help please! need an answer asap................

Answer:

12

Step-by-step explanation:

Through 12 years  he would have painted 72 paintings and since he hasn't sold two of them he has only sold 70.

The equation that represents the sentence "28 is the quotient of a number and 4" is 28 = n/4.

In the given sentence, "28 is the quotient of a number and 4," we can break down the sentence into mathematical terms. The term "quotient" refers to the result of division, and "a number" can be represented by the variable "n." The divisor is 4.

1) Define the variable.

Let's assign the variable "n" to represent "a number."

2) Write the equation.

Since the sentence states that "28 is the quotient of a number and 4," we can write this as an equation: 28 = n/4.

The equation 28 = n/4 represents the fact that the number 28 is the result of dividing "a number" (n) by 4. The left side of the equation represents 28, and the right side represents "a number" divided by 4.

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x - 8 = 68

x - 8 + 8 = 68 + 8

x = 76

hope this helped

Use the rslash() function in R Studio to generate a random sample of size 100 from the Slash distribution.

To generate a random sample of size 100 from the Slash distribution without using extra packages in R Studio, you can use the inverse transform method. The Slash distribution is a continuous probability distribution with a density function given by f(x) = 1 / (π(1 + x^2)).

First, generate a random sample of size 100 from a uniform distribution on the interval [0, 1]. Then, transform the uniform random numbers using the inverse cumulative distribution function (CDF) of the Slash distribution, which is given by F^(-1)(x) = tan(π(x - 0.5)). This will map the uniform random numbers to the corresponding values from the Slash distribution.

In R Studio, you can use the following code to generate the random sample:

# Set seed for reproducibility

set.seed(42)

# Generate uniform random sample

uniform_sample <- runif(100)

# Transform uniform random sample to Slash distribution

slash_sample <- tan(pi * (uniform_sample - 0.5))

The slash_sample variable will contain the generated random sample of size 100 from the Slash distribution.

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The value of m∠C is 85°.

Given that, △ABC ∼ △EFG. Also, m∠A = 39° and m∠F = 56°. We need to find m∠C.

Let us first write down the formula for the similarity of triangles.  The two triangles are similar if their corresponding angles are congruent.

In other words, we can write: `∠A ≅ ∠E`, `∠B ≅ ∠F`, and `∠C ≅ ∠G`.

Now, in △ABC, we have: ∠A + ∠B + ∠C = 180° (Interior angle property of a triangle)

Also, in △EFG, we have: ∠E + ∠F + ∠G = 180°(Interior angle property of a triangle)

We know that ∠A ≅ ∠E and ∠B ≅ ∠F.

Substituting these values, we get:

39° + ∠B + ∠C = 180° (From △ABC)56° + ∠B + ∠G = 180° (From △EFG)

Simplifying, we get ∠B + ∠C = 141°...(Eq 1)

∠B + ∠G = 124°....  (Eq 2)

Now, let's subtract Eq 2 from Eq 1.

We get∠C − ∠G = 17°               

Substituting values from Eq 2:

∠C − 68° = 17° ∠C = 85°

Therefore, m∠C is 85°.

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Step-by-step explanation:

0.45 kilograms. you decide the mass value by 1000.

f(z) = z^2 + 1 has an antiderivative on the domain G = C \ {i, -i}.

(b) Hence, we cannot determine whether f(z) = z^2 + 1 has an antiderivative on the domain G = {z in C | Re(z) > 0} based on the Cauchy-Goursat theorem alone.

(a) To determine whether f(z) = z^2 + 1 has an antiderivative on the domain G = C \ {i, -i}, we can check if f(z) satisfies the Cauchy-Riemann equations on G.

The Cauchy-Riemann equations state that for a function f(z) = u(x, y) + iv(x, y) to have a derivative at a point, its real and imaginary parts must satisfy the partial derivative conditions:

∂u/∂x = ∂v/∂y and ∂u/∂y = -∂v/∂x.

For f(z) = z^2 + 1, we have u(x, y) = x^2 - y^2 + 1 and v(x, y) = 2xy.

Calculating the partial derivatives, we find:

∂u/∂x = 2x, ∂v/∂y = 2x,

∂u/∂y = -2y, ∂v/∂x = 2y.

Since ∂u/∂x = ∂v/∂y and ∂u/∂y = -∂v/∂x hold for all (x, y) in the domain G, f(z) satisfies the Cauchy-Riemann equations on G. Hence, f(z) has an antiderivative on G = C \ {i, -i}.

(b) Now, let's consider the domain G = {z in C | Re(z) > 0}. To determine if f(z) = z^2 + 1 has an antiderivative on G, we can utilize the Cauchy-Goursat theorem, which states that a function has an antiderivative on a simply connected domain if and only if its line integral around every closed curve in the domain is zero.

For f(z) = z^2 + 1, we can calculate its line integral over a closed curve C in G. However, since G is not simply connected (it has a "hole" at Re(z) = 0), the Cauchy-Goursat theorem does not apply, and we cannot conclude whether f(z) has an antiderivative on G based on this theorem.

To provide a definitive answer, further analysis or techniques such as the residue theorem may be required.

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Answer:

I don't know thank answer sorry I just really need points you can report me if you want but I REALLY need some

Answer:

polynomial of degree 10

Step-by-step explanation:

The degree of the polynomial is the sum of the exponents, that is

[tex]w^{7}[/tex]y³ → has degree 7 + 3 = 10

Answer:

सैम ने 9 पाउंड आटा बनाया

Step-by-step explanation:

Answer:

x-intercept = 2

y-intercept = 6

Step-by-step explanation:

x-intercept:  6x = 12;  x = 2

y-intercept:  2y = 12;  y = 6

Answer:

y=-3x+5.5

Step-by-step explanation:

The answer is Y=-3x+5.5 because we already know the slope which is -3. Now all you have to do is change the Y-Intercept. As you can tell the Y-Intercept is not a whole number. So you have to change it to a decimal to get the point that you want. You can put the last part of the equation as 5.5 or 5.55. It doesn't matter but 5.5 is more official. If you find any fault in my answer let me know. Thanks. Have a good day!

:] i honestly have no clue my self

The exact area of the surface obtained by rotating the curve y = √(1 + eˣ) about the x-axis over the interval 0 ≤ x ≤ 7, we would need to use numerical methods to approximate the value of the integral since it does not have a simple closed-form solution.

To find the exact area of the surface obtained by rotating the curve y = √(1 + eˣ) about the x-axis, we can use the formula for the surface area of a solid of revolution.

The formula for the surface area of a curve y = f(x) rotated about the x-axis over the interval [a, b] is given by:

A = 2π∫[a, b] y * sqrt(1 + (dy/dx)²) dx

In this case, the given curve is y = √(1 + eˣ) and the interval of interest is 0 ≤ x ≤ 7. To calculate the area, we need to find the derivative dy/dx and substitute it into the formula.

Let's start by finding the derivative of y = √(1 + eˣ) with respect to x. Applying the chain rule, we have:

dy/dx = (1/2)(1 + eˣ)^(-1/2) * eˣ

Now, we can substitute y and dy/dx into the surface area formula:

A = 2π∫[0, 7] √(1 + eˣ) * sqrt(1 + [(1/2)(1 + eˣ)^(-1/2) * eˣ]²) dx

Simplifying the expression inside the integral, we have:

A = 2π∫[0, 7] √(1 + eˣ) * sqrt(1 + (eˣ/2)(1 + eˣ)^(-1)) dx

Now, we need to evaluate this integral over the interval [0, 7] to find the exact area of the surface.

Unfortunately, the integral for this particular curve does not have a simple closed-form solution. Therefore, to find the exact area, we would need to rely on numerical methods, such as numerical integration techniques or computer algorithms, to approximate the value of the integral.

Using these numerical methods, we can calculate an accurate estimate of the surface area by dividing the interval [0, 7] into smaller subintervals and applying techniques like the trapezoidal rule or Simpson's rule. The more subintervals we use, the more accurate the approximation will be.

In summary, to find the exact area of the surface obtained by rotating the curve y = √(1 + eˣ) about the x-axis over the interval 0 ≤ x ≤ 7, we would need to use numerical methods to approximate the value of the integral since it does not have a simple closed-form solution.

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Answer: you know it’s 9mins but Im not sure how I should make the equation probably I think (53= 9T + 4)

Step-by-step explanation:

The equation of the asymptote for the given function f(x) = 0.7(4x-3) - 2 is y = 2.8x - 4.1.

The equation of an asymptote for a function can be determined by analyzing the behavior of the function as x approaches positive or negative infinity.

For the given function f(x) = 0.7(4x-3) - 2, let's simplify it:

f(x) = 2.8x - 2.1 - 2

f(x) = 2.8x - 4.1

As x approaches positive or negative infinity, the term 2.8x dominates the function. Therefore, the equation of the asymptote can be determined by considering the behavior of the linear term.

The coefficient of x is 2.8, so the slope of the asymptote is 2.8. The y-intercept of the asymptote can be found by setting x to 0 in the equation, resulting in -4.1. Therefore, the equation of the asymptote is y = 2.8x - 4.1.

In conclusion, the equation of the asymptote for the given function f(x) = 0.7(4x-3) - 2 is y = 2.8x - 4.1.

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Answer:

9√2

Step-by-step explanation:

To do this, we need to use the Pythagoras' Theorem. Which is a^2+b^2=c^2

In this case, we need to solve for C. So, we do 9^2 (A) +9^2 (B), assuming a and b are the same. So we end up with 81+81=c^2. Now, we find the square root of 162. Around 13 or 9√2

a) The cumulative distribution function for X is F(x) = 1 – exp(-x²)

is = 1 – exp(-x²)

b) P(X ≤ 2) = 0.865

c) Generate a uniformly distributed random number u between 0 and 1.

a) We have given a probability density function f(x) = 2x exp(-x²), x ≥ 0

To find the cumulative distribution function (CDF), we integrate the probability density function (PDF) from negative infinity to x as follows;

∫f(x)dx = ∫2x exp(-x²)dx

Using u =

-x², du/dx = -2x

dx = -du/2∫2x exp(-x²)dx

= -∫exp(u)du

= -exp(u) + C

= -exp(-x²) + C

We know that, F(x) = ∫f(x)dx.

From the above calculation, the CDF of X is given by;

F(x) = 1 – exp(-x²)

b)

We are to calculate P(X ≤ 2)

We know that F(2) = 1 – exp(-2²)

= 0.865

Therefore, P(X ≤ 2) = 0.865

c)

The inversion method is a way of generating random values of a random variable X using the inverse of the cumulative distribution function of X, denoted as F⁻¹(u),

where u is a uniformly distributed random number between 0 and 1.

The steps for generating a random value of X using the inversion method are:

Generate a uniformly distributed random number u between 0 and 1.

Find the inverse of the cumulative distribution function, F⁻¹(u).

This gives us the value of X.

d)

R command for one random value of X by using the inversion method```{r}

# setting seed to be 100 sets. seed(100)

# defining the inverse CDFF_inv = function(u) q norm(u, lower.tail=FALSE)

# generating a random value of Uu = run if(1)

# calculating the corresponding value of Xx = F_inv(u)```

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Answer:

B) 44

Step-by-step explanation:

70 - 25 + 9 - 2 × 10 ÷ 2

70 - 25 + 9 - 20 ÷ 2

70 - 25 + 9 - 10

45 + 9 - 10

54 - 10

44

Answer:220

Step-by-step explanation:

LET X BE THE LENGTH OF RECTANGLE AND FOR UPPER PORTION OF DIA GRAM BASE OF RIGHT ANGLE TRIANGLE SO X=20

LET Y BE WIDTH OF RECTANGLE SO Y=8

LET P BE THE PERPENDICULAR OF THE RIGHT TRIANGLE SO P=6

THEN

AREA OF RECTANGLE=LENGTH*WIDTH

SO AREA OF RECTANGLE BECOMES=(20)(8)=160

AND AREA OF RIGHT ANGLE TRIANGLE BECOMES=1/2(BASE*(PERPENDICULAR)

SO =1/2(20)(6)=60

SO THE TOTAL AREA OF THE DIAGRAM=AREA OF RIGHT ANGLE TRIANGLE+AREA OF RECTANGLE=160+60=220

To sketch the graph of a function that has a local maximum at 6 and is differentiable at 6, we can consider a function that approaches a maximum value at 6 and has a smooth, continuous curve around that point.

In the graph, we can depict a curve that gradually increases as we move towards x = 6 from the left side. At x = 6, the graph reaches a peak, representing the local maximum. From there, the curve starts to decrease as we move towards larger x-values.

The important aspect to note is that the function should be differentiable at x = 6, meaning the slope of the curve should exist at that point. This implies that there should be no sharp corners or vertical tangents at x = 6, indicating a smooth and continuous transition in the graph.

By incorporating these characteristics into the graph, we can represent a function with a local maximum at 6 and differentiability at that point.

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Answer:

Step-by-step explanation:

The margin of error E is approximately 0.31 oz

Margin of error is known to be a statistic expressing the amount of random sampling error in a survey's results. The margin of error informs you how close your survey findings are to the actual population's overall results. It is commonly represented by E.

The formula for margin of error is as follows:

z = critical value

σ = standard deviation

n = sample size

E = margin of error

The formula is, E = zσ/ √n

Here,

Sample size n = 51; Mean = 1.72; Standard deviation σ = 0.87 oz

Level of confidence = 97%

The level of confidence that corresponds to a z-score of 1.88 is 97% (using a standard normal table or calculator).

That is, z = 1.88 (by referring to a standard normal table or calculator)

To calculate the margin of error, we need to substitute the values in the formula

E = zσ/ √n

E = (1.88) (0.87) / √51

E = 0.3081 oz (approx)

Hence, the margin of error is approximately 0.31 oz (rounding the answer to two decimal places).

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Answer:

I am not sure rdbugs h h on grh g ih fv vy f byv7iplovh v v6c78

Answer:

[tex] m\angle SYX=76\degree [/tex]

Step-by-step explanation:

[tex] m\angle SYX = m\angle UYV[/tex]

(Vertical angles)

[tex] \because m\angle UYV=76\degree [/tex]

[tex] \therefore m\angle SYX=76\degree [/tex]

The solution to the Cauchy problem is x(t) = e^t and y(t) = te^t.

The Cauchy problem can be solved by finding the solution to the given system of differential equations.

In more detail, we have the following system of differential equations:

dx/dt = x - y

dy/dt = x + y

To solve this system, we can use the method of separation of variables. Starting with the first equation, we separate the variables:

dx/(x - y) = dt

Integrating both sides, we have:

ln|x - y| = t + C1

Exponentiating both sides, we get:

|x - y| = e^(t + C1)

Taking the absolute value, we have two cases:

(x - y) = e^(t + C1)

(x - y) = -e^(t + C1)

Simplifying, we obtain:

x - y = Ce^t, where C = e^(C1)

x - y = -Ce^t, where C = -e^(C1)

Next, we consider the second equation of the system. We differentiate both sides:

dy/dt = x + y

Substituting the expressions for x - y from the first equation, we have:

dy/dt = (Ce^t) + y

This is a linear first-order ordinary differential equation. We can solve it using an integrating factor. The integrating factor is e^t, so we multiply both sides by e^t:

e^t(dy/dt) - e^ty = Ce^t

We recognize the left side as the derivative of (ye^t) with respect to t:

d(ye^t)/dt = Ce^t

Integrating both sides, we have:

ye^t = Ce^t + C2

Simplifying, we obtain:

y = Ce^t + C2e^(-t), where C2 is the constant of integration

Using the initial conditions x(0) = 1 and y(0) = 0, we can find the values of the constants C and C2:

1 - 0 = C + C2

C = 1 - C2

Substituting this back into the equation for y, we have:

y = (1 - C2)e^t + C2e^(-t)

Therefore, the solution to the Cauchy problem is x(t) = e^t and y(t) = te^t.

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The average rate of change for the interval from x=9 to x=10 is 39366

The standard exponential equation is given as y = ab^x

From the values of the average change, you can see that it is increasing geometrically as shown;

2, 6, 18...

In order to , find the average rate of change for the interval from x=9 to x=10, we need to find the 10 term of the sequence using the nth term of the sequence;

Tn = ar^n-1

Given the following

a = 2

r = 3

n = 10

Substitute

T10 = 2(3)^10-1
T10 = 2(3)^9
T10 = 39366

Hence the  average rate of change for the interval from x=9 to x=10 is 39366

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Answer:

39,366

Step-by-step explanation:

its right

Answer:

100.00-0.22 is 99.78

U have to use decimal method. don't use Normal method

The correlation coefficient (p(Z, W)) between Z and W is sqrt(2) / 2.

To find the covariance of Z and W and the correlation coefficient (p(Z, W)), we can use the properties of covariance and correlation for independent random variables.

Given that X and Y are independent N(0, 2) random variables, we know that their means are zero and variances are 2 each.

Covariance:

Cov(Z, W) = Cov(7 + X + Y, 1 + Y)

Since X and Y are independent, the covariance between them is zero:

Cov(X, Y) = 0

Using the properties of covariance, we have:

Cov(Z, W) = Cov(7 + X + Y, 1 + Y)

= Cov(X, Y) + Cov(Y, Y)

= Cov(X, Y) + Var(Y)

Since Cov(X, Y) = 0 and Var(Y) = 2, we can substitute these values:

Cov(Z, W) = 0 + 2

= 2

Therefore, the covariance of Z and W is 2.

Correlation Coefficient:

p(Z, W) = Cov(Z, W) / (sqrt(Var(Z)) * sqrt(Var(W)))

To calculate p(Z, W), we need to find Var(Z) and Var(W):

Var(Z) = Var(7 + X + Y)

= Var(X) + Var(Y) (since X and Y are independent)

= 2 + 2 (since Var(X) = Var(Y) = 2)

= 4

Var(W) = Var(1 + Y)

= Var(Y) (since 1 is a constant and does not affect variance)

= 2

Now we can calculate p(Z, W):

p(Z, W) = Cov(Z, W) / (sqrt(Var(Z)) * sqrt(Var(W)))

= 2 / (sqrt(4) * sqrt(2))

= 2 / (2 * sqrt(2))

= 1 / sqrt(2)

= sqrt(2) / 2

Therefore, the correlation coefficient (p(Z, W)) between Z and W is sqrt(2) / 2.

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