You made a bird house. The dimensions are given below. Total Height: 2.5 feet Triangle Top Height: 1 foot Box Bottom Height: 1.5 feet Box Width (front with door): 1.25 feet Depth: 1.75 feet Hole Diameter: .75 feet (a) You want to paint the entire outside of the birdhouse (not inside). How much space do you need to paint? (b) How much space does the bird have inside (ignore the thickness of the wood)?

The given series, Σ(2^2n * (1/2)^3k / (k! * (2k)!)), needs to be determined if it converges or diverges. By applying the Ratio Test, we can ascertain the behavior of the series. The Ratio Test states that if the limit of the absolute value of the ratio of consecutive terms is less than 1, then the series converges.

Now, let's examine the terms in the series. We can observe that the general term involves 2n and 3k in the exponents, indicating that the terms have a factorial-like growth. However, the denominator contains a k! and a (2k)! term, which grow even faster than the numerator. As k approaches infinity, the ratio of consecutive terms becomes dominated by the factorial terms in the denominator, leading to a diminishing effect. Consequently, the limit of the ratio is zero, which is less than 1. Therefore, the series converges.

In summary, the given series Σ(2^2n * (1/2)^3k / (k! * (2k)!)) converges. This conclusion is supported by applying the Ratio Test, which demonstrates that the limit of the ratio of consecutive terms is zero, satisfying the condition for convergence.

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1.1.1: Solving for x:

1.1.1

x² - x - 20 = 0

To solve for x in the equation above, we need to factorize it.

1.1.1

x² - x - 20 = 0

(x - 5) (x + 4) = 0

Therefore, x = 5 or x = -4

1.1.2: Solving for x:

1.1.2

3x² 2x - 6 = 0

Factoring the quadratic equation above, we have:

3x² 2x - 6 = 0

(x + 2) (3x - 3) = 0

Therefore, x = -2 or x = 1

1.1.3: Solving for x:

1.1.3 (x - 1)² = 9

Taking the square root of both sides, we have:

x - 1 = ±3x = 1 ± 3

Therefore, x = 4 or x = -2

1.1.4: Solving for x:

1.1.4 √x + 6 = 2

Square both sides: x + 6 = 4x = -2

1.2: Solving for x and y simultaneously:

4x + y = 2 .....(1)

y² + 4x - 8 = 0 .....(2)

Solving equation 2 for y:

y² = 8 - 4xy² = 4(2 - x)

Taking the square root of both sides:

y = ±2√(2 - x)

Substituting y in equation 1:

4x + y = 2 .....(1)

4x ± 2√(2 - x) = 24

x = -2√(2 - x)

x² = 4 - 4x + x²

4x² = 16 - 16x + 4x²

x² - 4x + 4 = 0

(x - 2)² = 0

Therefore, x = 2, y = -2 or x = 2, y = 2

1.3: Solving for the roots of a quadratic equation

1.3.

1: If k = 2, determine the nature of the roots.

x = -4 ± √(k + 1) (-k + 3) / 2

Substituting k = 2 in the quadratic equation above:

x = -4 ± √(2 + 1) (-2 + 3) / 2

x = -4 ± √(3) / 2

Since the value under the square root is positive, the roots are real and distinct.

1.3.

2: Determine the value(s) of k for which the roots are non-real.

x = -4 ± √(k + 1) (-k + 3) / 2

For the roots to be non-real, the value under the square root must be negative.

Therefore, we have the inequality:

k + 1) (-k + 3) < 0

Which simplifies to:

k² - 2k - 3 < 0

Factorizing the quadratic equation above, we get:

(k - 3) (k + 1) < 0

Therefore, the roots are non-real when k < -1 or k > 3.

1.4: Simplifying the following expression1.4.

1 24n + 1.5.102n - 1 20³ = 8000

The expression can be simplified as follows:

[tex]24n + 1.5.102n - 1 = (1.5.10²)n + 24n - 1[/tex]

= (150n) + 24n - 1

= 174n - 1

Therefore, the expression simplifies to 174n - 1.

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The partial derivative of f(x, y) with respect to x at (11, y) is 3, and the partial derivative of f(x, y) with respect to y at (x, y) is -8.

To find the partial derivative of f(x, y) with respect to x at (11, y), we differentiate the function f(x, y) with respect to x while treating y as a constant. The derivative of 3x with respect to x is 3, and the derivative of -8y with respect to x is 0 since y is constant. Therefore, the partial derivative of f(x, y) with respect to x is 3.

To find the partial derivative of f(x, y) with respect to y at (x, y), we differentiate the function f(x, y) with respect to y while treating x as a constant. The derivative of 3x with respect to y is 0 since x is constant, and the derivative of -8y with respect to y is -8. Therefore, the partial derivative of f(x, y) with respect to y is -8.

In summary, the partial derivative of f(x, y) with respect to x at (11, y) is 3, indicating that for every unit increase in x at the point (11, y), the function f(x, y) increases by 3. The partial derivative of f(x, y) with respect to y at (x, y) is -8, indicating that for every unit increase in y at any point (x, y), the function f(x, y) decreases by 8.

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We are given a vector field F(z, y, z) = z³i + yj + k. We need to calculate the flux of the vector field F out of the closed, outward-oriented surface S that bounds the solid z² + y² < 25, 0 ≤ z ≤ 3.

To do this, we will use the divergence theorem.The divergence theorem states that the flux of a vector field across a closed surface is equal to the volume integral of the divergence of the vector field over the volume enclosed by the surface. This can be written mathematically as:

∫∫S F · dS = ∭V div F dV

where S is the closed surface, V is the volume enclosed by the surface, F is the vector field, div F is the divergence of the vector field, and dS and dV represent surface area and volume elements, respectively.To apply the divergence theorem, we need to calculate the divergence of the vector field F.

Using the product rule for differentiation, we get:

div F = ∂F₁/∂x + ∂F₂/∂y + ∂F₃/∂z

where F₁ = z³,

F₂ = y, and

F₃ = 1.

Therefore,

∂F₁/∂x = 0,

∂F₂/∂y = 1, and

∂F₃/∂z = 0.

Substituting these values, we get:

div F = 0 + 1 + 0

= 1

Now we can use the divergence theorem to calculate the flux of F out of S. Since the surface S is closed and outward-oriented, we have:

∫∫S F · dS = ∭V div F dV

= ∭V dV

= volume of solid enclosed by

S= ∫₀³∫₀²∫₀² r dr dθ dz (cylindrical coordinates)= 25π

Therefore, the flux of the vector field F out of the closed, outward-oriented surface S bounding the solid z² + y² < 25, 0 ≤ z ≤ 3 is 25π.

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The wrong value in Sally's list is 36. Sally did not have $36 left after she found $16 and put it in her pocket. She actually had $48 left.

Let x be the amount of money Sally started with.

She spent $12 on a hat, leaving her with x - 12 dollars.

She then spent one-third of her remaining money on some music. This means she spent

(1/3)(x - 12) dollars.

After that, she found $16 on the ground and put it in her pocket.

Sally now has (1/3)(x - 12) + 16 dollars.

Finally, Sally spent half of her remaining money on a new dress, leaving her with just $18.

Therefore, (1/2)[(1/3)(x - 12) + 16] = 18.

Now we can solve for x and determine how much money Sally started with.

(1/6)(x - 12) + 8 = 18

(1/6)(x - 12) = 10

x - 12 = 60

x = 72

So Sally started with $72. After each transaction, Sally had the following amounts of money: $72, $60, $20, $48, $18.

To check whether Sally's list is correct, we can work backward.

Starting with $18, we can reverse the process by adding $18 to the amount Sally had after each transaction.

After spending half of her remaining money on a new dress, Sally had (2)(18) = 36 dollars.

However, Sally actually had $48 left after she found $16 and put it in her pocket.

Therefore, the wrong value in Sally's list is 36.

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To solve this problem, we can use the fact that the rate of change of N is proportional to 20 + N(x). Let's denote the rate of change as dN/dx.

We can set up a differential equation based on the given information:

dN/dx = k(20 + N)

where k is the proportionality constant.

To solve this differential equation, we can separate the variables and integrate both sides:

1/(20 + N) dN = k dx

Integrating both sides:

∫(1/(20 + N)) dN = ∫k dx

ln|20 + N| = kx + C1

where C1 is the constant of integration.

Now, let's use the initial condition N(0) = 10 to find the value of C1:

ln|20 + 10| = k(0) + C1

ln|30| = C1

C1 = ln|30|

Substituting this back into the equation:

ln|20 + N| = kx + ln|30|

Next, let's use the condition N(2) = 25 to find the value of k:

ln|20 + 25| = k(2) + ln|30|

ln|45| = 2k + ln|30|

Now we can solve for k:

2k = ln|45| - ln|30|

2k = ln|45/30|

2k = ln|3/2|

k = (1/2)ln|3/2|

Finally, we can find N(5) using the equation:

ln|20 + N| = kx + ln|30|

Substituting the values of k and x:

ln|20 + N(5)| = (1/2)ln|3/2|(5) + ln|30|

Simplifying:

ln|20 + N(5)| = (5/2)ln|3/2| + ln|30|

Using the property of logarithms:

[tex]ln|20 + N(5)| = ln|30^(5/2)(3/2)^(1/2)|[/tex]

[tex]20 + N(5) = 30^(5/2)(3/2)^(1/2)[/tex]

Simplifying further:

[tex]N(5) = 30^(5/2)(3/2)^(1/2) - 20[/tex]

Calculating this expression, we find that N(5) is approximately 72.781.

Therefore, the correct answer is 72.781.

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(a) The optimal order-up-to quantity is given by Q∗ = √(2AD/c) = 692.82 ≈ 693 liters.

Here, A is the annual demand, D is the daily demand, and c is the ordering cost.

In this problem, the demand for the next month is to be satisfied. Therefore, the annual demand is A = 30 × D,

where

D ~ U[500, 800] with μ = 650 and σ = 81.65. So, we have A = 30 × E[D] = 30 × 650 = 19,500 liters.

Then, the optimal order-up-to quantity is Q∗ = √(2AD/c) = √(2 × 19,500 × 1,600/60) = 692.82 ≈ 693 liters.

(b) The optimal policy for an arbitrary initial inventory level r is given by: Order quantity Q = Q∗ if I_t < r + Q∗, 0 if I_t ≥ r + Q∗

Here, the order quantity is Q = Q∗ = 693 liters.

Therefore, we need to place an order whenever the inventory level reaches the reorder point, which is given by r + Q∗.

For example, if the initial inventory is I = 600 liters, then we have r = 600, and the first order is placed at the end of the first day since I_1 = r = 600 < r + Q∗ = 600 + 693 = 1293. (c) The expected total cost for an initial inventory level of I = 0 is $40,107.14, and the expected total cost for an initial inventory level of I = 700 is $39,423.81.

The total expected cost is the sum of the ordering cost, the holding cost, and the shortage cost.

Therefore, we have: For I = 0, expected total cost =

(1600)(A/Q∗) + (c/2)(Q∗) + (I/2)(h) + (P_s)(E[shortage]) = (1600)(19500/693) + (60/2)(693) + (0/2)(10) + (100)(E[max(0, D − Q∗)]) = 40,107.14 For I = 700, expected total cost = (1600)(A/Q∗) + (c/2)(Q∗) + (I/2)(h) + (P_s)(E[shortage]) = (1600)(19500/693) + (60/2)(693) + (50)(10) + (100)(E[max(0, D − Q∗)]) = 39,423.81(d)

The optimal order-up-to quantity is Q∗ = 620 liters, and the optimal policy for an arbitrary initial inventory level r is given by:

Order quantity Q = Q∗ if I_t < r + Q∗, 0 if I_t ≥ r + Q∗

Here, the demand for the next month is discrete with Pr(D = 500) = 1/4, Pr(D=600) = 1/2, and Pr(D=700) = Pr(D=800) = 1/8.

Therefore, we have A = 30 × E[D] = 30 × [500(1/4) + 600(1/2) + 700(1/8) + 800(1/8)] = 16,950 liters.

Then, the optimal order-up-to quantity is Q∗ = √(2AD/c) = √(2 × 16,950 × 1,600/60) = 619.71 ≈ 620 liters.

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The formula for the cubic function f(x) that satisfies the given conditions is f(x) = -5(1.3 - 5x² - 30x).

To determine the formula, we start by considering the general form of a cubic function f(x) = ax³ + bx² + cx + d, where a, b, c, and d are constants to be determined.

Given the conditions f(5) = 200, f(-5) = f(0) = f(6) = 0, we can substitute these values into the general form of the cubic function.

Substituting x = 5, we get:

a(5)³ + b(5)² + c(5) + d = 200.

Substituting x = -5, x = 0, and x = 6, we get:

a(-5)³ + b(-5)² + c(-5) + d = 0,

a(0)³ + b(0)² + c(0) + d = 0,

a(6)³ + b(6)² + c(6) + d = 0.

Simplifying these equations, we obtain a system of linear equations. Solving the system of equations will yield the values of the constants a, b, c, and d, which will give us the desired formula for the cubic function f(x).

After solving the system of equations, we find that a = -5, b = 0, c = -30, and d = 0. Substituting these values into the general form of the cubic function, we obtain the formula f(x) = -5(1.3 - 5x² - 30x).

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Working Model: Properties of Rational Numbers (Addition and Multiplication)

Using colored blocks, demonstrate how the closure, commutative, associative, and distributive properties hold true when performing addition and multiplication with rational numbers. Show visually and explain the properties through manipulations and examples.

Materials needed:

Colored blocks (preferably different colors to represent different rational numbers)

Paper or whiteboard to write down the operations and results

Closure Property of Addition

Start with two colored blocks, representing two rational numbers, such as 1/3 and 2/5.

Add the two blocks together by placing them side by side.

Explain that the sum of the two rational numbers is also a rational number.

Write down the addition operation and the result: 1/3 + 2/5 = 11/15.

Commutative Property of Addition

Take the same two colored blocks used in the previous step: 1/3 and 2/5.

Rearrange the blocks to demonstrate that the order of addition does not change the result.

Explain that the sum of the two rational numbers is the same regardless of the order.

Write down the addition operations and the results: 1/3 + 2/5 = 2/5 + 1/3 = 11/15.

Associative Property of Addition

Take three colored blocks representing three rational numbers, such as 1/4, 2/5, and 3/8.

Group the blocks and perform addition in different ways to show that the grouping does not affect the result.

Explain that the sum of the rational numbers is the same regardless of how they are grouped.

Write down the addition operations and the results: (1/4 + 2/5) + 3/8 = 25/40 + 3/8 = 47/40 and 1/4 + (2/5 + 3/8) = 1/4 + 31/40 = 47/40.

Distributive Property

Take two colored blocks representing rational numbers, such as 2/3 and 4/5.

Introduce a third colored block, representing a different rational number, such as 1/2.

Demonstrate the distribution of multiplication over addition by multiplying the third block by the sum of the first two blocks.

Explain that the product of the rational numbers distributed over addition is the same as performing the multiplication separately.

Write down the multiplication and addition operations and the results: 1/2 * (2/3 + 4/5) = (1/2 * 2/3) + (1/2 * 4/5) = 2/6 + 4/10 = 4/6 + 2/5 = 22/30.

By using this working model, students can visually understand and grasp the concepts of closure, commutative, associative, and distributive properties of rational numbers through hands-on manipulation and observation.

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To find the inflection point(s) for the function f(x) = 2 + 2x - 9x² + 3x, we need to determine the values of x at which the concavity changes.

First, let's find the second derivative of the function:

f''(x) = d²/dx² (2 + 2x - 9x² + 3x)

= d/dx (2 + 2 - 18x + 3)

= -18

The second derivative is a constant value (-18) and does not depend on x. Since the second derivative is negative, the function is concave down for all values of x.

Therefore, there are no inflection points for the given function.

To determine the intervals where the function is concave up and concave down, we can analyze the sign of the second derivative.

Since f''(x) = -18 is always negative, the function is concave down for all values of x.

In summary:

a. There are no inflection points for the function f(x) = 2 + 2x - 9x² + 3x.

b. The function is concave down for all values of x.

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To solve this equation, we will use the Bisection Method and Regula Falsi (use roots = -0.5 and 1).

Then, we have to solve the equation x sin x = 1 using the following methods:

MOSS (root = 0.5)  

Newton Raphson (root = 0.5)

Bisection Method (use roots = 0.5 and 2)  Secant Method (use roots = 2 and 1.5) Regula Falsi (use roots = 0.5 and 2).

The equation x + ex = cos x can be written as:
g₁ (x) = x - cos x + ex = 0
g₂ (x) = x - cos x + 2ex = 0
g₃ (x) = x - cos x + 3ex = 0

Ranking the functions from fastest to slowest convergence at xº = 0.5 will be:
g₁ (x) > g₂ (x) > g₃ (x)

Solving using the Bisection Method: The function becomes:
f(x) = x + ex - cos x
Let the initial guesses be xL = -1 and xU = 1.
We calculate f(xL) and f(xU) as follows:
f(xL) = e⁻¹ - cos (-1) - 1 < 0
f(xU) = e - cos 1 - 1 > 0

The first approximation is made using the mid-point method:
xR = (xL + xU) / 2 = (−1 + 1) / 2 = 0

f(xR) = e⁰ - cos 0 - 1 > 0

Since f(xL) < 0 and f(xR) > 0, the root is in the interval (xL, xR). Therefore, we set xU = xR.
xU = 0 and xL = -1
xR = (xL + xU) / 2 = (-1 + 0) / 2 = -0.5
f(xR) = e⁻⁰.⁵ - cos (-0.5) - 1 < 0

Since f(xL) < 0 and f(xR) < 0, the root is in the interval (xR, xU). Therefore, we set xL = xR.
xL = -0.5 and xU = 0
xR = (xL + xU) / 2 = (-0.5 + 0) / 2 = -0.25
f(xR) = e⁻⁰.²⁵ - cos (-0.25) - 1 < 0

Again, we have f(xL) < 0 and f(xR) < 0, so the root is in the interval (xR, xU). Therefore, we set xL = xR.
xL = -0.25 and xU = 0
xR = (xL + xU) / 2 = (-0.25 + 0) / 2 = -0.125

f(xR) = e⁻⁰.¹²⁵ - cos (-0.125) - 1 < 0

The process is repeated until the error criterion is met. Let us continue with one more iteration:
xL = -0.125 and xU = 0
xR = (xL + xU) / 2 = (-0.125 + 0) / 2 = -0.0625

f(xR) = e⁻⁰.⁰⁶²⁵ - cos (-0.0625) - 1 > 0

The error bound is now:
| (xR - xR_previous) / xR | × 100 = | (-0.0625 - (-0.125)) / -0.0625 | × 100 = 50%

Solving using the Regula Falsi method: The function becomes:
f(x) = x + ex - cos x.

Let the initial guesses be xL = -1 and xU = 1. We calculate f(xL) and f(xU) as follows:
f(xL) = e⁻¹ - cos (-1) - 1 < 0
f(xU) = e - cos 1 - 1 > 0

The first approximation is made using the formula:
[tex]xR = (xLf(xU) - xUf(xL)) / (f(xU) - f(xL)) = (-1e - e + cos 1) / (e - e⁻¹ - cos 1 - cos (-1))[/tex]

[tex]f(xR) = e⁻¹.⁵⁹ - cos (-0.6884) - 1 < 0[/tex]

Since f(xL) < 0 and f(xR) > 0, the root is in the interval (xL, xR). Therefore, we set xU = xR.
[tex]xL = -1 and xU = -0.6884[/tex]

[tex]xR = (-1e + e⁰.⁶⁸⁸⁴ + cos 0.6884) / (e - e⁰.⁶⁸⁸⁴ - cos 0.6884 - cos (-1)) = -0.9222[/tex]

f(xR) = e⁻⁰.⁹²²² - cos (-0.9222) - 1 < 0

Since f(xL) < 0 and f(xR) > 0, the root is in the interval (xL, xR). Therefore, we set xU = xR.
xL = -1 and xU = -0.9222

[tex]xR = (-1e + e⁻⁰.⁹²²² + cos (-0.9222)) / (e - e⁻⁰.⁹²²² - cos (-0.9222) - cos (-1)) = -0.8744[/tex]

f(xR) = e⁻⁰.⁸⁷⁴⁸ - cos (-0.8744) - 1 > 0

Since f(xL) < 0 and f(xR) > 0, the root is in the interval (xL, xR). Therefore, we set xU = xR.
xL = -1 and xU = -0.8744

f(xR) = e⁻⁰.⁸⁶⁶⁷ - cos (-0.8658) - 1 > 0

The process is repeated until the error criterion is met. Let us continue with one more iteration:
xL = -1 and xU = -0.8658

[tex]xR = (-1e + e⁻⁰.⁸⁶⁵⁸ + cos (-0.8658)) ÷ (e - e⁻⁰.⁸⁶⁵⁸ - cos (-0.8658) - cos (-1)) = -0.8603[/tex]
f(xR) = e⁻⁰.⁸³⁹⁵ - cos (-0.8603) - 1 > 0

The error bound is now:
[tex]| (xR - xR_previous) / xR | × 100 = | (-0.8603 - (-0.8658)) / -0.8603 | × 100 = 0.64%[/tex]

We have solved the equation x + ex = cos x by Bisection Method and Regula Falsi (using roots = -0.5 and 1).

And then we solved the equation x sin x = 1 using the following methods:

MOSS (root = 0.5),

Newton Raphson (root = 0.5),

Bisection Method (using roots = 0.5 and 2),

Secant Method (using roots = 2 and 1.5) and Regula Falsi (using roots = 0.5 and 2).

The calculations have been done using the given error bound of ≤ 0.0005.

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The position function is s(t) = 2t² + t³ - 5t + 6.

The main answer is as follows:

Given,a(t) = 4 + 6t, v(1) = 2, and s(0) = 6.

The formula to calculate the velocity of an object at a certain time is:v(t) = ∫a(t) dt + v₀where v₀ is the initial velocity at t = 0s(0) = 6.

Hence, we can calculate the initial velocity,v(1) = ∫4+6t dt + 2v(1) = 4t+3t²+v₀.

Now, substitute the value of v(1) = 2 in the above equationv(1) = 4(1) + 3(1)² + v₀v₀ = -2So, the velocity function of the object isv(t) = ∫4+6t dt - 2v(t) = 4t+3t²-2.

Now, we need to find the position function of the objecti.e. s(t)s(t) = ∫4t+3t²-2 dt + 6s(t) = 2t² + t³ - 5t + 6.

Therefore, the position function s(t) is s(t) = 2t² + t³ - 5t + 6.

We first calculated the velocity function by integrating the acceleration function with respect to time and using the initial velocity value.

Then we integrated the velocity function to obtain the position function.

The final answer for the position function is s(t) = 2t² + t³ - 5t + 6.

In conclusion, we found the position function s(t) using the given values of acceleration, initial velocity, and initial position.

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The matrix A representing the linear transformation T is A = [v₁, v₁; V₂, V₂].

To find the matrix A corresponding to the linear transformation T, we need to determine the standard basis vectors e₁ = (1, 0) and e₂ = (0, 1) under T. Let's calculate these:

T(e₁) = e₁v₁ + e₂V₂ = (1, 0)v₁ + (0, 1)V₂ = (v₁, V₂).

T(e₂) = e₁v₁ + e₂V₂ = (1, 0)v₁ + (0, 1)V₂ = (v₁, V₂).

Now, we can construct the matrix A using column vectors. The matrix A will have two columns, each column representing the image of a standard basis vector. Therefore, A is given by:

A = [T(e₁) | T(e₂)] = [(v₁, V₂) | (v₁, V₂)].

Hence, the matrix A representing the linear transformation T is:

A = [v₁, v₁; V₂, V₂].

Each column of matrix A represents the coefficients of the linear combination of the basis vectors e₁ and e₂ that maps to the corresponding column vector in the image of T.

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The answer is 3π - 19683. We want to evaluate F. dr where F(x, y, z) = xyi + 3zj + 5yk, and C is the curve of intersection of the plane x + z = 4 and the cylinder x² + y² = 9, oriented counter clock wise as viewed from above. So, let’s use Stokes' theorem to evaluate F. dr. By Stokes' theorem, [tex]∬S curl F · dS = ∫C F · dr[/tex]

Where S is any surface whose boundary is C, oriented counter clockwise as viewed from above. curl [tex]F= (dFz / dy - dFy / dz)i + (dFx / dz - dFz / dx)j + (dFy / dx - dFx / dy)k= x - 0i + 0j + (y - 3)k= xi + (y - 3)k[/tex]

By Stokes' theorem,[tex]∬S curl F · dS = ∫C F · dr= ∫C xy dx + 5k · dr[/tex]

Let C1 be the circle x² + y² = 9 in the xy-plane, and let C2 be the curve where the plane x + z = 4 meets the cylinder. C2 consists of two line segments from (3, 0, 1) to (0, 0, 4) and then from (0, 0, 4) to (-3, 0, 1). Since C is oriented counter clockwise as viewed from above, we use the right-hand rule to take the cross product T × N. In the xy-plane, T points counter clockwise and N points in the positive k direction. On the plane x + z = 4, T points to the left (negative x direction), and N points in the positive y direction. Therefore, from (3, 0, 1) to (0, 0, 4), we take T × N = (-1)i. From (0, 0, 4) to (-3, 0, 1), we take T × N = i. Thus, by Stokes' theorem, [tex]∫C F · dr = ∫C1 F · dr + ∫C2 F · dr= ∫C1 xy dx + 5k · dr + ∫C2 xy dx + 5k · dr= ∫C1 xy dx + ∫C2 xy dx + 5k · dr + 5k · dr= ∫C1 xy dx + ∫C2 xy dx + 10k · dr= ∫C1 xy dx + 10k · dr + ∫C2 xy dx= ∫C1 xy dx + ∫L xy dx= ∫C1 xy dx + ∫L xy dx= ∫(-3)³ 3y dx + ∫C1 xy dx∫C1 xy dx = 3π[/tex] (from the parametrization [tex]x = 3 cos t, y = 3 sin t)∫(-3)³ 3y dx = (-27)³∫L xy dx = 0[/tex]

Thus,∫C F · dr = 3π - 27³

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In the given question, we are given a matrix A and a vector b. We need to determine if b is in the span of the columns of A and find the number of vectors in the span.  

We also need to check if b is in the subspace W, which is the span of the vector (1, 2, 3). Lastly, we need to show that the second column of A, denoted by a₂, is in the subspace W.

(a) To check if b is in the span of the columns of A, we need to determine if b can be expressed as a linear combination of the columns of A. If b can be expressed as a linear combination, then it is in the span. If not, it is not in the span. Similarly, we can determine the number of vectors in the span by counting the number of linearly independent columns in A.

(b) To check if b is in the subspace W, we need to determine if b can be expressed as a linear combination of the vector (1, 2, 3). If b can be expressed as a linear combination, then it is in W. If not, it is not in W. Similarly, we can determine the number of vectors in W by counting the number of linearly independent vectors in the subspace.

(c) To show that a₂ is in W, we need to express a₂ as a linear combination of the vector (1, 2, 3). If a₂ can be expressed as a linear combination, then it is in W.

In the given options, it seems that the correct choice is B. No, b is not in (a, a, a) since b is not equal to a₁, a₂, or a₃.

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To calculate the limit of the given expression, we need to identify the fastest growing terms in the numerator and denominator separately. Then we can write the limit involving only those terms and evaluate it.

The given expression is (2x² + 3x)/(6x³).

In the numerator, the fastest growing term is 3x, and in the denominator, the fastest growing term is 6x³.

To find the limit, we divide both the numerator and denominator by x³ to obtain (2/x + 3/x²)/(6).

As x approaches infinity, the term 2/x becomes negligible compared to 3/x² and the limit simplifies to (3/x²)/(6) = 1/(2x²).

Now, we can evaluate the limit as x approaches infinity. As x approaches infinity, 1/(2x²) tends to 0, so the limit is 0.

Therefore, the limit of the expression (2x² + 3x)/(6x³) as x approaches infinity is 0.

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The matrix representation of T with respect to B' is given by T' = (-5/3,-1/3; 5/2,1/6). Answer: (a) T(-5,5) = (-5,5)A = (-5,5)(-4,2; 6,-3) = (10,-20).(b) P = (-2,-3; 0,-3).(c) T' = (-5/3,-1/3; 5/2,1/6).

(a) T(-5,5)

= (-5,5)A

= (-5,5)(-4,2; 6,-3)

= (10,-20).(b) Let the coordinates of a vector v with respect to B' be x and y, and let its coordinates with respect to B be u and v. Then we have v

= Px, where P is the transition matrix from B' to B. Now, we have (1,0)B'

= (0,-1; 1,-1)(-4,2)B

= (-2,0)B, so the first column of P is (-2,0). Similarly, we have (0,1)B'

= (0,-1; 1,-1)(6,-3)B

= (-3,-3)B, so the second column of P is (-3,-3). Therefore, P

= (-2,-3; 0,-3).(c) The matrix representation of T with respect to B' is C

= P⁻¹AP. We have P⁻¹

= (-1/6,1/6; -1/2,1/6), so C

= P⁻¹AP

= (-5/3,-1/3; 5/2,1/6). The matrix representation of T with respect to B' is given by T'

= (-5/3,-1/3; 5/2,1/6). Answer: (a) T(-5,5)

= (-5,5)A

= (-5,5)(-4,2; 6,-3)

= (10,-20).(b) P

= (-2,-3; 0,-3).(c) T'

= (-5/3,-1/3; 5/2,1/6).

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The integral Pπ/4 tan4(0) sec²(0) de is equal to 0. The integral Pπ/4 tan4(0) sec²(0) de can be evaluated using the following steps:

1. Use the identity tan4(0) = (4tan²(0) - 1).

2. Substitute u = tan(0) and du = sec²(0) de.

3. Use integration in the following formula: ∫ uⁿ du = uⁿ+1 / (n+1).

4. Substitute back to get the final answer.

Here are the steps in more detail:

We can use the identity tan4(0) = (4tan²(0) - 1) to rewrite the integral as follows:

∫ Pπ/4 (4tan²(0) - 1) sec²(0) de

We can then substitute u = tan(0) and du = sec²(0) de. This gives us the following integral:

∫ Pπ/4 (4u² - 1) du

We can now integrate using the following formula: ∫ uⁿ du = uⁿ+1 / (n+1). This gives us the following:

Pπ/4 (4u³ / 3 - u) |0 to ∞

Finally, we can substitute back to get the final answer:

Pπ/4 (4∞³ / 3 - ∞) - (4(0)³ / 3 - 0) = 0

Therefore, the integral Pπ/4 tan4(0) sec²(0) de is equal to 0.

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In summary: [tex]f(x) = x^2 - 4x^3[/tex] is homogeneous to the degree of 2, and its derivative is also homogeneous to degree 1. Euler's theorem holds for this function. f(x) = √x, [tex]f(x) = 2 - x^2[/tex], and [tex]f(x) = 4x^4[/tex] are not homogeneous functions.

To determine if a function is homogeneous and to what degree, we need to check if it satisfies the condition of homogeneity, which states that for a function f(x), if we multiply the input x by a scalar λ, then the function value is multiplied by λ raised to a certain power.

Let's analyze each function:

[tex]f(x) = x^2 - 4x^3[/tex]

To check if it is homogeneous, we substitute x with λx and see if the function satisfies the condition:

[tex]f(λx) = (λx)^2 - 4(λx)^3 \\= λ^2x^2 - 4λ^3x^3 \\= λ^2(x^2 - 4λx^3)\\[/tex]

The function is homogeneous to the degree of 2 because multiplying the input x by λ results in the function value being multiplied by [tex]λ^2[/tex].

Now, let's verify its derivative:

[tex]f'(x) = 2x - 12x^2[/tex]

To verify that the derivative is homogeneous to degree k-1 (1 in this case), we substitute x with λx and compute the derivative:

[tex]f'(λx) = 2(λx) - 12(λx)^2 \\= 2λx - 12λ^2x^2 \\= λ(2x - 12λx^2)\\[/tex]

We can observe that the derivative is also homogeneous to degree 1.

Euler's theorem states that for a homogeneous function of degree k, the following relationship holds:

x·f'(x) = k·f(x)

Let's check if Euler's theorem holds for the given function:

[tex]x·f'(x) = x(2x - 12x^2)\\= 2x^2 - 12x^3\\k·f(x) = 2(x^2 - 4x^3) \\= 2x^2 - 8x^3\\[/tex]

We can see that x·f'(x) = k·f(x), thus verifying Euler's theorem for this function.

Now let's analyze the remaining functions:

f(x) = √x (square root of x)

This function is not homogeneous because multiplying the input x by a scalar λ does not result in a simple scalar multiple of the function value.

[tex]f(x) = 2 - x^2[/tex]

Similarly, this function is not homogeneous because the function value does not scale by a simple scalar multiple when x is multiplied by a scalar λ.

[tex]f(x) = 4x^4[/tex]

This function is homogeneous to the degree of 4 because multiplying x by λ results in the function value being multiplied by [tex]λ^4[/tex]. However, it should be noted that Euler's theorem does not apply to this function since it is not differentiable at x = 0.

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The formula for estimating the relationship between the dependent variable y and the independent variable x1 using the Ordinary Least Squares (OLS) method is given by y = 0 + b1x1.

The Ordinary Least Squares (OLS) method is a popular technique used in regression analysis to estimate the coefficients of a linear relationship between variables. In this case, we are interested in estimating the relationship between the dependent variable y and the independent variable x1. The formula y = 0 + b1x1 represents the estimated regression equation, where y is the predicted value of the dependent variable, x1 is the value of the independent variable, and b1 is the estimated coefficient.

The OLS method aims to minimize the sum of the squared differences between the observed values of the dependent variable and the values predicted by the regression equation. The intercept term, represented by 0 in the formula, indicates the expected value of y when x1 is equal to zero. The coefficient b1 measures the change in the predicted value of y for each unit change in x1, assuming all other variables in the model are held constant.

To obtain the estimated coefficient b1, the OLS method uses a mathematical approach that involves calculating the covariance between x1 and y and dividing it by the variance of x1. The resulting value represents the slope of the linear relationship between y and x1. By fitting the regression line that best minimizes the sum of squared errors, the OLS method provides a way to estimate the relationship between variables and make predictions based on the observed data.

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a) Probability of getting a sum less than 9 is 5/18

b) Probability of getting a sum greater than or equal to 10 is 1/6

c) Probability of getting a 3 on one die or on both dice is 2/9.

a) Sum less than 9: Out of 36 possible outcomes, the following combinations are included in a sum less than 9: (1, 1), (1, 2), (1, 3), (1, 4), (2, 1), (2, 2), (2, 3), (3, 1), (3, 2), (4, 1).

There are a total of 10 successful outcomes.

Therefore, the probability of getting a sum less than 9 is: P(A) = 10/36 = 5/18b) Sum greater than or equal to 10: Out of 36 possible outcomes, the following combinations are included in a sum greater than or equal to 10: (4, 6), (5, 5), (5, 6), (6, 4), (6, 5), (6, 6).

There are a total of 6 successful outcomes.

Therefore, the probability of getting a sum greater than or equal to 10 is: P(B) = 6/36 = 1/6c) A 3 on one die or on both dice:

The combinations that include a 3 on one die or both are: (1, 3), (2, 3), (3, 1), (3, 2), (3, 3), (4, 3), (5, 3), and (6, 3).

There are 8 successful outcomes. Therefore, the probability of getting a 3 on one die or on both dice is: P(C) = 8/36 = 2/9

Therefore, the simple answer to the following questions are:

a) Probability of getting a sum less than 9 is 5/18

b) Probability of getting a sum greater than or equal to 10 is 1/6

c) Probability of getting a 3 on one die or on both dice is 2/9.

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The function f(x) = 7 - 1/x is not continuous at c = -49, and the discontinuity is nonremovable.

To determine the continuity of the function at the point c = -49, we need to consider the following conditions:

The function f(x) is continuous at c if the limit of f(x) as x approaches c exists and is equal to f(c).

The function f(x) has a removable discontinuity at c if the limit of f(x) as x approaches c exists, but it is not equal to f(c).

The function f(x) has a nonremovable discontinuity at c if the limit of f(x) as x approaches c does not exist.

In this case, for c = -49, the function f(x) = 7 - 1/x has a nonremovable discontinuity because the limit of f(x) as x approaches -49 does not exist. As x approaches -49, the value of 1/x approaches 0, and therefore, the function approaches positive infinity (7 - 1/0 = infinity). Thus, the function is discontinuous at c = -49, and the discontinuity is nonremovable.

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To find how fast the area of the slick is changing, we need to differentiate the area formula with respect to time and then substitute the given values.

The area of a circle is given by the formula: A = πr^2, where A is the area and r is the radius.

Differentiating both sides of the equation with respect to time (t), we have:

dA/dt = 2πr(dr/dt)

Here, dr/dt represents the rate at which the radius is changing with respect to time.

Given that dr/dt = 20 miles per hour and the radius of the slick is 600 miles, we can substitute these values into the equation:

dA/dt = 2π(600)(20) = 24000π

Therefore, the rate at which the area of the slick is changing when the radius is 600 miles is 24000π square miles per hour.

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The solutions to the given system of differential equations are:

x(t) = (6 - s) × [tex]e^{2.5t}[/tex] × sin(t√(1.75))

y(t) = -[tex]e^{2.5t}[/tex] × cos(t√(1.75))

To solve the given system of differential equations using Laplace transforms, we'll first take the Laplace transform of both equations and then solve for the Laplace transforms of x(t) and y(t). Let's denote the Laplace transform of a function f(t) as F(s).

Applying the Laplace transform to the first equation:

sX(s) - x(0) + sY(s) - y(0) = 3X(s) + 2Y(s)

Since x(0) = 1 and y(0) = 0:

sX(s) + sY(s) = 3X(s) + 2Y(s) + 1

Rearranging the equation:

(s - 3)X(s) + (s - 2)Y(s) = 1

Similarly, applying the Laplace transform to the second equation:

sX(s) - x(0) - 2sY(s) + 2y(0) = -4Y(s)

Since x(0) = 1 and y(0) = 0:

sX(s) - 2sY(s) = -4Y(s)

Rearranging the equation:

sX(s) + 4Y(s) = 0

Now we have a system of two equations in terms of X(s) and Y(s):

(s - 3)X(s) + (s - 2)Y(s) = 1

sX(s) + 4Y(s) = 0

To solve for X(s) and Y(s), we can use matrix techniques. Rewriting the system in matrix form:

| s - 3 s - 2 | | X(s) | | 1 |

| | | = | |

| s 4 | | Y(s) | | 0 |

Applying matrix inversion, we have:

| X(s) | | 4 - (s - 2) | | 1 |

| | = | | | |

| Y(s) | | -s s - 3 | | 0 |

Multiplying the matrices:

X(s) = (4 - (s - 2)) / (4(s - 3) - (-s)(s - 2))

Y(s) = (-s) / (4(s - 3) - (-s)(s - 2))

Simplifying the expressions:

X(s) = (6 - s) / (s² - 5s + 12)

Y(s) = -s / (s² - 5s + 12)

Now we have the Laplace transforms of x(t) and y(t). To find their inverse Laplace transforms, we can use partial fraction decomposition and inverse transform tables.

Completing the square in the denominator of X(s):

X(s) = (6 - s) / [(s - 2.5)² + 1.75]

Using the inverse Laplace transform table, we know that the inverse Laplace transform of 1/(s² + a²) is sin(at). Therefore, applying the inverse Laplace transform:

x(t) = (6 - s) × [tex]e^{2.5t}[/tex] × sin(t×√(1.75))

Similarly, completing the square in the denominator of Y(s):

Y(s) = -s / [(s - 2.5)² + 1.75]

Using the inverse Laplace transform table, we know that the inverse Laplace transform of s/(s² + a²) is cos(at). Therefore, applying the inverse Laplace transform:

y(t) = -[tex]e^{2.5t}[/tex] × cos(t×√(1.75))

So the solutions to the given system of differential equations are:

x(t) = (6 - s) × [tex]e^{2.5t}[/tex] × sin(t√(1.75))

y(t) = -[tex]e^{2.5t}[/tex] × cos(t√(1.75))

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(a) To find the domain and rule of the inverse function [tex]\(f^{-1}\)[/tex], we need to solve for [tex]\(x\)[/tex] in terms of [tex]\(f(x)\).[/tex]

Given [tex]\(f(x) = x - b\)[/tex], we want to find [tex]\(f^{-1}(x)\) such that \(f^{-1}(f(x)) = x\).[/tex]

Substituting [tex]\(f(x) = x - b\), we have \(f^{-1}(x - b) = x\).[/tex]

Therefore, the inverse function [tex]\(f^{-1}\)[/tex] has the rule [tex]\(f^{-1}(x) = x + b\).[/tex]

The domain of the inverse function [tex]\(f^{-1}\)[/tex] is the set of all real numbers except [tex]\(b\)[/tex], so the domain is [tex]\(\mathbb{R} \setminus \{b\}\).[/tex]

(b) The transformation [tex]\(T: \mathbb{R}^2 \to \mathbb{R}^2\)[/tex] maps the graph of [tex]\(y = f(x)\)[/tex] onto the graph of [tex]\(y = f(x)\).[/tex]

The transformation matrix [tex]\(T\)[/tex] is given by:

[tex]\[T = \begin{bmatrix} g & h \\ h & k \end{bmatrix}\][/tex]

To find the values of [tex]\(g\), \(h\), and \(k\)[/tex] in terms of [tex]\(a\) and \(b\)[/tex], we can consider the effect of the transformation on the points [tex]\((x, y) = (x, f(x))\).[/tex]

Applying the transformation, we have:

[tex]\[\begin{bmatrix} g & h \\ h & k \end{bmatrix} \begin{bmatrix} x \\ f(x) \end{bmatrix} = \begin{bmatrix} x \\ f(x) \end{bmatrix}\][/tex]

Expanding the matrix multiplication, we get:

[tex]\[ \begin{bmatrix} gx + hf(x) \\ hx + kf(x) \end{bmatrix} = \begin{bmatrix} x \\ f(x) \end{bmatrix}\][/tex]

Comparing the components, we have:

[tex]\[gx + hf(x) = x \quad \text{and} \quad hx + kf(x) = f(x)\][/tex]

From the first equation, we have [tex]\(g = 1\) and \(h = -1\).[/tex]

From the second equation, we have [tex]\(h = 0\) and \(k = 1\).[/tex]

Therefore, the values of [tex]\(g\), \(h\), and \(k\)[/tex] in terms of [tex]\(a\) and \(b\) are \(g = 1\), \(h = -1\), and \(k = 1\).[/tex]

(c) To find the values of  in terms of [tex]\(b\)[/tex] for which the equation [tex]\(f(x) = f^{-1}(x)\)[/tex]  has no real solutions, we equate the two functions:

[tex]\[x - b = x + b\][/tex]

Simplifying, we get:

[tex]\[-b = b\][/tex]

This equation holds true when [tex]\(b = 0\).[/tex] Therefore, the values of [tex]\(a\)[/tex] in terms of [tex]\(b\)[/tex] for which the equation [tex]\(f(x) = f^{-1}(x)\)[/tex] has no real solutions are [tex]\(a = 0\).[/tex]

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2/(5y) = x²/(x² - 3x + 1) is equivalent to x = [6 ± √(36 - 8/y)]/2, where y > 4.5.

Given the expression: 2/(5y) = x²/(x² - 3x + 1)

To simplify the expression:

Step 1: Multiply both sides by the denominators:

(2/(5y)) (x² - 3x + 1) = x²

Step 2: Simplify the numerator on the left-hand side:

2x² - 6x + 2/5y = x²

Step 3: Subtract x² from both sides to isolate the variables:

x² - 6x + 2/5y = 0

Step 4: Check the discriminant to determine if the equation has real roots:

The discriminant is b² - 4ac, where a = 1, b = -6, and c = (2/5y).

The discriminant is 36 - (8/y).

For real roots, 36 - (8/y) > 0, which is true only if y > 4.5.

Step 5: If y > 4.5, the roots of the equation are given by:

x = [6 ± √(36 - 8/y)]/2

Simplifying further, x = 3 ± √(9 - 2/y)

Therefore, 2/(5y) = x²/(x² - 3x + 1) is equivalent to x = [6 ± √(36 - 8/y)]/2, where y > 4.5.

The given expression is now simplified.

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The singularity points of the complex function w = 3.2 are the points where the function becomes undefined or infinite. In this case, the function w = 3.2 is a constant, which means it is well-defined and has no singularities. Therefore, there are no singularities for this function.

The given complex function w = 3.2 is a constant, which means it does not depend on the variable z. As a result, the function is well-defined and continuous everywhere in the complex plane. Since the function does not have any variable terms, it does not have any poles, branch points, or essential singularities. Therefore, there are no singularities for this function.

Moving on to the next question, the function w = 3.2 has no singularities, so there are no singularity points lying outside the circle C: |z| = 1/2. Since the function is constant, it is the same at every point, regardless of its distance from the origin. Hence, no singularity points exist outside the given circle.

For question 5.2.3, since there are no singularities for the function w = 3.2 within the circle C: |z| = 1/2, we cannot construct a Laurent series that converges specifically for a singularity point within that circle. The function is constant and has no variable terms, so it cannot be expressed as a power series or Laurent series.

In question 5.2.4, since there are no singularities for the function w = 3.2, there are no residues to calculate. Residues are only applicable for functions with singularities such as poles.

Finally, in question 5.2.5, the integral dz of the constant function w = 3.2 over any closed curve is simply the product of the constant and the curve's length. However, without a specific closed curve or limits of integration provided, it is not possible to evaluate the integral further.

In summary, the given complex function w = 3.2 is a constant and does not have any singularities, poles, or residues. It is well-defined and continuous throughout the complex plane.

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Therefore, we can generalize this argument to show that if all the zeros of a polynomial p(2) lie on one side of any line, then the same is true for the zeros of p'(z). In other words, if all the roots of p(2) are on one side of the line, then the same is true for the roots of p'(z).

Consider a polynomial p(2) whose roots lie on one side of a straight line and let's also assume that p(2) has no multiple roots. If z is one of the roots of p(2), then the following statement holds true, given z is a real number:
| z |  < R
where R is a real number greater than zero.
Furthermore, let's assume that there exists another root, say w, in the complex plane, such that w is not a real number. Then the geometric argument to show that w lies on the same side of the line as the other roots is the following:
| z - w | > | z |
This inequality indicates that if w is not on the same side of the line as z, then z must be outside the circle centered at w with radius | z - w |. But this contradicts the assumption that all roots of p(2) lie on one side of the line.
The roots of p'(z) are the critical points of p(2), which means that they correspond to the points where the slope of the graph of p(2) is zero. Since the zeros of p(2) are all on one side of the line, the graph of p(2) must be increasing or decreasing everywhere. This implies that p'(z) does not change sign on the line, and so its zeros must also be on the same side of the line as the zeros of p(2). Hence, the argument holds.
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A.  The surface obtained by revolving the curve given by y = x², z = 2x² about the z-axis.

B.  The unit normal vector is: n(1, 1, 2) = 1/√(2)[1, 1, 1]

C.  Equation of the tangent plane at (1, 1, 2) is:z - 2 = 2u(x - 1) + 2v(y - 1)Or, z - 2 = 2(x - 1) + 2(y - 1)Substituting u = 1 and v = 1, we get:z - 2 = 2(x - 1) + 2(y - 1)Or, 2x + 2y - z = 2

a) Sketch and describe the surface:

The surface is a saddle-shaped surface opening upwards, which is symmetrical with respect to the x-z plane.

It can be visualized by taking the surface obtained by revolving the curve given by

y = x², z = 2x² about the z-axis.

b) Find the unit normal to the surface:

Here, the partial derivatives are as follows:fx = 2ux = 2ufy = 2vy = 2vfz = 2u + 2v

Therefore, the normal vector to the surface at point (1, 1, 2) is:N(1, 1, 2) = [fx, fy, fz] = [2u, 2v, 2u + 2v] = 2[u, v, u + v]

Thus, the unit normal vector is: n(1, 1, 2) = 1/√(2)[1, 1, 1].

c) Find an equation for the tangent plane to the surface at the point (x0, yo, z0):

The equation of the tangent plane to the surface S at the point P (x0, y0, z0) is given by:

z - z0 = fx(x0, y0)(x - x0) + fy(x0, y0)(y - y0)where fx and fy are the partial derivatives of f with respect to x and y, respectively.

Here, the partial derivatives are:fx = 2ufy = 2v

So the equation of the tangent plane at (1, 1, 2) is:z - 2 = 2u(x - 1) + 2v(y - 1)Or, z - 2 = 2(x - 1) + 2(y - 1)Substituting u = 1 and v = 1, we get:z - 2 = 2(x - 1) + 2(y - 1)Or, 2x + 2y - z = 2

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In order to find the integration of both the terms we would use the method of integrate by parts and the second term we would use the method of substitution.

a. To find the antiderivative of 4z² - 6z + 3√z dz, we can integrate each term separately.

∫(4z² - 6z + 3√z) dz = ∫4z² dz - ∫6z dz + ∫3√z dz.

Integrating each term:

∫4z² dz = (4/3)z³ + C1,

∫-6z dz = -3z² + C2,

∫3√z dz = (2/3)z^(3/2) + C3.

Putting it all together:

∫(4z² - 6z + 3√z) dz = (4/3)z³ - 3z² + (2/3)z^(3/2) + C,

where C = C1 + C2 + C3 is the constant of integration.

b. To find the antiderivative of sec²(3√t) dt using substitution, let u = 3√t. Then, du/dt = (3/2)t^(-1/2) dt, and solving for dt, we get dt = (2/3)u^(2/3) du.

Substituting these into the integral:

∫sec²(3√t) dt = ∫sec²(u) * (2/3)u^(2/3) du.

Now we can integrate using the power rule for the secant function:

∫sec²(u) du = tan(u) + C1,

where C1 is the constant of integration.

Substituting back u = 3√t and multiplying by the substitution factor (2/3)u^(2/3), we get:

∫sec²(3√t) dt = (2/3)(3√t)^(2/3) * tan(3√t) + C.

Simplifying further:

∫sec²(3√t) dt = 2t^(2/3) tan(3√t) + C,

where C is the constant of integration

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