what is the y value when x= -1?

what is the y value when x=0?

what is the y value when x=2?

The arc length of the curve x = 7 cos ( 7 t ) , y = 7 sin ( 7 t ) with 0 ≤ t ≤ π 14 , we can use the formula:
L = ∫[a,b]√[dx/dt]^2 + [dy/dt]^2 dtThe arc length of the curve x = 7 cos ( 7 t ) , y = 7 sin ( 7 t ) with 0 ≤ t ≤ π 14 , is π/2 units.

Find the arc length of the curve x = 7 cos ( 7 t ) , y = 7 sin ( 7 t ) with 0 ≤ t ≤ π 14 , we can use the formula:
L = ∫[a,b]√[dx/dt]^2 + [dy/dt]^2 dt
where a and b are the limits of integration, and dx/dt and dy/dt are the derivatives of x and y with respect to t.
In this case, we have:
dx/dt = -7 sin (7t)
dy/dt = 7 cos (7t)
So, we can substitute these values into the formula and integrate over the given range of t:
L = ∫[0,π/14]√[(-7 sin (7t))^2 + (7 cos (7t))^2] dt
L = ∫[0,π/14]7 dt
L = 7t |[0,π/14]
L = 7(π/14 - 0)
L = π/2
Therefore, the arc length of the curve x = 7 cos ( 7 t ) , y = 7 sin ( 7 t ) with 0 ≤ t ≤ π 14 is π/2 units.

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To make population pyramids for the cities in her state, Shania will need the following information: the age and gender of the population, the fertility rates of the population, and the population distribution of the cities.

A population pyramid, also known as an age-sex pyramid, is a visual representation of a population's age and gender composition. It's a graphical representation of population data, with the age cohorts on the vertical axis and the percentage of the population on the horizontal axis. Population pyramids are used to explain demographic variables such as birth rate, life expectancy, and infant mortality rate. They're also utilized to predict the future population size of a region or country.

The following information is required to make a population pyramid: Age and gender of the population: A population pyramid is divided into male and female categories. The age distribution of the population is divided into five-year age cohorts. For example, age cohorts from 0 to 4 years, 5 to 9 years, and so on. Fertility rates of the population: The birth rates of a population are represented by the shape of a pyramid. The number of children born per woman is referred to as the fertility rate. Population distribution of the cities: The population size of a particular location affects the shape of the pyramid.

The population can be divided into urban and rural areas, and their numbers will affect the shape of the pyramid.

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Answer:

100 feet

Step-by-step explanation:

The fence goes around the patio. It has to be 30ft across the top and bottom each. And 20ft up and down the left and right sides.

Perimeter (all the way around)

= 20+30+20+30

= 100

The fence will need to be 100ft long.

Answer:

1) 80

2) 90

3) 120

4) 80

5) 50

6) 5

7) 35

8) 10

9) 90

10) 180

Step-by-step explanation:

:o

Answer:

1) 80

2) 90

3) 120

4) 80

5) 50

6) 5

7) 35

8) 10

9) 90

10) 180

Step-by-step explanation:

The inverse function of f(x) = 7tan(9x) is f⁻¹(x) = (1/9)arctan(x/7).

To find the inverse function of f(x) = 7tan(9x), we first need to understand the concept of inverse functions. An inverse function reverses the operation of the original function, meaning that if f(x) takes an input x and produces an output y, then the inverse function, denoted as f⁻¹(x), takes an input y and produces an output x.

Follow these steps to find the inverse function of f(x) = 7tan(9x):

1. Replace f(x) with y: y = 7tan(9x).
2. Swap x and y: x = 7tan(9y).
3. Solve for y: First, divide both sides by 7 to isolate the tangent function: x/7 = tan(9y).
4. Apply the arctangent (inverse tangent) function to both sides: arctan(x/7) = 9y.
5. Divide by 9 to solve for y: (1/9)arctan(x/7) = y.

Thus, the inverse function of f(x) = 7tan(9x) is f⁻¹(x) = (1/9)arctan(x/7). This inverse function takes an input x and returns the value of y such that the original function f(x) would map that y back to the input x. In other words, if f(x) = 7tan(9x) transforms a value x to a value y, then f⁻¹(x) = (1/9)arctan(x/7) will transform that same value y back to the original value x.

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Answer:

no, he did not substitute the formula correctly in step one.

The formula for the exponential function passing through the points (-3, 3/25) and (1, 15) is:
f(x) = 15 * 5^x

To find a formula for the exponential function passing through the points (-3, 3/25) and (1, 15), we can start by using the general form of an exponential function, which is f(x) = ab^x, where a is the initial value and b is the growth factor. Using the two given points, we can form two equations:

3/25 = ab^(-3)
15 = ab

We can solve for a and b by first dividing the second equation by the first equation:

15 / (3/25) = ab / (ab^(-3))

Simplifying, we get:

125 = b^3

Taking the cube root of both sides, we get:

b = 5

Substituting this value into one of the original equations, we can solve for a:

3/25 = a(5)^(-3)
a = 3/25 * 125 = 15

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We use the chain rule and the power rule of differentiation and get the value of y' as, [tex]y' = (4x^3 - (15/2)x^{(1/2)}) / ln(6).[/tex]

The given equation defines a function y that is the natural logarithm (base e) of an algebraic expression involving x.

[tex]y = log6(x^4 - 5x^{(3/2)})[/tex]

We can find the derivative of y with respect to x using the chain rule and the power rule of differentiation.

The derivative of y is denoted as y' and is obtained by differentiating the expression inside the logarithm with respect to x, and then multiplying the result by the reciprocal of the natural logarithm of the base.

[tex]y' = (1 / ln(6)) * d/dx (x^4 - 5x^{(3/2}))[/tex]

The final expression for y' involves terms that include the power of x raised to the third and the half power, which can be simplified as necessary.

[tex]y' = (1 / ln(6)) * (4x^3 - (15/2)x^{(1/2)})[/tex]

Therefore, [tex]y' = (4x^3 - (15/2)x^{(1/2)}) / ln(6).[/tex]

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The letter used to represent Pearson's product-moment correlation coefficient is "r".

This coefficient measures the strength and direction of the linear relationship between two variables. It ranges from -1 to 1, where 1 indicates a perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates no linear correlation.

To calculate Pearson's correlation coefficient, we first standardize the variables by subtracting their means and dividing by their standard deviations. Then, we calculate the product of the standardized values for each pair of corresponding data points. The sum of these products is divided by the product of the standard deviations of the two variables. The resulting value is the correlation coefficient "r".

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The distance from the point S with coordinates (5, 10, 2) to the plane defined by the equation x + y + 10z - 3 = 0 is estimated to be around 2.77 units.

The distance between a point and a plane can be calculated using the formula:

d = |ax + by + cz + d| / √(a² + b² + c²)

where (a, b, c) is the normal vector to the plane, and (x, y, z) is any point on the plane.

The given plane can be written as:

x + y + 10z - 3 = 0

So, the coefficients of x, y, z, and the constant term are 1, 1, 10, and -3, respectively. The normal vector to the plane is therefore:

(a, b, c) = (1, 1, 10)

To find the distance between the point S(5, 10, 2) and the plane, we can substitute the coordinates of S into the formula for the distance:

d = |1(5) + 1(10) + 10(2) - 3| / √(1² + 1² + 10²)

Simplifying the expression, we get:

Therefore, the distance between the point S(5, 10, 2) and the plane x + y + 10z - 3 = 0 is approximately 2.77 units.

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The selling price for the tickets is $209.

Here, we have

Given:

If the cost of tickets is 190 dollars, and the markup is 10 percent,

We have to find the selling price.

Markup refers to the amount that must be added to the cost price of a product or service in order to make a profit.

It is computed by multiplying the cost price by the markup percentage. To find out what the selling price would be, you just need to add the markup to the cost price.

The markup percentage is 10%.

10 percent of the cost of tickets ($190) is:

$190 x 10/100 = $19

Therefore, the markup is $19.

Now, add the markup to the cost of tickets to obtain the selling price:

Selling price = Cost price + Markup= $190 + $19= $209

Therefore, the selling price for the tickets is $209.

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The coefficient matrix A is [-5/2 4; 3/4 -3].

The characteristic equation is det(A-lambda*I) = 0, where lambda is the eigenvalue and I is the identity matrix. Solving for lambda, we get lambda² - (11/4)lambda - 15/8 = 0. The eigenvalues are lambda1 = (11 + sqrt(161))/8 and lambda2 = (11 - sqrt(161))/8.


To find the eigenvalues of the coefficient matrix A, we need to solve the characteristic equation det(A-lambda*I) = 0. This equation is formed by subtracting lambda times the identity matrix I from A and taking the determinant. The resulting polynomial is of degree 2, so we can use the quadratic formula to find the roots.

In this case, the coefficient matrix A is given as [-5/2 4; 3/4 -3]. We subtract lambda times the identity matrix I = [1 0; 0 1] to get A-lambda*I = [-5/2-lambda 4; 3/4  -3-lambda]. Taking the determinant of this matrix, we get the characteristic equation det(A-lambda*I) = (-5/2-lambda)(-3-lambda) - 4*3/4 = lambda²- (11/4)lambda - 15/8 = 0.

Using the quadratic formula, we can solve for lambda: lambda = (-(11/4) +/- sqrt((11/4)² + 4*15/8))/2. Simplifying, we get lambda1 = (11 + sqrt(161))/8 and lambda2 = (11 - sqrt(161))/8. These are the eigenvalues of the coefficient matrix A.

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The joint density function of the random variables X and Y is given by f(x, y) = (2e^(-2x))/x for 0 ≤ x < ∞ and 0 ≤ y ≤ x, and 0 otherwise. (a) The covariance of X and Y can be computed using the definition of covariance.

(a) The covariance of X and Y, Cov(X, Y), can be computed using the formula Cov(X, Y) = E(XY) - E(X)E(Y). We need to calculate the expectations E(XY), E(X), and E(Y) to find the covariance.

(b) To find E(Y|X), we need to calculate the conditional expectation of Y given X. This can be done by integrating Y multiplied by the conditional probability density function f(y|x) with respect to y, where f(y|x) is obtained by dividing f(x, y) by the marginal density function of X, fX(x).

(c) To compute Cov(X, E(Y|X)), we first find E(Y|X) using the method described in (b). Then we calculate the covariance between X and E(Y|X) using the definition of covariance. It can be shown that Cov(X, E(Y|X)) is the same as Cov(X, Y).

Therefore, by following the steps outlined above, we can compute the covariance of X and Y, find the conditional expectation E(Y|X), and verify that the covariance of X and E(Y|X) is the same as the covariance of X and Y

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In this team activity, we were given a weather forecasting problem in which we had to determine the stochastic matrix and calculate the probabilities of different weather conditions for a given day.

To solve the problem, we first needed to determine the stochastic matrix M, which is a matrix that represents the probabilities of transitioning from one state to another. In this case, the three possible states are good, indifferent, and bad weather. Using the given probabilities, we constructed the following stochastic matrix:

M = [[0.7, 0.2, 0.1], [0.6, 0.3, 0.1], [0.4, 0.4, 0.2]]

For the second question, we used the stochastic matrix to calculate the probabilities of bad weather tomorrow, given that there is a 20% chance of good weather and an 80% chance of indifferent weather today. We first calculated the probability vector for today as [0.2, 0.8, 0], and then multiplied it by the stochastic matrix to get the probability vector for tomorrow. The resulting probability vector was [0.14, 0.36, 0.5], so the chance of bad weather tomorrow is 50%.

For the third question, we used the stochastic matrix to calculate the probability of good weather on Wednesday, given that the predicted weather for Monday is 50% indifferent and 50% bad. We first calculated the probability vector for Monday as [0, 0.5, 0.5], and then multiplied it by the stochastic matrix twice to get the probability vector for Wednesday. The resulting probability vector was [0.46, 0.31, 0.23], so the chance of good weather on Wednesday is 46%.

For the final question, we needed to find the steady-state vector, which is a vector that represents the long-term probabilities of being in each state. We calculated the steady-state vector by solving the equation Mv = v, where v is the steady-state vector. The resulting steady-state vector was [0.5, 0.3, 0.2], so in the long run, the chance of bad weather on a given day is 20%.

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Given that x follows a binomial distribution with parameters n = 12 and p = 0.15, we can use the following formulas to find the expected value E(x) and variance Var(x):

E(x) = n * p

Var(x) = n * p * (1 - p)

Substituting n = 12 and p = 0.15, we get:

E(x) = 12 * 0.15 = 1.8

Var(x) = 12 * 0.15 * (1 - 0.15) = 1.53

Therefore, the expected value of x is E(x) = 1.8, and the variance of x is Var(x) = 1.53.

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The correct answer is: Standard Deviation = 4.03.

To calculate the standard deviation of a set of data, you can use the following steps:

Calculate the mean (average) of the data.

Subtract the mean from each data point and square the result.

Calculate the mean of the squared differences.

Take the square root of the mean from step 3 to get the standard deviation.

Let's apply these steps to the data you provided: 23, 19, 28, 30, 22.

Step 1: Calculate the mean

Mean = (23 + 19 + 28 + 30 + 22) / 5 = 122 / 5 = 24.4

Step 2: Subtract the mean and square the result for each data point:

(23 - 24.4)² = 1.96

(19 - 24.4)² = 29.16

(28 - 24.4)² = 13.44

(30 - 24.4)² = 31.36

(22 - 24.4)² = 5.76

Step 3: Calculate the mean of the squared differences:

Mean of squared differences = (1.96 + 29.16 + 13.44 + 31.36 + 5.76) / 5 = 81.68 / 5 = 16.336

Step 4: Take the square root of the mean from step 3 to get the standard deviation:

Standard Deviation = √(16.336) ≈ 4.03

Therefore, the correct answer is: Standard Deviation = 4.03.

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Answer:B

Step-by-step explanation: had the question before

There are 2 possible values for N.

To find the number of possible values for N, we must first find the common fraction representing people who value both color and smell. To do this, we need to find the LCM (Least Common Multiple) of the denominators 14 and 12. The LCM of 14 and 12 is 84.

Let x be the number of people who value both color and smell. Then, (9/14)N + (7/12)N - x = 753, which simplifies to (27/84)N + (14/84)N - x = 753. Combining the fractions gives (41/84)N - x = 753.

Now, we know that x is an integer, and (41/84)N must be an integer as well. Therefore, N must be a multiple of 84. Since 41 is a prime number, the only multiples of 84 that can satisfy this condition are 84 and 168, making 2 possible values for N.

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∫_A^B ∫_B^C ∫_D^E (10%)(x^2 + y^2 + z^12) dz dr dθ.

This represents the full iterated integral for F_sav over the given solid cylinder.

(a) The iterated integral for F_sav with the given limits of integration is as follows:

∫∫∫_W (10%)(x^2 + y^2 + z^12) dz dr dθ,

where the limits of integration are A = 0, B = C = 0, D = 6, and E = -1.

(b) To evaluate the integral, we begin with the innermost integration with respect to z. Since z ranges from -1 to 6, the integral becomes:

∫∫_D^E (10%)(x^2 + y^2 + z^12) dz.

Next, we integrate with respect to r, where r represents the radial distance from the z-axis. As the solid cylinder is centered about the z-axis and has a base radius of 6, r ranges from 0 to 6. Thus, the integral becomes:

∫_B^C ∫_D^E (10%)(x^2 + y^2 + z^12) dz dr.

Finally, we integrate with respect to θ, where θ represents the angle around the z-axis. As the cylinder is symmetric about the z-axis, we integrate over a full circle, so θ ranges from 0 to 2π. Hence, the integral becomes:

∫_A^B ∫_B^C ∫_D^E (10%)(x^2 + y^2 + z^12) dz dr dθ.

This represents the full iterated integral for F_sav over the given solid cylinder.

The problem asks for the iterated integral of F_sav over the solid cylinder W. To evaluate this integral, we use the cylindrical coordinate system (r, θ, z) since the cylinder is centered about the z-axis. The function inside the integral is 10% times the sum of squares of x, y, and z^12. By integrating successively with respect to z, r, and θ, and setting appropriate limits of integration, we obtain the final iterated integral. The integration limits are determined based on the given dimensions of the cylinder.

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Thus, m∠F =  33º for the corresponding angle measures for each similar triangle ΔABC and ΔDEF.

To start, we know that similar triangles have corresponding angles that are congruent. Therefore, we can set up the following proportion:

m∠BAC/m∠EDF = m∠BCA/m∠DFE

Substituting the given angle measures, we get:
(x² - 5x)/(4x + 36) = (4x - 5)/m∠F

To solve for m∠F, we need to isolate it on one side of the equation. First, we can cross-multiply to get:
(4x - 5)(4x + 36) = (x² - 5x)m∠F

Expanding the left side, we get:
16x² + 116x - 180 = (x² - 5x)m∠F

Next, we can divide both sides by (x² - 5x):
(16x² + 116x - 180)/(x² - 5x) = m∠F

Simplifying the left side, we get:
(4x + 29)/(x - 5) = m∠F

Therefore, m∠F = (4x + 29)/(x - 5).

To check our answer, we can plug in a value for x and find the corresponding angle measures for each triangle. For example, if x = 6:

m∠BAC = (6² - 5(6))º = 16º
m∠BCA = (4(6) - 5)º = 19º
m∠EDF = (4(6) + 36)º = 60º

Using our formula for m∠F, we get:
m∠F = (4(6) + 29)/(6 - 5) = 33º

We can see that this satisfies the proportion and therefore our answer is correct.

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To find the expected value of the random variable exp(X), we need to calculate the integral of exp(x) multiplied by the probability density function (PDF) of X, and then evaluate it over the appropriate range.

Given that X is an exponential random variable with PDF fX(x) = 2 exp(-2x) for x ≥ 0, we want to find E[exp(X)], which is the expected value of exp(X).

The expected value of a continuous random variable can be computed using the following formula:

E[g(X)] = ∫ g(x) * fX(x) dx

In our case, we want to find E[exp(X)], so we need to compute the following integral:

E[exp(X)] = ∫ exp(x) * 2 exp(-2x) dx

Simplifying the expression:

E[exp(X)] = 2 ∫ exp(-x) dx

Now, we can integrate the expression:

E[exp(X)] = -2 exp(-x) + C

To evaluate the integral, we need to determine the limits of integration. Since X is an exponential random variable defined for x ≥ 0, the limits of integration will be from 0 to infinity.

E[exp(X)] = -2 exp(-x) |_0^∞

E[exp(X)] = -2 [exp(-∞) - exp(0)]

Since exp(-∞) approaches 0, and exp(0) = 1, we can simplify further:

E[exp(X)] = -2 [0 - 1] = 2

Therefore, the expected value of the random variable exp(X) is 2.

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Answer:

n

Step-by-step explanation:

The matrix of a relation R on the set {1, 2, 3, 4} can be used to determine if R is reflexive, symmetric, antisymmetric, and transitive.

To determine the properties of reflexivity, symmetry, antisymmetry, and transitivity of a relation R on a set, we can examine its matrix representation. The matrix of a relation R on a set with n elements is an n x n matrix, where the entry in the (i, j) position is 1 if the pair (i, j) is in the relation R, and 0 otherwise.

For reflexivity, we check if the diagonal entries of the matrix are all 1. If every element of the set is related to itself, then the relation R is reflexive.

For symmetry, we compare the matrix with its transpose. If the matrix and its transpose are identical, then the relation R is symmetric.

For antisymmetry, we examine the off-diagonal entries of the matrix. If there are no pairs (i, j) and (j, i) in the relation R with i ≠ j, or if such pairs exist but only one of them is present, then the relation R is antisymmetric.

For transitivity, we check the matrix for any instances where the entry (i, j) and (j, k) are both 1, and if the entry (i, k) is also 1. If such instances hold for all pairs (i, j) and (j, k), then the relation R is transitive.

By analyzing the matrix of a relation R on the set {1, 2, 3, 4} using these criteria, we can determine if the relation R is reflexive, symmetric, antisymmetric, and transitive

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Evaluating the expression under the conditions: cos 2theta; sin theta = - 8/17, theta in Quadrant III -

sin 5x cos 6x can be expressed as sin 11x / 2.

To evaluate the expression cos 2θ, we need to find the value of θ first.

We have,

sin θ = -8/17 and θ is in Quadrant III.

Since sin θ = -8/17, we know that the opposite side of the triangle is -8 and the hypotenuse is 17. Using the Pythagorean theorem, we can find the adjacent side:

adjacent^2 = hypotenuse^2 - opposite^2

adjacent^2 = 17^2 - (-8)^2

adjacent^2 = 289 - 64

adjacent^2 = 225

adjacent = 15

Now, we can use the definition of cosine to evaluate cos θ:

cos θ = adjacent / hypotenuse

cos θ = 15 / 17

Since cos 2θ is a double-angle identity, we can use the formula:

cos 2θ = cos^2 θ - sin^2 θ

Plugging in the values we found, we get:

cos 2θ = (15/17)^2 - (-8/17)^2

cos 2θ = 225/289 - 64/289

cos 2θ = (225 - 64) / 289

cos 2θ = 161/289

Therefore, cos 2θ is equal to 161/289.

To express sin 5x cos 6x as a sum, we can use the double-angle identity for sine:

sin 2θ = 2sin θ cos θ

Let's rewrite sin 5x cos 6x using the double-angle identity:

sin 5x cos 6x = (2sin 5x cos 6x) / 2

             = sin (5x + 6x) / 2

Simplifying further:

sin (5x + 6x) / 2 = sin 11x / 2

Therefore, sin 5x cos 6x can be expressed as sin 11x / 2.

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The equivalent expression of the rational exponent ∛a² is [tex](a)^{\frac{2}{3}[/tex].

Rational exponents are exponents that are fractions, where the numerator is a power and the denominator is a root.

So rational exponents (fractional exponents) are exponents that are fractions or rational expressions.

To determine the rational exponent equivalent to the expression given, we will apply the power rule of indices as shown below.

The given expression is ;

∛a²

The rational exponent is calculated as follows;

∛a² = [tex](a)^{\frac{2}{3}[/tex]

Thus, based on exponent power rule, the given expression is equivalent to ∛a² = [tex](a)^{\frac{2}{3}[/tex]

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The complete question is below:

Find the equivalent expression of the rational exponent ∛a². does anyone know it

The Pearson's linear correlation coefficient, also known as the Pearson's r, measures the strength and direction of association between two continuous random variables. It ranges from -1 to 1.

A value near ±1 indicates a strong linear association, with positive values signifying a direct relationship and negative values an inverse relationship.

If the value is close to ±1, the association is indeed quasi-perfectly linear. However, it's important to note that correlation doesn't imply causation.

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a. To find the 30th percentile for the standard normal distribution, we first need to locate the z-score that corresponds to this percentile. We can use a standard normal distribution table or a calculator to find this value. From the table, we can see that the z-score that corresponds to the 30th percentile is approximately -0.524. Therefore, the 30th percentile for the standard normal distribution is z = -0.524.

b. To find the 30th percentile for a normal distribution with mean 10 and standard deviation 1.5, we can use the formula for transforming a standard normal distribution to a normal distribution with a given mean and standard deviation. This formula is:
z = (x - μ) / σ
where z is the standard normal score, x is the raw score, μ is the mean, and σ is the standard deviation.
To find the 30th percentile for this distribution, we first need to find the corresponding z-score using the formula above:
-0.524 = (x - 10) / 1.5
Multiplying both sides by 1.5, we get:
-0.786 = x - 10
Adding 10 to both sides, we get:
x = 9.214
Therefore, the 30th percentile for a normal distribution with mean 10 and standard deviation 1.5 is x = 9.214. This means that 30% of the observations in this distribution are below 9.214.

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The probability that a customer does not win an appetizer is given as follows:

p = 0.8 = 80%.

A probability is calculated as the division of the desired number of outcomes by the total number of outcomes in the context of a problem/experiment.

The total number of marbles is given as follows:

1 + 4 + 7 + 3 = 15 marbles.

An appetizer is won with a green marble, and 3 of the marbles are green, while 12 are not, hence the probability that a customer does not win an appetizer is given as follows:

p = 12/15

p = 0.8 = 80%.

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Answer: Therefore, the correlation coefficient, r, is 0.877. This indicates a strong positive correlation between age and fundamental frequency in hyena giggles.

Step-by-step explanation:

To calculate the correlation coefficient, r, we need to follow these steps:

Step 1: Calculate the sum of squares of age.

Step 2: Calculate the sum of squares for fundamental frequency.

Step 3: Calculate the sum of products between age and frequency.

Step 4: Compute the correlation coefficient, r.

Here are the calculations:

Step 1: Calculate the sum of squares of age.

2^2 + 2^2 + 2^2 + 6^2 + 9^2 + 10^2 + 13^2 + 10^2 + 14^2 + 14^2 + 12^2 + 7^2 + 11^2 + 11^2 + 14^2 + 20^2 = 1066

Step 2: Calculate the sum of squares for fundamental frequency.

840^2 + 670^2 + 580^2 + 470^2 + 540^2 + 660^2 + 510^2 + 520^2 + 500^2 + 480^2 + 400^2 + 650^2 + 460^2 + 500^2 + 580^2 + 500^2 = 1990600

Step 3: Calculate the sum of products between age and frequency.

2840 + 2670 + 2580 + 6470 + 9540 + 10660 + 13510 + 10520 + 14500 + 14480 + 12400 + 7650 + 11460 + 11500 + 14580 + 20500 = 190080

Step 4: Compute the correlation coefficient, r.

r = [nΣ(xy) - ΣxΣy] / [sqrt(nΣ(x^2) - (Σx)^2) * sqrt(nΣ(y^2) - (Σy)^2))]

where n is the number of observations, Σ is the sum, x is the age, y is the fundamental frequency, and xy is the product of x and y.

Using the values we calculated in steps 1-3, we get:

r = [16190080 - (106500)] / [sqrt(162066 - 106^2) * sqrt(161990600 - 500^2)]

= 0.877

Therefore, the correlation coefficient, r, is 0.877. This indicates a strong positive correlation between age and fundamental frequency in hyena giggles.

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The vector function r(t) is [tex]r(t) = (1/7) t^7 i + e^t j + (1/3) e^{(3t)} k[/tex]

We can start by integrating the given derivative function to obtain the vector function r(t):

[tex]r'(t) = t^6 i + e^t j + 3t e^{(3t)} k[/tex]

Integrating the first component with respect to t gives:

[tex]r_1(t) = (1/7) t^7 + C_1[/tex]

Integrating the second component with respect to t gives:

[tex]r_2(t) = e^t + C_2[/tex]

Integrating the third component with respect to t gives:

[tex]r_3(t) = (1/3) e^{(3t)} + C_3[/tex]

where [tex]C_1, C_2,[/tex] and[tex]C_3[/tex] are constants of integration.

Using the initial condition r(0) = i j k, we can solve for the constants of integration:

[tex]r_1(0) = C_1 = 0r_2(0) = C_2 = 1r_3(0) = C_3 = 1/3[/tex]

Therefore, the vector function r(t) is:

[tex]r(t) = (1/7) t^7 i + e^t j + (1/3) e^{(3t)} k[/tex]

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