Find y as a function of x if y^m– 16y^n+ 63y' = 0, y(0) = 8, y'(0) = 2, y^n(0) = 3. y(x) =

The answer is 0. It is not DNE or [infinity] or -[infinity] because as x approaches infinity, the denominator (5x+5) grows much faster than the numerator (ln(3x+4)).

To evaluate the limit, you can apply L'Hôpital's Rule when the limit approaches the form 0/0 or ∞/∞ as x→∞. In this case, the limit is in the form ∞/∞, so you can apply L'Hôpital's Rule:

lim (x→∞) ln(3x + 4)/(5x + 5)

Taking the derivative of the numerator and denominator with respect to x:

d/dx(ln(3x + 4)) = (3)/(3x + 4)
d/dx(5x + 5) = 5

Now, the limit becomes:

lim (x→∞) (3)/(3x + 4) / 5

Simplify the expression by dividing by 5:

lim (x→∞) (3/5)/(3x + 4)

As x→∞, the denominator (3x + 4) becomes very large, and the entire fraction approaches 0. Therefore, the limit exists, and the answer is:

Limit = 0

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Test is left tailed So critical region is   t < - 3.106.

What does the term "critical value" mean?

A criticial value is the test statistic's value that establishes a confidence interval's upper and lower boundaries or the level of statistical significance for a given test.

Z: To determine crucial value. Knowing whether a test is upper-tailed, lower-tailed, or two-tailed is necessary to determine critical value. For instance, the critical value is 1.645 if Za = 0.05 and an upper tailed test is used. It is -1.645 for a test with fewer tails.

Here we have given that  n = 12 and alpha = 0.005

We have to find critical region for left tailed t test,

So degress of freedom   = df = n- 1 = 12-1  =   11

So for df = 11 and left tailed test alpha = 0.005

Using t table, (Check attachement)

So critical value  = 3.106, Test is left tailed So critical region is   t < - 3.106

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In a 4x3x2x2 factorial experiment, you have 4 independent variables and potentially 4 main effect hypotheses.

The 4 independent variables are represented by the four numbers in the experimental design

(i.e., 4 levels of variable A, 3 levels of variable B, 2 levels of variable C, and 2 levels of variable D).

The potentially 4 main effect hypotheses are one for each independent variable, which states that there is a significant effect of that independent variable on the outcome variable.

A factorial experiment includes multiple factors simultaneously, each consisting of two or more

levels. Many factors simultaneously influence what is studied in a factorial experiment, and

experimenters consider the main effects and interactions between factors.

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Yes, the distributor has a right to complain to the water bottling company because a 1-gallon bottle containing exactly 1-gallon of water lies outside the 95% confidence interval.

The 95% confidence interval is a statistical measure that provides a range of values within which a true population parameter is likely to fall with 95% confidence. If a 1-gallon bottle containing exactly 1-gallon of water lies outside this confidence interval, it means that the actual quantity of water in the bottle is either significantly higher or significantly lower than the expected amount. This indicates a potential issue with the accuracy or consistency of the water bottling process.

The fact that the measured quantity of water falls outside the 95% confidence interval suggests that there may be inconsistencies or errors in the water bottling process, resulting in variations in the amount of water being filled into the bottles. This can be a valid reason for the distributor to complain to the water bottling company, as it indicates a lack of quality control and adherence to standards in the production process.

Therefore, based on the results indicating that a 1-gallon bottle containing exactly 1-gallon of water lies outside the 95% confidence interval, the distributor has a right to complain to the water bottling company about the potential inconsistency in the quantity of water in the bottles.

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It is important to note that this statement is about the process of constructing intervals, not about any particular interval we might construct.

To construct a 95% two-sided confidence interval on the death rate from lung cancer, we need to know the sample proportion, sample size, and the level of confidence. Given the problem statement, we have:

Sample proportion (P) = 360/900 = 0.4

Sample size (n) = 900

Level of confidence = 95%

We can use the formula for the confidence interval for a population proportion as follows:

Confidence interval = P ± zα/2 * √(P(1-P)/n)

where P is the sample proportion, n is the sample size, zα/2 is the z-value from the standard normal distribution with a level of significance of α/2 (α/2 = 0.025 for a 95% confidence interval).

To find the z-value, we can use a z-table or a calculator. Using a calculator, we find the z-value for α/2 = 0.025 to be 1.96.

Substituting the values into the formula, we get:

Confidence interval = P ± zα/2 * √(P(1-P)/n)

Confidence interval = 0.4 ± 1.96 * √(0.4(1-0.4)/900)

Confidence interval = 0.4 ± 0.034

Therefore, the 95% two-sided confidence interval on the death rate from lung cancer is (0.366, 0.434).

This means that we are 95% confident that the true death rate from lung cancer falls within this interval. It is important to note that this statement is about the process of constructing intervals, not about any particular interval we might construct.

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1. The General solution of the differential equation is y(x) = C1 * e⁻³ˣ + C2 * xe⁻³ˣ - (1/6)x² * e⁴ˣ.

2 . The General solution of the differential equation is y(x) = C1 + C2 * x + C3 * x² + eˣ + 21.

1. To solve the differential equation y'' + 6y' + 9y = -xe⁴ˣ by undetermined coefficients, we first find the complementary solution and then the particular solution.

Complementary solution: r² + 6r + 9 = 0. Solving the quadratic equation, r = -3 (double root). Hence, yc(x) = C1 * e⁻³ˣ + C2 * xe⁻³ˣ.

Particular solution: Assume yp(x) = Ax² * e⁴ˣ. Then, yp''(x) + 6yp'(x) + 9yp(x) = -xe⁴ˣ. Plugging in and solving, we find A = -1/6.

Thus, y(x) = C1 * e⁻³ˣ + C2 * xe⁻³ˣ - (1/6)x² * e⁴ˣ.

2. To solve the differential equation y''' - 3y'' + 3y' - y = eˣ - x + 21 by undetermined coefficients, we follow the same approach.

Complementary solution: r³ - 3r² + 3r - 1 = 0. Solving, r = 1 (triple root). Hence, yc(x) = C1 + C2 * x + C3 * x².

Particular solution: Assume yp(x) = A * eˣ + Bx³ + C. Then, yp'''(x) - 3yp''(x) + 3yp'(x) - yp(x) = eˣ - x + 21. Solving, we find A = 1, B = 0, and C = 21.

Thus, y(x) = C1 + C2 * x + C3 * x² + eˣ + 21.

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the likelihood of the arbitrary(random) number generator producing a 6 is 1/10 or 0.1. 

A uniform distribution may be a likelihood distribution in which all conceivable results are similarly likely.

Within the case of an arbitrary number generator that creates numbers irregular numbers between and 9 comprehensive taking after a uniform distribution, each number has the same likelihood of being produced, which is 1/10 (or 0.1).

This implies that the likelihood of producing any particular number, such as 6, is additionally 1/10 (or 0.1).

The concept of a uniform distribution is imperative in insights and likelihood hypothesis since it permits us to demonstrate circumstances where we have no reason to accept that any specific result is more likely than any other result.

For illustration, in case we were rolling a reasonable six-sided pass on, we would anticipate each number to be similarly likely to come up.

In rundown, the uniform distribution could be a simple but imperative concept in the likelihood hypothesis, and it is regularly utilized to demonstrate circumstances where all results are similarly likely.

Within the case of an arbitrary number generator that creates numbers arbitrary numbers between and 9 comprehensive taking after a uniform distribution, each number has the same likelihood of being produced, which is 1/10 (or 0.1). 

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The closest time between Tom and Sam is 12:00 pm.

To find the time at which Tom and Sam are the closest, follow these steps:

1. Set up a coordinate system with Tom's house at the origin (0,0). At 10 am, Tom starts moving east and Sam is 100 km south of Tom's house, located at (0,-100).

2. Calculate the positions of Tom and Sam at any time t. Tom's position is (5t,0), and Sam's position is (0,-100+8t).

3. Use the distance formula to find the distance between Tom and Sam: D(t) = sqrt((5t-0)² + (0-(-100+8t))²).

4. Differentiate D(t) with respect to time t to find the rate of change of distance between Tom and Sam.

5. Set the derivative equal to zero and solve for t. The result is t=2 hours.

6. Add 2 hours to the starting time of 10 am to get the actual time of 12:00 pm when Tom and Sam are closest.

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The asymptote of the following function f(x) = x/ (x-1)(x+2) is; A: At x = negative 2, limit of f (x) as x approaches negative 2 minus = negative infinity and limit of f (x) as x approaches negative 2 plus = infinity.

A vertical asymptote of a graph is a vertical line x = a where the graph tends toward positive or negative infinity as the inputs approach a.

For example is a value of x for which the denominator of the function is 0, and the function approaches infinity for these values of x.

We are given the function;

f(x) = x/ (x-1)(x+2)

Vertical asymptote:

Point in which the denominator is 0, so:

(x + 2) = 0

x = -2

Thus, we conclude that x = -2 is the vertical asymptote

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The equation of the oblique asymptote of the average cost function C(x) is calculated to be y = 0.02x + 95.

The average cost function is given by:

AC(x) = C(x)/x

Substituting C(x) = 14,000 + 95x + 0.02x^2, we get:

AC(x) = (14,000 + 95x + 0.02x^2)/x

Dividing the numerator by x, we get:

AC(x) = 14,000/x + 95 + 0.02x

As x approaches infinity, the 14,000/x term becomes negligible compared to the other terms, so the oblique asymptote of AC(x) is y = 0.02x + 95.

Therefore, the equation of the oblique asymptote of the average cost function C(x) is y = 0.02x + 95.

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For problem 19: The value of the integral is (5π/2).

For problem 22: The value of the integral is  [(e²)/2 - 1/2]

For problem 19, it is difficult to integrate in the given order of integration because the limits of integration for y depend on the value of x. To change the order of integration, we can integrate with respect to y first and then with respect to x. So the new iterated integral becomes:

∫ from 0 to 2π ∫ from 0 to ln(7/2) eˣ dy dx

Evaluating this integral, we get:

∫ from 0 to 2π (e^(ln(7/2)) - e⁰) dx

= ∫ from 0 to 2π (7/2 - 1) dx

= (7/2 - 1) * (2π - 0)

= 7π/2 - π

= (5π/2)

Therefore, the value of the iterated integral is (5π/2).

For problem 22, it is difficult to integrate in the given order of integration because the limits of integration for y depend on the value of x. To change the order of integration, we can integrate with respect to y first and then with respect to x. So the new iterated integral becomes:

∫ from 1 to e ∫ from ln y to 1 1 + ln y dy dx

Evaluating this integral, we get:

∫ from 1 to e (∫ from ln y to 1 1 + ln y dy) dx

= ∫ from 1 to e (y(ln y - 1) + y) dx

= ∫ from 1 to e (xy - x + x) dx

= ∫ from 1 to e (xy) dx

= [(e²)/2 - 1/2]

Therefore, the value of the iterated integral is [(e²)/2 - 1/2].

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When the population standard deviation is unknown and the sample size is less than 30, we need to use the t-distribution to compute a confidence interval for the mean, and we should consult a t-table to find the appropriate t-value based on the degrees of freedom and the desired level of confidence.

When the population standard deviation is unknown and the sample size is less than 30, we need to use the t-distribution to compute a confidence interval for the mean.

The t-distribution is similar to the standard normal distribution, but with heavier tails, and it is used when the population standard deviation is unknown.

To compute the confidence interval for the mean using the t-distribution, we need to find the appropriate t-value from a t-table. The t-table provides critical values for different degrees of freedom and levels of confidence.

The degrees of freedom for a t-distribution with a sample size of n is (n-1). For example, if we have a sample size of 20, the degrees of freedom would be 19.

To find the appropriate t-value from the t-table, we need to know the degrees of freedom and the desired level of confidence. For example, if we have a sample size of 20 and want to calculate a 95% confidence interval, we would look for the t-value with 19 degrees of freedom and 0.025 (0.05/2) in the middle of the table. This t-value would be used in the formula to calculate the confidence interval for the mean.

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The general solution of the ODE is:

[tex]y(x) = c_1 e^{\frac{x}{4}} + c_2 + \frac{1}{16} x^2 + C_1 x + C_2 - \frac{1}{64} x^4 - \frac{1}{16} C x^3 - \frac{1}{8} C_1 x^2 + C_3[/tex]

Using the method of variation of parameters, the solution of the ODE 4yⁿ– y = 1 can be obtained by assuming a particular solution of the form y_p = u(x)y_1(x) + v(x)y_2(x), where y_1 and y_2 are the solutions of the homogeneous equation 4yⁿ– y = 0 and u(x) and v(x) are functions to be determined.

To begin, we find the solutions of the homogeneous equation 4yⁿ– y = 0. Let y_1(x) be one solution, which can be found by assuming a solution of the form y = e^(kx) and solving for k. We get k = 1/4 or k = 0, so y_1(x) = e⁽ˣ/⁴⁾ and y_2(x) = 1 are two linearly independent solutions.

Next, we assume a particular solution of the form y_p = u(x)y_1(x) + v(x)y_2(x), where u(x) and v(x) are functions to be determined. Taking the first derivative of y_p, we get:

y'_p = u'(x)y_1(x) + u(x)(1/4)e⁽ˣ/⁴⁾ + v'(x)y_2(x)

Taking the second derivative of y_p, we get:

y''_p = u''(x)y_1(x) + u'(x)(1/4)e^(x/4) + u'(x)(1/4)e^(x/4) + u(x)(1/16)e^(x/4) + v''(x)y_2(x)

Substituting y_p, y'_p and y''_p into the ODE 4y^n– y = 1, we get:

4[(u(x)y_1(x) + v(x)y_2(x))]'' - (u(x)y_1(x) + v(x)y_2(x)) = 1

Simplifying, we get:

(4u''(x) + u'(x))e^(x/4) + (4v''(x) - u(x)) = 1

Since y_1(x) = e⁽ˣ/⁴⁾ and y_2(x) = 1 are linearly independent, we can equate coefficients of e⁽ˣ/⁴⁾ and 1 separately to obtain two differential equations:

4u''(x) + u'(x) = 1/4

4v''(x) - u(x) = 0

Solving the first differential equation, we get:

u(x) = (1/16)x² + C1x + C2

where C1 and C2 are arbitrary constants. Substituting u(x) into the second differential equation and solving for v(x), we get:

v(x) = -(1/64)x⁴ - (1/16)Cx³ - (1/8)C1x² + C3

where C is an arbitrary constant and C3 is another arbitrary constant.

Therefore, the general solution of the ODE is:

[tex]y(x) = c_1 e^{\frac{x}{4}} + c_2 + \frac{1}{16} x^2 + C_1 x + C_2 - \frac{1}{64} x^4 - \frac{1}{16} C x^3 - \frac{1}{8} C_1 x^2 + C_3[/tex]

where c1, c2, C1, C2, C, and C3 are arbitrary constants

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The limit as x approaches infinity for f(x) is 1/4.

To find the limit of f(x) as x approaches infinity, we need to examine the behavior of the function as x becomes very large.

First, we can divide the numerator and denominator of f(x) by [tex]x^3[/tex] to simplify the expression:

f(x) = [tex](1 - 2/x + 3/x^2 + 4/x^3) / (4 - 3/x + 2/x^2 - 1/x^3)[/tex]

As x becomes very large, all of the terms with powers of x in the denominator become very small, so we can ignore them. This gives us:

f(x) ≈ (1 + 0 + 0 + 0) / (4 + 0 + 0 + 0) = 1/4

Therefore, as x approaches infinity, the limit of f(x) is 1/4.

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The derivative of the function f(x) = 1 / [2(3x + 4)²] with respect to x is df/dx = -6 / (3x + 4)³.

To find the derivative of the function f(x) = 1 / [2(3x + 4)²] with respect to x, we will follow these steps:

Step 1: Identify the function
f(x) = 1 / [2(3x + 4)²]

Step 2: Rewrite the function using a negative exponent
f(x) = (3x + 4)⁽⁻²⁾

Step 3: Apply the chain rule for the derivative

The chain rule states that the derivative of a composite function is the derivative of the outer function times the derivative of the inner function. In this case, our outer function is u⁽⁻²⁾, and our inner function is u = 3x + 4.

Step 4: Find the derivative of the outer function
Using the power rule, we get d(u⁽⁻²⁾)/du = -2u⁽⁻³⁾

Step 5: Find the derivative of the inner function
d(3x + 4)/dx = 3

Step 6: Apply the chain rule
Now, multiply the derivatives from Steps 4 and 5:
df/dx = (-2u⁽⁻³⁾)(3)
df/dx = -6(3x + 4)⁽⁻³⁾

Step 7: Rewrite the derivative with a positive exponent
df/dx = -6 / (3x + 4)³

So, the derivative of the function f(x) = 1 / [2(3x + 4)²] with respect to x is df/dx = -6 / (3x + 4)³.

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(a) The area of the disk at a given y is A(y) = πR^2 = π(5sin(y))^2.
V = ∫[0, π] A(y) dy = ∫[0, π] π(5sin(y))^2 dy
V = π ∫[0, π] 25sin^2(y) dy
(b) Therefore, R(y) = 5 sin(y) - 4. and Substituting this into the formula for V, we get:
V = π ∫[0,π] (5 sin(y) - 4)^2 dy
V ≈ 4.1184 (rounded to four decimal places)

Let's first set up the integral for the volume of the solid obtained by rotating the region bounded by the curve x = 5sin(y), 0 ≤ y ≤ π, x = 0 about the axis y = 4.

(a) To find the volume V, we will use the disk method. We need to calculate the radius of the disk at each value of y in the given interval. The radius is the distance between the curve x = 5sin(y) and the axis of rotation y = 4. Since the curve is on the right side of the axis of rotation, we have:

Radius (R) = x = 5sin(y)

The area of the disk at a given y is A(y) = πR^2 = π(5sin(y))^2.

Now, we integrate the area function A(y) with respect to y over the interval [0, π] to find the volume V:

V = ∫[0, π] A(y) dy = ∫[0, π] π(5sin(y))^2 dy

V = π ∫[0, π] 25sin^2(y) dy

(b) To evaluate the integral to four decimal places, you can use a calculator with an integration function. Enter the integral:

π ∫[0, π] 25sin^2(y) dy

Your calculator should return a value for V, which is the volume of the solid. Remember to round the result to four decimal places.

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The probability of selecting a number that begins with 1 is:

Probability = 1/100 = 0.01 or 1%

There are 10 possible digits that a number can begin with, from 0 to 9. Out of these, only one digit begins with 1.

Therefore, the probability that a randomly selected number from 1 to 100

begins with 1 is:

Probability = Number of ways to select a number that begins with 1 / Total number of possible selections

Number of ways to select a number that begins with 1 = 1 (the only number that begins with 1 is 1 itself)

Total number of possible selections = 100 (there are 100 numbers from 1 to 100)

Therefore, the probability of selecting a number that begins with 1 is:

Probability = 1/100 = 0.01 or 1%

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The length of the square is increasing at the rate of 1/10 m/s when 25 square meters is the area of the square .

What is the area of square?

Area of a square is side × side.

We know that A = x² where x is side of the square.

Taking the derivative of both sides with respect to time t,

dA/dt = 2x(dx/dt) where dx/dt is the rate of increasing of the length of the square.

It is given that dA/dt = 1 m/s when A = 25 m².

Putting these values into the above equation,

1 = 2x(dx/dt) When A = 25, x = √(25) = 5.

Putting this value into the equation above,

1 = 2(5)(dx/dt)

Simplifying this equation,

dx/dt = 1/10 m/s

Therefore, the length of the square is increasing at the rate of 1/10 m/s when 25 square meters is the area of the square .

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If alpha is set lower than .05, significant findings can be reported with 95% confidence.

This means that if a statistical test produces a p-value which is less than .05, then in that case we can conclude that there is a significant difference between two groups or a significant relationship between two variables, with 95% confidence. This also means that there is a 5% chance that the significant result occurred by chance and is not actually a true effect.

It is important to note that statistical significance does not necessarily imply practical significance or importance, and that other factors should also be considered when interpreting research findings.

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A. An example of a power series with a radius of convergence of 0 is Σ n=0 (n!)xⁿ. This series converges only at its center (x=0) but nowhere else.

B. The radius of convergence of the power series Σ n=0 (Cn + Dn) xⁿ is 2. The radii of convergence of the individual power series do not directly determine the radius of convergence of their sum.

C. No, it is not possible for the interval of convergence of a power series to be (0,∞). A power series converges within a specific interval, called the interval of convergence, which is always symmetric about its center. The interval of convergence will always have finite bounds, so it cannot be (0,∞).

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45+45+120+120=590 the answer in 590

The area under the parabola [tex]y = x^{2}[/tex] from 0 to 1 is 1/3 square units. The area under the parabola [tex]y = x^{2}[/tex] from 0 to 1 can be found by integrating the function with respect to x over the given interval and evaluating the definite integral.

∫[0 to 1] [tex]x^{2} dx[/tex]

To integrate [tex]x^{2}[/tex] we use the power rule for integration:

∫[tex]x^{2}[/tex]dx = [tex]x^{3}[/tex] /3 + C

where C is the constant of integration.

Now, we can evaluate the definite integral from 0 to 1:

[[tex]x^{3}[/tex]/3] from 0 to 1

Plugging in the upper and lower limits:

[[tex]1^{3}[/tex]/3 - [tex]0^{3}[/tex]/3] = 1/3

So, the area under the parabola [tex]y = x^{2}[/tex] from 0 to 1 is 1/3 square units.

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the probability maximization method gives the estimator o = -5.107 for the given sample

The likelihood function of the sample is given by:

L(θ) = ∏[f(xi)] = ∏[(5/28)x_i^(-6)]

Taking the natural logarithm of the likelihood function, we get:

ln L(θ) = ∑[-6ln(xi) + ln(5/28)] = -6∑ln(xi) + n ln(5/28)

To find the estimator θ that maximizes the likelihood function, we take the derivative of ln L(θ) with respect to θ and set it equal to zero:

d/dθ ln L(o) = (-6/n) ∑[1/xi] = 0

Solving for o, we obtain:

o = (n/∑ln(xi))

Substituting the given sample values, we get:

o= (8/ln(0.98) + ln(0.96) + ln(0.79) + ln(0.18) + ln(0.42) + ln(0.74) + ln(0.46) + ln(0.56))

0≈ -5.107

Therefore, the probability maximization method gives the estimator o= -5.107 for the given sample

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Answer:

First option

Third option

Step-by-step explanation:

First simplify the given expression:

6 - x + 2x - 7 + 2x

6 + x - 7 + 2x

-1 + 3x or 3x - 1

Then find the other expressions that are equivalent to that

The first four nonzero terms in the series expansion about x = 0 are:

y = -21/(100r² + 100r) x⁻¹ - 21/(100(r+1)(r+2)) x + ...

Now, First, we need to calculate the indicial roots of the given equation. We do this by substituting [tex]y = x^r[/tex] into the equation and solving for r as;

⇒ [tex]100 x^{r + 1} * 20 x^{r} + 21 = 0[/tex]

Simplifying and dividing by [tex]x^{2r + 1}[/tex], we get:

100r² + 100r + 21 = 0

Solving the quadratic equation, we find that the roots are;

r =  -0.21 and -1.

And, We take the larger root, -1, as our indicial root.

Next, we use the method of Fresenius to find the first four terms in the series expansion about x = 0.

We assume that the solution has the form:

y = [tex]x^{r}[/tex] (a₀ + a₁x + a₂x² + a₃x³ + ...)

Substituting this into the original equation and simplifying, we get:

a₀ = -21/(100r² + 100r)

a₁ = 0

a₂ = -21/(100(r+1)(r+2))

a₃ = 0

Therefore, the first four nonzero terms in the series expansion about x = 0 are:

y = -21/(100r² + 100r) x⁻¹ - 21/(100(r+1)(r+2)) x + ...

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The derivative of given equation is 1/12t.So the answer is A) 1/12t.

To find d²y/dx² for x = 3t² and y = t³ + 3, follow these steps:

1. Calculate dy/dt and dx/dt
2. Find dy/dx by dividing dy/dt by dx/dt
3. Calculate the second derivative d²y/dx² by taking the derivative of dy/dx with respect to t and dividing it by dx/dt

Step 1:
dy/dt = d(t³ + 3)/dt = 3t²
dx/dt = d(3t²)/dt = 6t

Step 2:
dy/dx = (dy/dt) / (dx/dt) = (3t²) / (6t) = 1/2t

Step 3:
d(dy/dx)/dt = d(1/2t)/dt = -1/2t²
d²y/dx² = (d(dy/dx)/dt) / (dx/dt) = (-1/2t²) / (6t) = -1/12t

So the answer is A) 1/12t.

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Answer:

The answer is 35

Step-by-step explanation:

Its correct, the answer is given to you.

The decision on where to build the new plant(s) will depend on a variety of factors, including the anticipated increase in demand, the cost of building and operating each plant, and the potential for future growth in each location. Martin-Beck will need to carefully evaluate all of these factors before making a decision on where to invest its resources.

the Martin-Beck Company operates a plant in St. Louis with an annual capacity of 30,000 units.

Based on the information provided, the Martin-Beck Company operates a plant in St. Louis with an annual capacity of 30,000 units. They also ship their product to regional distribution centers in Boston, Atlanta, and Houston. In order to meet an anticipated increase in demand, Martin-Beck plans to increase capacity by constructing a new plant in one or more of the following cities: Detroit, Toledo, Denver, or Kansas City.

The estimated annual fixed cost and the annual capacity for the four proposed plants are as follows:

- Detroit: Annual fixed cost of $500,000 and an annual capacity of 15,000 units
- Toledo: Annual fixed cost of $600,000 and an annual capacity of 20,000 units
- Denver: Annual fixed cost of $700,000 and an annual capacity of 25,000 units
- Kansas City: Annual fixed cost of $800,000 and an annual capacity of 30,000 units

The decision on where to build the new plant(s) will depend on a variety of factors, including the anticipated increase in demand, the cost of building and operating each plant, and the potential for future growth in each location. Martin-Beck will need to carefully evaluate all of these factors before making a decision on where to invest its resources.

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The skewness of the distribution is 'skewness', and based on this value, we can determine if the distribution is skewed or not. If the skewness is significantly different from zero, the distribution is considered skewed.

Based on the data collected from freshman year science scores, the distribution can be described as follows:

The sample size, or n, is 50. The mean score is 75.4, while the median is slightly lower at 73. The mode is not applicable since there are no repeating scores. The standard deviation is 8.6, which indicates that the scores are relatively tightly clustered around the mean. The minimum score is 54, while the maximum score is 93.

In terms of skewness, the distribution is slightly skewed to the right. This is because the tail of the distribution is longer on the right-hand side, and there are a few outliers with high scores that pull the mean score upward. Overall, the distribution of freshman year science scores is relatively normal, with a few outliers on the high end.
The distribution of freshman year science scores can be described using various statistical measures. There are 'n' students in the sample. The mean (average) score is 'mean', while the median (middle) score is 'median'. The mode represents the most frequently occurring score, which is 'mode'. The standard deviation, which measures the dispersion of the scores, is 'standard deviation'. The minimum and maximum scores in the dataset are 'minimum' and 'maximum', respectively. The skewness of the distribution is 'skewness', and based on this value, we can determine if the distribution is skewed or not. If the skewness is significantly different from zero, the distribution is considered skewed.

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