(x) = d/dx (cos(x^2) * 2x) = -2x^2 * sin(x^2) + 2cos(x^2)

Evaluating these derivatives at x = 0, we get:

f(0) = 0

f'(0) = sin(0) = 0

f''(0) = cos(0) * 2 * 0 = 0

f'''(0) = -2 * 0^2 * sin(0) + 2 * cos(0) = 2

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The drama club must sell at least 74 student tickets in order to meet the show's expenses.

Let's denote the number of student tickets sold as "S".

We know that each student ticket sells for $7.50, so the total revenue from student ticket sales is 7.50S dollars.

We are also given that each adult ticket sells for $10, and 37 adult tickets were sold. Therefore, the revenue from adult ticket sales is 10 * 37 dollars.

The total revenue from ticket sales must be at least $920 to cover the show's costs. Therefore, we can set up the equation:

7.50S + 10 * 37 ≥ 920

Now, we can solve this equation to find the range of possible values for S:

7.50S + 370 ≥ 920

7.50S ≥ 920 - 370

7.50S ≥ 550

S ≥ 550 / 7.50

S ≥ 73.33

Since the number of student tickets must be a whole number, the smallest possible value for S is 74. Therefore, the drama club must sell at least 74 student tickets in order to meet the show's expenses.

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For an experiment with four conditions with n = 7 each, q = 7.815 for alpha level .01 and q = 5.318 for alpha level .05.

To find q, we need to first calculate the total number of observations in the experiment, which is given by multiplying the number of conditions by the sample size in each condition. In this case, we have 4 conditions with n = 7 each, so:

Total number of observations = 4 x 7 = 28

Next, we need to calculate the critical values of q for the given alpha levels and degrees of freedom (df = K - 1 = 3):

For alpha level .01 and df = 3, the critical value of q is 7.815.

For alpha level .05 and df = 3, the critical value of q is 5.318.

Therefore, for an experiment with four conditions with n = 7 each, q = 7.815 for alpha level .01 and q = 5.318 for alpha level .05.

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The given initial value problem is y′′-4y=0. The solution to the initial value problem is y(t)=(3/2)*e^(2t)-(1/2)*e^(-2t).

This is a second-order homogeneous linear differential equation with constant coefficients. The characteristic equation is r^2-4=0, which has roots r=±2. Therefore, the general solution is y(t)=c1e^(2t)+c2e^(-2t), where c1 and c2 are constants determined by the initial conditions.

To find c1 and c2, we need to use the initial conditions. Let's say that y(0)=1 and y'(0)=2. Then, we have:

y(0)=c1+c2=1

y'(0)=2c1-2c2=2

Solving these equations simultaneously gives us c1=3/2 and c2=-1/2. Therefore, the solution to the initial value problem is y(t)=(3/2)*e^(2t)-(1/2)*e^(-2t).

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The fractions that are equivalent to 0.63 are options A and C, which are 63/100 and 7/11 .

To find out which fractions are equivalent to 0.63, we can express 0.63 as a fraction in simplest form and then compare the resulting fraction with the given options.

0.63 can be written as 63/100 since 63 is the numerator and 100 is the denominator.

To check if 63/100 is equivalent to the other options, we can simplify each fraction to its simplest form and see if it matches with 63/100.

Option A: 63/100 is already in simplest form, so it is equivalent to itself.

Option B: We can simplify 7/11 to its simplest form by dividing both the numerator and denominator by their greatest common factor, which is 1. This gives us 7/11, which is not equivalent to 63/100.

Option C: We can simplify 63/99 to its simplest form by dividing both the numerator and denominator by their greatest common factor, which is 9. This gives us 7/11, which is equivalent to 63/100.

Option D: We can simplify 6/11 to its simplest form by dividing both the numerator and denominator by their greatest common factor, which is 1. This gives us 6/11, which is not equivalent to 63/100.

Therefore, correct options are a and c.

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Complete question is:

Which fractions are equivalent to 0.63? Select all that apply.

A) 63/100

B)  7/11

C) 63/99

D) 6/11

Step-by-step explanation:

percent change = (new - old) / old

= (54244-51644) / 51644

= 2600/51644

= 0.050344 = 5.03% increase

To implies that f(y) < g(y) contradicts the assumption that f and g are both isomorphisms from A to B.

To conclude that f = g and there can be only one isomorphism from A to B.

Let A and B be two well-ordered structures that are isomorphic and let f and g be two isomorphisms from A to B.

We want to show that f = g.

To prove this use proof by contradiction.

Suppose that f and g are not equal, that is there exists an element x in A such that f(x) is not equal to g(x).

Without loss of generality may assume that f(x) < g(x).

Let Y be the set of all elements of A that are less than x.

Since A is well-ordered Y has a least element say y.

Then we have:

f(y) ≤ f(x) < g(x) ≤ g(y)

Since f and g are isomorphisms they preserve the order of the elements means that:

f(y) < f(x) < g(y)

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a)  The posterior density p(θ | n) is p(θ | n) ∝ L(θ | n) * f(θ). b) the posterior distribution of θ will assign negligible probability mass around θ = 0.99 for large sample sizes. c) The posterior distribution would be more informative and accurately capture the true value of θ.

(a) To determine the posterior density of θ given n heads, we can use Bayes' theorem:

Posterior density ∝ Likelihood × Prior

Let's denote the posterior density as p(θ | n), the likelihood as L(θ | n), and the prior as f(θ).

The likelihood L(θ | n) is the probability of observing n heads given θ. In a coin toss, the probability of getting a head on a single toss is θ, so the likelihood is given by the binomial distribution:

L(θ | n) = (n choose n) * θ^n * (1-θ)^(n-n)

The prior density f(θ) is given as a Uniform[0.4, 0.6] distribution. Since it is a continuous uniform distribution, the prior density is a constant within the interval [0.4, 0.6] and zero outside this interval.

Now, we can calculate the posterior density p(θ | n):

p(θ | n) ∝ L(θ | n) * f(θ)

The constant of proportionality can be obtained by integrating the posterior density over the entire range of θ and dividing by it to make it a proper probability density.

(b) Suppose the true value of θ is 0.99. In this case, the likelihood L(θ | n) will decrease rapidly as n increases. This is because, as we observe more heads (n increases), the likelihood of obtaining those heads given a true θ of 0.99 becomes extremely low. As a result, the posterior distribution of θ will assign negligible probability mass around θ = 0.99 for large sample sizes.

(c) From part (b), we can conclude that the choice of prior is important. In this case, the Uniform[0.4, 0.6] prior was not suitable for capturing the true value of θ = 0.99, especially as the number of observations (n) increases. If we have strong prior knowledge or belief about the range of θ, it would be better to choose a prior that assigns higher probability mass around the true value. This way, the posterior distribution would be more informative and accurately capture the true value of θ.

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The probability of getting more than 12 successes in 14 trials with success probability 0.9 is approximately 0.9919. The variance of the given binomial distribution is 1.26 (rounded to two decimal places). The standard deviation of the given binomial distribution is approximately 1.123.

Part 1: To find the probability P(More than 12) for a binomial experiment with n=14 trials and success probability p=0.9, we can use the cumulative distribution function (CDF) of the binomial distribution:

P(More than 12) = 1 - P(0) - P(1) - ... - P(12)

where P(k) is the probability of getting exactly k successes in 14 trials:

[tex]P(k) = (14 choose k) * 0.9^k * 0.1^(14-k)[/tex]

Using a calculator or a statistical software, we can compute each term of the sum and then subtract from 1:

P(More than 12) = 1 - P(0) - P(1) - ... - P(12)

= 1 - binom.cdf(12, 14, 0.9)

≈ 0.9919 (rounded to four decimal places)

Therefore, the probability of getting more than 12 successes in 14 trials with success probability 0.9 is approximately 0.9919.

Part 2: The mean of a binomial distribution with n trials and success probability p is given by:

mean = n * p

Substituting n=14 and p=0.9, we get:

mean = 14 * 0.9

= 12.6

Therefore, the mean of the given binomial distribution is 12.6 (rounded to two decimal places).

Part 3: The variance of a binomial distribution with n trials and success probability p is given by:

variance = n * p * (1 - p)

Substituting n=14 and p=0.9, we get:

variance = 14 * 0.9 * (1 - 0.9)

= 1.26

Therefore, the variance of the given binomial distribution is 1.26 (rounded to two decimal places).

The standard deviation is the square root of the variance:

standard deviation = sqrt(variance)

= sqrt(1.26)

≈ 1.123 (rounded to three decimal places)

Therefore, the standard deviation of the given binomial distribution is approximately 1.123.

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Answer:

A) 33 ft

Step-by-step explanation:

With two angles and one side given, we should use the Law of Sines:

[tex]\displaystyle \frac{\sin A}{a}=\frac{\sin B}{b}=\frac{\sin C}{c}\\\\\frac{\sin 48^\circ}{\overline{BC}}=\frac{\sin 130^\circ}{34}\\\\34\sin48^\circ=\overline{BC}\sin130^\circ\\\\\overline{BC}=\frac{34\sin48^\circ}{\sin130^\circ}\\\\\overline{BC}\approx 33[/tex]

Chase must win 30 additional games in a row to achieve a 90% win percentage.

Given the information that Chase has won 70% of the 30 football video games, he has played with his brother.

The equation can be solved to determine the number of additional games in a row, x, that Chase must win to achieve a 90% win percentage is:

(70% of 30 + x) / (30 + x) = 90%

Let's solve for x:`(70/100) × 30 + 70/100x = 90/100 × (30 + x)

Multiplying both sides by 10:

210 + 7x = 270 + 9x2x = 60x = 30

Therefore, Chase must win 30 additional games in a row to achieve a 90% win percentage.

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This describes the straight line segment from (0, 0) to (4, 8) as t varies from 0 to 1. The value of the line integral is 80/3.

(a) A parametrization of the path C can be given by:

r(t) = (4t, 8t), for 0 ≤ t ≤ 1.

This describes the straight line segment from (0, 0) to (4, 8) as t varies from 0 to 1.

(b) To evaluate the line integral of x^2 + y^2 over C, we need to find the arclength of C. The arclength integral is given by:

s = ∫₀¹ √(dx/dt)^2 + (dy/dt)^2 dt

Using the parametrization r(t) above, we have:

dx/dt = 4 and dy/dt = 8

So, √(dx/dt)^2 + (dy/dt)^2 = √(16 + 64) = √80 = 4√5.

Hence, the arclength of C is:

s = ∫₀¹ 4√5 dt = 4√5.

Finally, we can evaluate the line integral:

∫ C (x^2 + y^2) ds = ∫₀¹ ((4t)^2 + (8t)^2) (4√5) dt

= ∫₀¹ (16t^2 + 64t^2) (4√5) dt

= 80 ∫₀¹ t^2 dt

= 80 (1/3)

= 80/3.

Therefore, the value of the line integral is 80/3.

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Answer:

[tex]y - 17 = -\frac{11}{6} (x+5)[/tex]

Step-by-step explanation:

Remember that the slope-point form of a line is:

[tex]y - y_{1} = m(x-x_{1})[/tex], where [tex](x_{1}, y_{1} )[/tex] the point on the line, and [tex]m[/tex] is the slope. All these values are given in the question, so we just go ahead and plug them in to get:

[tex]y - 17 = -\frac{11}{6} (x+5)[/tex]

Hope this helps

Based on the model, a player with a career total of 40,000 points would have scored 3,734 points in their rookie season.

In this exercise, we would plot the rookie season-points on the x-axis (x-coordinates) of a scatter plot while the overall points would be plotted on the y-axis (y-coordinate) of the scatter plot through the use of Microsoft Excel.

On the Microsoft Excel worksheet, you should right click on any data point on the scatter plot, select format trend line, and then tick the box to display an equation of the curve of best fit (trend line) on the scatter plot.

Based on the scatter plot shown below, which models the relationship between the rookie season-points and the overall points, an equation of the curve of best fit is modeled as follows:

y = 5.74x + 18568

Based on the equation of the curve of best fit above, a player with a career total of 40,000 points would have scored the following points in their rookie season:

y = 5.74x + 18568

40,000 = 5.74x + 18568

5.74x = 40,000 - 18568

x = 21,432/5.74

x = 3,733.80 ≈ 3,734 points.

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The value of cos(2022π) is easy to compute by hand because the argument (2022π) is a multiple of 2π, which means it lies on the x-axis of the unit circle.

Recall that the unit circle is the circle centered at the origin with radius 1 in the Cartesian plane. The x-coordinate of any point on the unit circle is given by cos(θ), where θ is the angle between the positive x-axis and the line segment connecting the origin to the point. Similarly, the y-coordinate of the point is given by sin(θ).

Since 2022π is a multiple of 2π, it represents an angle that has completed a full revolution around the unit circle. Therefore, the point corresponding to this angle lies on the positive x-axis, and its x-coordinate is equal to 1. Hence, cos(2022π) = 1.

In summary, cos(2022π) is easy to compute by hand because the argument lies on the x-axis of the unit circle, and its x-coordinate is equal to 1.

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By Maclaurin series the sum of the series is e^(7/2) * 3 + (637/48).

We can use the formula for the Maclaurin series of the exponential function[tex]e^x[/tex]:

e^x = Σ(x^n / n!), n=0 to infinity

Substituting x = 7/2, we get:

e^(7/2) = Σ((7/2)^n / n!), n=0 to infinity

Multiplying both sides by 2^n, we get:

2^n * e^(7/2) = Σ(7^n / (n! * 2^(n - 1))), n=0 to infinity

Substituting n! with n * (n - 1)!, we get:

2^n * e^(7/2) = Σ(7^n / (n * 2^n * (n - 1)!)), n=0 to infinity

Simplifying the expression, we get:

2^n * e^(7/2) = Σ(7/2)^n / n(n - 1)!, n=2 to infinity

(Note that the terms for n = 0 and n = 1 are zero, since 7^0 = 7^1 = 1 and 0! = 1!)

Now, we can add the first two terms of the series separately:

Σ(7/2)^n / n(n - 1)!, n=2 to infinity = (7/2)^2 / 2! + (7/2)^3 / 3! + Σ(7/2)^n / n(n - 1)!, n=4 to infinity

Simplifying the first two terms, we get:

(7/2)^2 / 2! + (7/2)^3 / 3! = (49/8) + (343/48) = (294 + 343) / 48 = 637/48

So, the sum of the series is:

2^0 * e^(7/2) + 2^1 * e^(7/2) + (637/48) = e^(7/2) * (1 + 2) + (637/48) = e^(7/2) * 3 + (637/48)

Therefore, the sum of the series is e^(7/2) * 3 + (637/48).

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