Elgar recorded the total amount of money he had saved at the end of each month.

The probability that the įth cup you are given has really salty water is 1/100.

We are given that;

Number of cups = 100

Now,

The probability of an event is the ratio of the number of favorable outcomes to the total number of possible outcomes1. In this case, the event is that the įth cup has really salty water, and there is only one favorable outcome out of 100 possible outcomes. Therefore, the probability is:

P(įth cup has really salty water) = 1/100

This probability is the same for any value of į from 1 to 100.

b. we need to find the expected value of the number of cups you drink before you encounter the really salty water. The expected value is the weighted average of all possible outcomes, where the weights are the probabilities of each outcome2. In this case, the possible outcomes are that you drink 1 cup, 2 cups, …, or 100 cups before you stop. The probability of each outcome depends on where the really salty water is located among the 100 cups.

Therefore, by probability the answer will be 1/100.

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The value of the line integral is (1/3) ([tex]e^{27}[/tex] - 1).

To evaluate the line integral, we need to parameterize the given curve C.

Since C is the arc of the curve x = [tex]e^{y}[/tex], we can parameterize C as:

x = [tex]e^{t}[/tex]

y = t

where t ranges from 0 to 9.

Then, we can express dx and dy in terms of dt:

dx =  [tex]e^{t}[/tex]dt

dy = dt

Substituting these into the integrand, we get:

[tex]x e^{y} dx = (e^{t} )(e^{t} ) e^{t} dt[/tex]=  [tex]e^{(3t)}[/tex]  dt

Thus, the line integral becomes:

∫C x[tex]e^{y}[/tex] dx = ∫[tex]0^{9}[/tex]  [tex]e^{(3t)}[/tex]  dt

Evaluating the integral, we get:

∫[tex]0^{9}[/tex]  [tex]e^{(3t)}[/tex]   dt = (1/3) [tex]e^{(3t)}[/tex] | from 0 to 9

= (1/3) ([tex]e^{27}[/tex]  - 1)

Therefore, the value of the line integral is (1/3) ([tex]e^{27}[/tex] - 1).

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Initial value problem constants are k = 3 and y0 = 8.

We need to follow these steps:

Step 1: Differentiate y(t) with respect to t.
Given y(t) = 8[tex]e^{-3t[/tex], let's find its derivative y'(t):

y'(t) = d(8[tex]e^{-3t[/tex])/dt = -24[tex]e^{-3t[/tex]

Step 2: Plug y(t) and y'(t) into the differential equation.
The differential equation is y' + ky = 0. Substitute y(t) and y'(t):

-24[tex]e^{-3t[/tex] + k(8[tex]e^{-3t[/tex]) = 0

Step 3: Solve for k.
Factor out [tex]e^{-3t[/tex]:

[tex]e^{-3t[/tex](-24 + 8k) = 0

Since [tex]e^{-3t[/tex] is never equal to 0, we can divide both sides by e^(-3t):

-24 + 8k = 0

Now, solve for k:

8k = 24
k = 3

Step 4: Find y0 using y(0).
y0 is the value of y(t) when t = 0:

y0 = 8[tex]e^{-3 * 0[/tex] = 8[tex]e^0[/tex] = 8

So, the constants are k = 3 and y0 = 8.

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Using a 95% confidence level, the critical value for a two-tailed test is 1.96.

A confidence interval is a group of values obtained from a statistical study of a set of data that, with a particular level of certainty, contains an unknown population parameter.

To construct a confidence interval for the difference in mean time spent reading for people ages 15 to 19 and people ages 75 and over, we can use the following formula:

CI = (X₁ - X₂) ± tα/2 * SE

where X₁ and X₂ are the sample means, tα/2 is the critical value from the t-distribution with degrees of freedom equal to the smaller sample size minus one, and The standard error of the mean difference is abbreviated as SE.

Let's first determine the ballpark estimate of the difference in means:

X₁ - X₂ = 7.8 - 43.8 = -36

Accordingly, those aged 75 and older read for 36 minutes longer each day than those between the ages of 15 and 19.

The standard error of the difference in means will now be determined:

SE = √(s₁²/n₁ + s₂²/n₂)

where the sample sizes are n1 and n2, and the standard deviations are s1 and s2, respectively.

SE = √((5.4²/975) + (35.5²/1050)) = 1.86

We must establish the degrees of freedom before we can identify the crucial value. Since the sample sizes are greater than 30, we can use the z-distribution instead of the t-distribution. The degrees of freedom are approximately equal to the smaller sample size minus one, which is 975 - 1 = 974.

Using a 95% confidence level, the critical value for a two-tailed test is 1.96.

Finally, we can construct the confidence interval:

CI = (-36) ± (1.96 * 1.86) = (-38.63, -33.37)

According to this confidence interval, we can say with 95% certainty that there is a difference between 38.63 and 33.37 minutes in the average amount of time per day that those aged 15 to 19 and those aged 75 and older spend reading. We can infer that there is a sizable variation in the mean daily reading time between the two age groups as the interval does not contain zero.

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Answer: y= -2x^2 + 24x - 75

y = -2(x-6)^2 - 3

y = -2 * (x-6)(x-6) -3

y = -2 * (x*x - x*6 - 6*x -6 * -6) - 3

y = -2 (x^2 - 12x + 36) - 3

y = -2x^2 + 24x - 72 - 3

y= -2x^2 + 24x - 75

Step-by-step explanation:

Answer:

y=-2x²+24x-75

Step-by-step explanation:

y=-2(x-6) ²-3

y=-2(x²+6²-12x) -3

y=-2x²-72+24x-3

y=-2x²+24x-75

Since the real parts of both eigenvalues are non-negative, it can be concluded that the origin is a center for the given planar system.

To show that the origin is a center for the given planar system, we will examine the system's stability around the origin (0,0). The system is given by:

dx/dt = 2x + 8y

First, we need to rewrite the system in matrix form. Let X be the column vector [x, y]^T, and A be the matrix of coefficients:

X' = AX

where X' = [dx/dt, dy/dt]^T and A = [[2, 8], [0, 0]].

Now, we find the eigenvalues of matrix A, which will determine the stability of the system around the origin. The characteristic equation of A is given by:

det(A - λI) = 0

where λ is an eigenvalue, and I is the identity matrix. The equation becomes:

(2 - λ)(0 - λ) - (8 * 0) = 0

Solving for λ, we find that the eigenvalues are:

λ1 = 2, λ2 = 0

Since one eigenvalue is positive (λ1 = 2) and the other is zero (λ2 = 0), the origin is not a stable equilibrium point, nor is it a spiral. However, since the real parts of both eigenvalues are non-negative, it can be concluded that the origin is a center for the given planar system.

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To find the area under the standard normal curve to the right of z = 1.72.

To find the area under the standard normal curve, we use a z-table which gives the area to the left of a given z-score. Since we need to find the area to the right of z = 1.72, we'll first find the area to the left and then subtract it from 1.

Step 1: Look up the z-score of 1.72 in a z-table. You'll find that the area to the left of z = 1.72 is approximately 0.9573.

Step 2: Subtract the area to the left from 1: 1 - 0.9573 = 0.0427.

So, the area under the standard normal curve to the right of z = 1.72 is approximately 0.0427, rounded to four decimal places.

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The required answer is the number of left co-sets of h in g and the number of left co-sets of k in h.

To prove that [g: k] = [g: h] [h: k], we need to show that the number of left co-sets of k in g is equal to the product of the number of left co-sets of h in g and the number of left co-sets of k in h.

Let x be an element of g, and let S be the set of left co-sets of k in g. Then we can define a function f from S to the set of left co-sets of hk in g by f(gk) = gxhk. This function is well-defined because if gk = g'k, then g' = gkx for some x in k, and so gxhk = g'xhk.

Furthermore, this function is injective, because if gxhk = g'xhk, then g'^{-1}g is in hk, and so g'^{-1}g = hk for some h in h and k' in k. But then gk = g'k' and so gk = g'k.

Finally, this function is surjective, because if gx is in g, then gx = gxh(kh^{-1}) for some h in h and k' in k. Therefore, gx is in the image of f(gk') for some k' in k.

Therefore, f is a bijection, and so the number of left co-sets of k in g is equal to the number of left co-sets of hk in g, which is equal to [g: h][h: k].

To prove that [g: k] = [g: h] [h: k], we will use the concept of co-sets and the counting principle.

Step 1: Define the terms and notation.

Let g be a finite group, h be a subgroup of g, and k be a subgroup of h. The notation [g: k] denotes the index of k in g, which is the number of left co-sets of k in g. Similarly, [g: h] denotes the index of h in g, and [h: k] denotes the index of k in h.

Step 2: Count the number of cosets.

By the definition of index, we have:
[g: k] = the number of left co-sets of k in g
[g: h] = the number of left co-sets of h in g
[h: k] = the number of left co-sets of k in h

Step 3: Use the counting principle.

For each left co-set of h in g, there are [h: k] left co-sets of k in h. So, the total number of left co-sets of k in g is the product of the number of left co-sets of h in g and the number of left co-sets of k in h.

Step 4: State the conclusion.

By the counting principle, we conclude that [g: k] = [g: h] [h: k]. This proves the statement we set out to prove.

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(a) The mean burning rates of the two propellants are not equal. (b) p-value is less than 0.001. (c) β-error is 0.04 or 4%. (d) The 95% confidence interval is: -6.35 ± 2.024 * 3 * sqrt(1/20 + 1/20) = (-10.27, -2.43)

(a) Test statistic is:

t = (x1 - x2) / (s pooled * sqrt(1/n1 + 1/n2))

Standard deviation is:

s = sqrt(((n1-1)*s1^2 + (n2-1)*s2^2) / (n1+n2-2))

So,

t = (18.02 - 24.37) / (3 * sqrt(1/20 + 1/20)) = -3.422

Using a two-tailed test at α = 0.05, and calculated value of t (-3.422) is beyond the critical values of t are ±2.024, we reject the null hypothesis and conclude that the mean burning rates of the two propellants are not equal.

(b) Using a t-table or calculator, the p-value is less than 0.001.

(c) Using a t-table or calculator, the critical values of t for a two-tailed test at α = 0.05 and 38 degrees of freedom are ±2.024. The non-centrality parameter for the test is:

δ = (μ1 - μ2) / (σ pooled * sqrt(1/n1 + 1/n2)) = 2.5 / (3 * sqrt(1/20 + 1/20)) = 1.8257

The power of the test for this non-centrality parameter is 0.96. Therefore, the β-error is 1 - power = 0.04 or 4%.

(d) Confidence interval is given by:

(x1 - x2) ± tα/2,s_p * sqrt(1/n1 + 1/n2)

So,

s_p = sqrt[((20 - 1) * 3^2 + (20 - 1) * 3^2) / (20 + 20 - 2)] = 3

tα/2,s_p = t0.025,38 = 2.024

(x1 - x2) = 18.02 - 24.37 = -6.35

Therefore, the 95% confidence interval is:

-6.35 ± 2.024 * 3 * sqrt(1/20 + 1/20) = (-10.27, -2.43)

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The values of a and b such that y = ax + b is Gaussian distributed with mean 0 and variance 1 are a = 1/σ and b = -µ/σ or a = -1/σ and b = µ/σ.

Let's first find the mean and variance of y, where y = ax + b.

The mean of y is given by:

E[y] = E[ax + b] = aE[x] + b = aµ + b

Similarly, the variance of y is given by:

Var[y] = Var[ax + b] = a²Var[x] = a²σ²

Now, we want y to be Gaussian distributed with mean 0 and variance 1, i.e., y ~ N(0,1).

So, we have:

aµ + b = 0   and   a²σ² = 1

From the first equation, we can solve for b in terms of a and µ:

b = -aµ

Substituting this into the second equation, we get:

a²σ² = 1

Solving for a, we get:

a = ± 1/σ

So, we have two possible values for a: a = 1/σ or a = -1/σ.

Substituting these values for a and b = -aµ into the expression for y, we get:

y = (x - µ)/σ  or y = -(x - µ)/σ

Both of these expressions have a standard normal distribution (i.e., mean 0 and variance 1), so either one can be used as the solution to the problem.

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Answer: 11.05 units

Step-by-step explanation:

plug in the coordinates of U and N into the distance formula:

[tex]d = \sqrt{(x_2 - x_1)^2 + (y_2-y_1)^2}[/tex]

substitute:

[tex]\sqrt{(3-2)^2+(-5-6)^2}[/tex]

solve:

[tex]\sqrt{1^2+(-11)^2}[/tex]

= [tex]\sqrt{122}[/tex] or 11.05

a. The matrix B is [[1, 0], [0, 1]].

b. Since B is the identity matrix, when it is applied to the vector (a, b), it does not change the vector's direction or magnitude. Geometrically, this means that the transformation does not affect the position of the vector in the plane R2.

To find the matrix B = [[b11, b12], [b21, b22]] such that it transforms a complex number a+ib to its transpose, let's first express the complex number as a vector (a, b).

The transformation can be represented as:
B * (a, b)^T = (a, b)

Since we're looking for a matrix that does not change the vector, we can write it in the form:
[[b11, b12], [b21, b22]] * [(a), (b)] = [(a), (b)]

By performing matrix multiplication, we get:
b11 * a + b12 * b = a
b21 * a + b22 * b = b

From these equations, we can deduce that:
b11 = 1, b12 = 0
b21 = 0, b22 = 1

So, the matrix B is:
[[1, 0], [0, 1]]

Now, let's explain geometrically the action of matrix B on the vector (a, b). Since B is the identity matrix, when it is applied to the vector (a, b), it does not change the vector's direction or magnitude. Geometrically, this means that the transformation does not affect the position of the vector in the plane R2.

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For a given sample with n = 50, the values are -

a. 90% confidence interval for the population mean is  22.5 ± 1.92.

b. 95% confidence interval for the population mean is  22.5 ± 2.18.

c. 99% confidence interval for the population mean is  22.5 ± 2.88.

d. The margin of error and the width of the confidence interval increases, as the confidence level increases.

What is a sample?

A sample is characterised as a more manageable and compact version of a bigger group. A smaller population that possesses the traits of a bigger group. When the population size is too big to include all participants or observations in the test, a sample is utilised in statistical analysis.

a. To develop a 90% confidence interval for the population mean, we use the formula -

CI = X' ± zα/2 × (σ/√n)

where X' is the sample mean, σ is the population standard deviation (which we don't know, so we use the sample standard deviation as an estimate), n is the sample size, and zα/2 is the z-score corresponding to the desired confidence level. For a 90% confidence level, α = 0.1/2 = 0.05 and zα/2 = 1.645 (using a z-table or calculator).

Substituting the values given, we get -

CI = 22.5 ± 1.645 × (4.5/√50) ≈ 22.5 ± 1.92

So the 90% confidence interval for the population mean is (20.6, 24.4).

b. To develop a 95% confidence interval for the population mean, we use the same formula but with zα/2 = 1.96 (using a z-table or calculator).

Substituting the values given, we get -

CI = 22.5 ± 1.96 × (4.5/√50) ≈ 22.5 ± 2.18

So the 95% confidence interval for the population mean is (20.3, 24.7).

c. To develop a 99% confidence interval for the population mean, we use the same formula but with zα/2 = 2.576 (using a z-table or calculator).

Substituting the values given, we get -

CI = 22.5 ± 2.576 × (4.5/√50) ≈ 22.5 ± 2.88

So the 99% confidence interval for the population mean is (19.6, 25.4).

d. As the confidence level is increased, the margin of error and the width of the confidence interval also increase.

This is because higher confidence levels require more certainty in the estimate, which means including a wider range of values.

However, this also means that the confidence interval becomes less precise and may include a wider range of possible population means.

Therefore, the confidence interval values are obtained.

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The y-intercept identified from the graph of the function is (0, 1)

From the question, we have the following parameters that can be used in our computation:

y = 4^x

Next, we graph the function

The graph of the function is added as an attachment

To identify the y-intercept, we look for where x = 0

On the graph, we have

y = 1 when x = 0

Hence, the y-intercept of the graph is (0, 1)

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We can solve this problem using the following differential equation:

[tex]$\frac{d Q}{d t}=2(2)-3 \frac{Q}{V} $[/tex]

where Q is the amount of salt in the tank at time t, V is the volume of water in the tank at time t, and [tex]$2(2)$[/tex] is the rate of salt inflow, i.e., 2 gallons/min with a salt concentration of 2 pounds/gallon. The term[tex]$3(Q/V)$[/tex]represents the rate of salt outflow from the tank, with a rate of 3 gallons/min and a salt concentration of [tex]$Q/V$[/tex] pounds/gallon.

We know that the initial amount of salt is 15 pounds and the initial volume of water is 300 gallons, so [tex]$Q(0) = 15$[/tex] and[tex]$V(0) = 300$[/tex]. To solve the differential equation, we first find the volume of water as a function of time. We have:

[tex]$$\frac{d V}{d t}=2-3=-1$$[/tex]

which gives us [tex]$\$ V(t)=V(0)-t=300-t \$$[/tex]. Substituting this into the differential equation for[tex]$\$ Q \$$[/tex]  and simplifying, we obtain:

[tex]$$\frac{d Q}{d t}+\frac{3}{300-t} Q=4$$[/tex]

which is a first-order linear differential equation. The integrating factor is [tex]$\$ \mathrm{e}^{\wedge}\{\backslash$[/tex] int [tex]$\backslash f r a c\{3\}$[/tex] [tex]$\{300-t\} d t\}=e^{\wedge}\{-3 \backslash \ln (300-t)\}=(300-t)^{\wedge}\{-3\} \$$[/tex] . Multiplying both sides of the differential equation by this factor, we get:

[tex]$$(300-t)^{-3} \frac{d Q}{d t}+\frac{3}{(300-t)^4} Q=4(300-t)^{-3} .$$[/tex]

The left-hand side is the derivative of [tex]$\$(300-\mathrm{t})^{\wedge}\{-2\} Q \$$[/tex], so we can rewrite the equation as:

[tex]$$\frac{d}{d t}\left((300-t)^{-2} Q\right)=4(300-t)^{-3}$$[/tex]

Integrating both sides with respect to [tex]$\$ \mathrm{t} \$$[/tex], we get:

[tex]$$(300-t)^{-2} Q=-\frac{4}{2(300-t)^2}+C$$[/tex]

where [tex]$\$ C \$$[/tex] is a constant of integration. Solving for[tex]$\$ Q \$$[/tex], we obtain:

[tex]$$Q(t)=\frac{2}{(300-t)^2}-\frac{C}{(300-t)^2}$$[/tex]

Using the initial condition [tex]$\$ Q(0)=15 \$$[/tex], we can solve for  :

[tex]$$15=\frac{2}{(300-0)^2}-\frac{C}{(300-0)^2} \Rightarrow C=\frac{2}{9000}=\frac{1}{4500}$$[/tex]

Therefore, the amount of salt in the tank at time t is:

[tex]$Q(t)=\frac{2}{(300-t)^2}-\frac{1}{4500(300-t)^2}$[/tex]

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Okay, here are the steps to solve this problem:

(a) The support of an exponential distribution with parameter λ is (0, ∞). It does not depend on λ.

(b) The MLE for λ is the inverse of the sample mean:

î = n/∑Xi

(c) We will Taylor expand g(x) = 1/x around x = 1/λ0, where λ0 is the true value of λ.

g'(x) = -1/x2

g"(x) = 2/x3

Taylor expansion at x = 1/λ0:

g(x) ≈ g(1/λ0) + g'(1/λ0)(x - 1/λ0) + g"(1/λ0)(x - 1/λ0)2/2

1/x ≈ 1/λ0 - (1/λ02)(x - 1/λ0) + (2/λ03)(x - 1/λ0)2/2

(d) Plug in x = 1/î:

1/î ≈ 1/λ0 - (1/λ02)(1/î - 1/λ0) + (2/λ03)(1/î - 1/λ0)2/2

λ0î ≈ λ0 - (λ0)2(λ0 - î) + 2(λ0)3(λ0 - λ0)2/2

λ0î + (λ0)2(λ0 - î) - 2(λ0)2 ≈ 0

Solving for î - λ0 gives:

î - λ0 ≈ - (λ0)2/(2(n - λ0))

So (î - λ0) is asymptotically N(0, (λ0)2/(2(n)).

(e) The Fisher information is:

I(λ0) = E[-∂2/∂λ2 log L(X|λ) | λ = λ0]

= 2n/λ02

Since this is positive and does not depend on λ0, the MLE is asymptotically normal according to the results in class.

Does this look correct? Let me know if you have any other questions!

The consumption function is c = c0 mpc(yd), we have to define the c0 term.

The consumption function c = c0 mpc(yd) represents the relationship between the level of consumption and disposable income (yd), where c0 is the autonomous consumption or the consumption level when disposable income is zero, and mpc is the marginal propensity to consume, which indicates the increase in consumption for every additional unit of disposable income.

Therefore, the c0 term in the consumption function is usually defined as the intercept or the level of consumption that does not depend on changes in disposable income.

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The general solution to the differential equation is y(x) = c1e^(r1x) + c2e^(r2x) + ... + ck e^(rkx) + yp(x). The uniqueness of the solution is guaranteed by the condition that an(x) ≠ 0 on I.

The given differential equation is a linear nth order differential equation with constant coefficients. The general form of such an equation is:

anD^n y + an-1D^(n-1) y + ... + a1Dy + a0y = g(x)

where a0, a1, ..., an are constants.

To solve this equation, we first find the characteristic equation by assuming a solution of the form y = e^(rx) and substituting it into the differential equation:

an(r^n)e^(rx) + an-1(r^(n-1))e^(rx) + ... + a1re^(rx) + a0e^(rx) = g(x)e^(rx)

Dividing both sides by e^(rx) and simplifying gives:

an(r^n) + an-1(r^(n-1)) + ... + a1r + a0 = g(x)

This equation is called the characteristic equation of the differential equation.

The roots of the characteristic equation are called characteristic roots or eigenvalues. Let the roots be r1, r2, ..., rk. Then the general solution to the differential equation is given by:

y(x) = c1e^(r1x) + c2e^(r2x) + ... + ck e^(rkx) + yp(x)

where c1, c2, ..., ck are constants, and yp(x) is a particular solution to the non-homogeneous differential equation.

If the initial conditions are given as y(x0) = y0, y'(x0) = y1, ..., y^(n-1)(x0) = yn-1, then we can determine the values of the constants c1, c2, ..., ck by solving a system of linear equations formed by substituting the initial conditions into the general solution.

The uniqueness of the solution is guaranteed by the condition that an(x) ≠ 0 on I. This condition ensures that the differential equation is not singular, which means that the coefficients do not simultaneously vanish at any point in I. If the equation is singular, then the solution may not be unique.

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The distance covered by the point P(t) = (x(t), y(t)) between t=0 and t=14 is approximately 28.84 units.

To find the distance covered, first differentiate x(t) and y(t) with respect to t:
dx/dt = 2t - 8
dy/dt = 2t - 8

Then, find the magnitude of the derivative vector using the Pythagorean theorem:
||dP/dt|| = √((dx/dt)² + (dy/dt)²) = √((2t - 8)² + (2t - 8)²) = √(2(2t - 8)²)

Next, integrate the magnitude from t=0 to t=14:
∫(√(2(2t - 8)²)) dt from 0 to 14

Using substitution, let u = 2t - 8, so du = 2dt. The new integral becomes:
1/2 ∫(√(2u²)) du from -8 to 20

Evaluating this integral, you get 1/2 * (2/3) * (u³/²) from -8 to 20, which equals 1/3 * (20³/² - (-8)³/²) ≈ 28.84 units.

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We can see here that the solutions to the triangles are:

1. 62.2°.

2. 35.9°

3. 61.9°

4. 53.1°

We can see here that using trigonometric ratio formula, we find the values of x.

We see the following:

1. Cos x = 7/15 = 0.4666

x = [tex]cos^{-1}[/tex] 0.4666 = 62.2°.

2. Sin x = 27/46 = 0.5869

x° = [tex]sin^{-1}[/tex]  0.5869  = 35.9°

3. Sin x = 30/34 = 0.8823

x° = [tex]sin^{-1}[/tex] 0.8823 = 61.9°

4. Tan x = 8/6 = 1.3333

x° = 1.3333 = 53.1°

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Answer:

x≥2

Step-by-step explanation:

x≥2

The increments of time used to break up the timeline can vary depending on the specific timeline being presented.

The increments of time used to break up the timeline can vary depending on the context and purpose.

However, common time increments include years, months, weeks, or days, which are displayed on the x-axis (horizontal) to divide the timeline into easily understandable segments.

For example, in a historical timeline, the increments might be years, decades, or centuries. In a project timeline, the increments might be weeks or months.

The x-axis (horizontal) is typically where these increments are marked and can help to visualize the timeline more clearly.

Ultimately, the increments chosen should be appropriate for the task at hand and allow for effective planning and tracking of progress.

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Answer:

the lowest common multiple is 2

Answer:

£160:£560

[tex]2 + 7 = 9[/tex]

[tex] \frac{720}{9} = 80[/tex]

[tex]2 \times 80 = 160[/tex]

[tex]7 \times 80 = 560[/tex]

Sure, here is the Python program to count the number of even and odd numbers from a series of numbers (1, 2, 3, 4, 5, 6, 7, 8, 9) using a while loop:

```
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9]
even_count = 0
odd_count = 0
i = 0

while i < len(numbers):
   if numbers[i] % 2 == 0:
       even_count += 1
   else:
       odd_count += 1
   i += 1

print("Number of even numbers:", even_count)
print("Number of odd numbers:", odd_count)
```

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Probability P(1/2 < X ≤ 3/4) = 5/16.

To compute P(1/2 < X ≤ 3/4) for the given random variable X that takes values in [0, 1] and has the cumulative distribution function F(x) = x^2 for 0 ≤ x ≤ 1:

Follow these steps:

STEP 1: Calculate F(3/4) using the given function:
  F(3/4) = (3/4)^2 = 9/16

STEP 2: Calculate F(1/2):
  F(1/2) = (1/2)^2 = 1/4

STEP 3:Subtract F(1/2) from F(3/4) to find the probability P(1/2 < X ≤ 3/4):
  P(1/2 < X ≤ 3/4) = F(3/4) - F(1/2) = (9/16) - (1/4) = (9/16) - (4/16) = 5/16

Your answer: P(1/2 < X ≤ 3/4) = 5/16.

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Probability P(1/2 < X ≤ 3/4) = 5/16.

To compute P(1/2 < X ≤ 3/4) for the given random variable X that takes values in [0, 1] and has the cumulative distribution function F(x) = x^2 for 0 ≤ x ≤ 1:

Follow these steps:

STEP 1: Calculate F(3/4) using the given function:
  F(3/4) = (3/4)^2 = 9/16

STEP 2: Calculate F(1/2):
  F(1/2) = (1/2)^2 = 1/4

STEP 3:Subtract F(1/2) from F(3/4) to find the probability P(1/2 < X ≤ 3/4):
  P(1/2 < X ≤ 3/4) = F(3/4) - F(1/2) = (9/16) - (1/4) = (9/16) - (4/16) = 5/16

Your answer: P(1/2 < X ≤ 3/4) = 5/16.

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To factor 12q^2+34q-28, we need to find two numbers that multiply to 12*(-28)=-336 and add up to 34.

We can start by listing all the factors of -336:

1, -1, 2, -2, 3, -3, 4, -4, 6, -6, 7, -7, 8, -8, 12, -12, 14, -14, 16, -16, 21, -21, 24, -24, 28, -28, 42, -42, 48, -48, 56, -56, 84, -84, 112, -112, 168, -168, 336, -336

Now, we need to find two numbers from this list that add up to 34. We can see that 21 and 16 satisfy this condition since 21+16=37 and 21-16=5, which is not 34, but we can adjust this by using the coefficients of q. Specifically, we can use the fact that 34=21q+16q, and then we can write:

12q^2+34q-28 = 12q^2+21q+16q-28

Now, we can factor by grouping:

= (12q^2+21q) + (16q-28)

= 3q(4q+7) + 4(4q+7)

= (3q+4)(4q+7)

Therefore, the factorization of 12q^2+34q-28 is:

12q^2+34q-28 = (3q+4)(4q+7)

Answer:

Original price = $400 / (1 - 25/100)

= $400 / 0.75

= $533.33

Step-by-step explanation:

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