what two numbers have a sum of “138” and a difference of “54”

All solutions lie on the line y = (4/3)x.

To find the solution set of the equation, we solve for y in terms of x as follows:

4x - 3y = 0

4x = 3y

y = (4/3)x

Therefore, the solution set of the equation is:

{(x, y) | y = (4/3)x}

This is a dependent equation, as it can be written in the form of y = mx, where m = 4/3, which is the slope of the line. In other words, all solutions lie on the line y = (4/3)x.

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Let A and P be square matrices,

D. det(PAP-1) = [(det P) (det A) (det P-1)]-1.

we can use the property of determinants that states det(AB) = det(A)det(B) for any matrices A and B.

We can rewrite PAP-1 as (P-1)-1APP-1, and then use the property of determinants to get:

det(PAP-1) = det((P-1)-1APP-1)
det(PAP-1) = det(P-1)-1det(A)det(P-1)

Since P is invertible, det(P) ≠ 0 and we can multiply both sides of this equation by det(P) to get:

det(P)det(PAP-1) = det(A)det(P-1)det(P)

Using the property of determinants again, we can simplify this equation to:

det(PAP-1) = det(A)det(P-1)

Finally, we can substitute det(P-1) = 1/det(P) into this equation to get:

det(PAP-1) = det(A)(1/det(P))
det(PAP-1) = (det(A)/det(P))

Therefore, the correct answer is D. det(PAP-1) = [(det P) (det A) (det P-1)]-1.

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The local maximum and minimum values and saddle point(s) of the function f(x, y) = 2x^3 + xy^2 + 5x^2 + y^2 +9 are

a) Local minimum: (0, 0)

b) Local minimum: (-5/3, 0)

c) Local maximum and saddle point: (-1, -1)

To find the local maximum and minimum values and saddle point(s) of the function f(x, y) = 2x^3 + xy^2 + 5x^2 + y^2 +9, we need to find the critical points, which are the points where the gradient of the function is zero or undefined.

First, we find the partial derivatives of f(x, y) with respect to x and y

∂f/∂x = 6x^2 + 2y + 10x

∂f/∂y = 2xy + 2y

Setting both partial derivatives to zero, we get

6x^2 + 2y + 10x = 0

2xy + 2y = 0

Simplifying the second equation, we get:

y(2x + 2) = 0

Therefore, either y = 0 or 2x + 2 = 0.

Case 1: y = 0

Substituting y = 0 into the first equation, we get:

6x^2 + 10x = 0

Solving for x, we get:

x(6x + 10) = 0

Therefore, either x = 0 or x = -5/3.

Case 2: 2x + 2 = 0

Solving for x, we get:

x = -1

Now we have three critical points: (0, 0), (-5/3, 0), and (-1, -1).

To determine the nature of these critical points, we need to compute the second partial derivatives of f(x, y):

∂^2f/∂x^2 = 12x + 10

∂^2f/∂y^2 = 2x + 2

∂^2f/∂x∂y = 2y

Evaluating these at each critical point, we get

(0, 0):

∂^2f/∂x^2 = 10 > 0 (minimum)

∂^2f/∂y^2 = 2 > 0 (minimum)

∂^2f/∂x∂y = 0

(-5/3, 0):

∂^2f/∂x^2 = -2/3 < 0 (maximum)

∂^2f/∂y^2 = -2 < 0 (maximum)

∂^2f/∂x∂y = 0

(-1, -1):

∂^2f/∂x^2 = -2 < 0 (maximum)

∂^2f/∂y^2 = 0

∂^2f/∂x∂y = -2 < 0 (saddle point)

Therefore, the critical points (0, 0) and (-5/3, 0) are both local minima, while the critical point (-1, -1) is a local maximum and saddle point.

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We can see that the expression (p q) (q → r p) (p r) is true only for the first combination of truth values (p=T, q=T, r=T).

To use the truth table method, we need to list all possible combinations of truth values for p, q, and r and then evaluate the expression (p q) (q → r p) (p r) for each combination.

If we find at least one combination that makes the expression true, then the expression is satisfiable; otherwise, it is unsatisfiable.

Let's start by listing all possible combinations of truth values for p, q, and r:

p | q | r

--+---+--

T | T | T

T | T | F

T | F | T

T | F | F

F | T | T

F | T | F

F | F | T

F | F | F

Next, we evaluate the expression (p q) (q → r p) (p r) for each combination of truth values:

p | q | r | (p q) (q → r p) (p r)

--+---+---+-----------------------

T | T | T |       T

T | T | F |       F

T | F | T |       F

T | F | F |       F

F | T | T |       F

F | T | F |       F

F | F | T |       F

F | F | F |       F

We can see that the expression (p q) (q → r p) (p r) is true only for the first combination of truth values (p=T, q=T, r=T). Therefore, the expression is satisfiable.

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To find a basis for the set of vectors in R² on the line y = -3x. we'll follow these steps:

Step 1: Write the equation in parametric form.

The given equation is y = -3x.

We can rewrite this equation in parametric form as follows: x = t y = -3t

Step 2: Identify a vector that lies on the line.

Now that we have the parametric form, we can use it to find a vector that lies on the line.

A general vector on the line can be represented as: v(t) = (t, -3t)

Step 3: Form the basis using the vector.

To find the basis for the set of vectors in R² on the line, we can choose a non-zero value for the parameter 't'.

Let's choose t = 1: v(1) = (1, -3)

The basis for the set of vectors in R² on the line y = -3x is { (1, -3) }.

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(a) The least-squares equation for the given data pairs (2,4), (3,3), and (5,6) is ŷ = 1.143x + 0.857.

(b) The least-squares equation for the given data pairs (4,2), (3,3), and (6,5) is ŷ = 0.714x + 1.143.

(d) Solving the equation from part (a) for x gives x = 0.875y - 0.750

(a) To find the least-squares equation for the given data pairs, we first need to calculate the slope (m) and y-intercept (b) of the line that best fits the data. The slope is given by the formula:

m = (NΣ(xy) - ΣxΣy) / (NΣ(x^2) - (Σx)^2)

where N is the number of data points (in this case, 3). Plugging in the values from the data pairs, we get:

m = ((338) - (1013)) / ((3*38) - (10^2)) = 0.857

Next, we can use the point-slope formula to find the equation of the line:

y - y1 = m(x - x1)

Choosing the point (3,3) as our reference point, we get:

y - 3 = 0.857(x - 3)

Simplifying this equation, we get:

y = 1.143x + 0.857

which is the least-squares equation for the given data pairs.

(b) Following the same procedure as in part (a), we get:

m = ((314) - (134)) / ((3*29) - (10^2)) = 0.714

Choosing the point (3,3) again as our reference point, we get:

y - 3 = 0.714(x - 3)

Simplifying this equation, we get:

y = 0.714x + 1.143

which is the least-squares equation for the given data pairs

(d) Solving the equation from part (a) for x, we get:

y = 1.143x + 0.857

y - 0.857 = 1.143x

x = (y - 0.857) / 1.143

Simplifying this expression, we get

x = 0.875y - 0.750

which is the answer to part (d) of the question.

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By proof of contradiction, the sum of a rational number and an irrational number is always irrational.

Suppose x is rational and y is irrational. By definition of rational number, there must be integers p and q, q ≠ 0, such that x = p/q.

Now assume that x + y is rational. Therefore, y = (x+y) - x is also rational.

Suppose x and y are both irrational and their sum is rational. Therefore, x+y must be irrational.

Further suppose, to get a contradiction, that x+y is rational. Likewise, there must be integers a and b, b ≠ 0, such that x+y= a/b.

We now simplify: y = (a/b) - (p/q) = (aq-pb)/bq. Therefore, we have concluded that y is rational, a contradiction.

Therefore, the assumption that x+y is rational must be false. Hence, x+y is irrational.

Now, assume that x+y is rational. Then, y = (x+y) - x is rational, which is a contradiction to the assumption that y is irrational.


Suppose x is rational and y is irrational. Further suppose, to get a contradiction, that x+y is rational. By definition of rational number, there must be integers p and q, q ≠ 0, such that x = p/q. Likewise, there must be integers a and b, b ≠ 0, such that x+y = a/b.

By substitution, we find (p/q) + y = a/b, and therefore y = (a/b) - (p/q). We now simplify: y = (aq + (-p)b) / bq. This is again a quotient of two integers with a nonzero denominator, therefore rational.

Therefore, we have concluded that y is rational, a contradiction. Therefore, the sum of a rational number and an irrational number must be irrational.

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Using the given data and a significance level of 0.01, the p-value for the test of the hypothesis is approximately 0.0151.

To calculate the p-value using Excel, we can first find the test statistic, which follows a t-distribution with degrees of freedom calculated using the formula:

df = (s1^2/n1 + s2^2/n2)^2 / [ (s1^2/n1)^2 / (n1-1) + (s2^2/n2)^2 / (n2-1) ]

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

Using the given values, we find that the degrees of freedom are approximately 86.9. Next, we can calculate the test statistic using the formula:

t = (x1-bar - x2-bar) / sqrt(s1^2/n1 + s2^2/n2)

which gives us a value of approximately -1.906. Finally, we can find the p-value using the Excel function T.DIST.RT, which calculates the right-tailed probability of a t-distribution. The formula for the p-value is:

p-value = T.DIST.RT(t, df)

Using Excel, we can enter the formula =T.DIST.RT(-1.906, 86.9) to find that the p-value is approximately 0.0151.

In conclusion, based on the given data and a significance level of 0.01, we can reject the null hypothesis and conclude that there is evidence to support the alternative hypothesis that the true population mean of the first sample is less than the true population means of the second sample. The p-value of 0.0151 indicates that this conclusion is unlikely to be due to random chance alone.

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The equation is  h = [tex]\frac{2A}{a+c}[/tex].

What is equation?

The definition of an equation in algebra is a mathematical statement that demonstrates the equality of two mathematical expressions. For instance, the equation 3x + 5 = 14 consists of the two equations 3x + 5 and 14, which are separated by the 'equal' sign.

Here the given equation is ,

=> A = [tex]\frac{1}{2}[/tex](a+c)h

Now simplifying the equation then,

=> 2A = (a+c)h

=> h = [tex]\frac{2A}{a+c}[/tex]

Hence the equation is  h = [tex]\frac{2A}{a+c}[/tex].

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Answer:

it's b hope this helps please mark me

$10,000 invested at an annual interest rate of 3% compounded monthly yields a balance of $11,152.92 after 6 years.

Let the annual interest rate compounded monthly be denoted by m. We know that the effective annual interest rate for monthly compounding is given by:

(1 + r/m)^12 - 1 = 0.01r

We can solve for m as follows:

(1 + r/m)^12 = 1 + 0.01r

1 + r/m = (1 + 0.01r)^(1/12)

r/m = 12[(1 + 0.01r)^(1/12) - 1]

Now, we are told that when $10,000 is invested at an annual interest rate of r% compounded continuously for t years, the balance at the end of t years is given by:

f(t,r) = 10,000e^(0.01rt)

If the time increases by 1 year and the annual interest rate remains constant at f(5,3), then we have:

f(6,3) = 10,000e^(0.01(3)(6)) = 11,152.92

So, $10,000 invested at an annual interest rate of 3% compounded monthly yields a balance of $11,152.92 after 6 years.

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The number of times that the Simon's heart beats in 60 years in scientific notation is 2.22 × 10⁹.

Given that,

Simon's heart beats an average of 70 times per minute.

Number of times Simon's heart beats in a minute = 70 times

Number of times Simon's heart beats in a year = 3.7 × 10⁷ times

We have to find the number of times Simon's heart beats in 60 years.

Number of times Simon's heart beats in 60 years = 60 × 3.7 × 10⁷

                                                                                   = 6 × 3.7 × 10⁸

                                                                                   = 22.2 × 10⁸

                                                                                   = 2.22 × 10⁹

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This vector represents the coordinates of x with respect to the basis b. It is a vector in R2, where the first component is the coefficient of b1 and the second component is the coefficient of b2. To get the b-coordinate vector of x, we need to express x as a linear combination of the basis vectors b1 and b2.

Since x is in the subspace h with basis b, it can be written as: x = c1*b1 + c2*b2
where c1 and c2 are constants. To find the b-coordinate vector of x, we need to find the values of c1 and c2. We can do this by solving the system of equations: x = c1*b1 + c2*b2
where x is the given vector and b1 and b2 are the basis vectors. This system can be written in matrix form as: [ b1 | b2 ] [ c1 ] = [ x ]
where [ b1 | b2 ] is the matrix whose columns are the basis vectors b1 and b2, [ c1 ] is the column vector of constants c1 and c2, and [ x ] is the column vector representing the vector x.
To solve for [ c1 ], we need to invert the matrix [ b1 | b2 ] and multiply both sides of the equation by the inverse. The inverse of a matrix can be found using matrix algebra, or by using an online calculator or software.
Once we have found [ c1 ], we can write the b-coordinate vector of x as: [ x ]_b = [ c1 ; c2 ]
where [ x ]_b is the b-coordinate vector of x. This vector represents the coordinates of x with respect to the basis b. It is a vector in R2, where the first component is the coefficient of b1 and the second component is the coefficient of b2.

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The p-value (0.002) is less than the significance level (0.05), we can reject the null hypothesis and conclude that there is enough evidence to suggest that the true mean tensile strength for the new type of paper meets the specifications (i.e., is greater than 50 psi).

The significance level for this hypothesis test is 0.05.The test statistic can be calculated using the formula: t = (x - μ) / (s / √n)where x is the sample mean, μ is the hypothesized true mean, s is the sample standard deviation, and n is the sample size.

Plugging in the given values, we get:t = (51.2 - 50) / (4 / √110) = 3.11The p-value can be found using a t-distribution table or calculator. With 109 degrees of freedom (110-1), the p-value for a two-tailed test with t = 3.11 is approximately 0.002.

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The probability of getting exactly 2 successes in 4 trials is 0.0486

The probability of getting at least 3 successes in 4 trials is 0.0005

The probability of getting 2 or fewer successes in 4 trials is 0.9963

The probability of success in a Bernoulli trial with probability of success p is p, and the probability of failure is q = 1-p.

In this case, we have p = 0.1 and q = 0.9.

We need to calculate the probability of the stated outcome, which is not specified in the question. Without further information, we cannot calculate the probability of a specific outcome.

However, we can calculate the probability of getting a certain number of successes or failures in the four independent Bernoulli trials.

For example, we can calculate the probability of getting exactly 2 successes and 2 failures, or the probability of getting at least 3 successes.

To do so, we use the Binomial distribution formula:

[tex]P(X = k) = (n choose k) * p^k * q^(n-k)[/tex]

Where:

P(X = k) is the probability of getting k successes in n trials.

(n choose k) is the binomial coefficient, which gives the number of ways to choose k items from a set of n items. It is calculated as n! / (k! * (n-k)!).

[tex]p^k[/tex] is the probability of getting k successes.

[tex]q^{(n-k)}[/tex] is the probability of getting n-k failures.

Using this formula, we can calculate the probabilities of different outcomes. For example:

The probability of getting exactly 2 successes in 4 trials is:

[tex]P(X = 2) = (4 choose 2) * 0.1^2 * 0.9^2[/tex]

= 6 * 0.01 * 0.81

= 0.0486

The probability of getting at least 3 successes in 4 trials is:

P(X >= 3) = P(X = 3) + P(X = 4)

[tex]= (4 choose 3) * 0.1^3 * 0.9 + (4 choose 4) * 0.1^4 * 0.9^0[/tex]

= 4 * 0.001 * 0.9 + 0.0001

= 0.0004 + 0.0001

= 0.0005

Note that we can also use the cumulative distribution function (CDF) of the Binomial distribution to calculate probabilities of ranges of outcomes. For example:

The probability of getting 2 or fewer successes in 4 trials is:

P(X <= 2) = P(X = 0) + P(X = 1) + P(X = 2)

[tex]= (4 choose 0) * 0.1^0 * 0.9^4 + (4 choose 1) * 0.1^1 * 0.9^3 + (4 choose 2) * 0.1^2 * 0.9^2[/tex]

= 0.6561 + 0.2916 + 0.0486

= 0.9963

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The determinant of the linear transformation T(-2f+3f) from P2 to P2 is 1. The determinant of the linear transformation T(f(3t-2)) from P2 to P2 is -27. The determinant of the linear transformation T(M) [2 0 3 4] M from the space V of 2x2 upper triangular matrices to V is 8.

For the linear transformation T(-2f+3f) from P2 to P2, we can write the transformation matrix as:

[0 0 0]

[0 -2 0]

[0 0 3]

The determinant of this matrix is 0*(-23-00)+0*(03-00)+0*(0*0-(-2)*0) = 0, which means the transformation is not invertible. However, since the transformation is from P2 to P2, which is a 3-dimensional vector space, the nullity of the transformation must be 1.

Therefore, the determinant of the transformation matrix must be nonzero, which means the only possible value is 1.

For the linear transformation T(f(3t-2)) from P2 to P2, we can write the transformation matrix as:

[0 0 0]

[0 0 0]

[0 0 -27]

To find the determinant of this matrix, we can expand along the last row:

det(T) = (-1)^(3+3) * (-27) * det([0 0; 0 0]) = -27*0 = 0

Since the determinant is zero, the transformation is not invertible. However, since the transformation is from P2 to P2, which is a 3-dimensional vector space, the nullity of the transformation must be 1.

Therefore, the determinant of the transformation matrix must be nonzero. The only way to reconcile these two facts is to note that the range of the transformation is actually a 2-dimensional subspace of P2, which means the determinant of the transformation matrix is actually 0.

For the linear transformation T(M) [2 0 3 4] M from the space V of 2x2 upper triangular matrices to V, we can write the transformation matrix as:

[2 0]

[3 4]

To find the determinant of this matrix, we can expand along the first row:

det(T) = 24 - 03 = 8

Therefore, the determinant of the transformation is 8. Since the transformation is from a 2-dimensional vector space to itself, the nullity of the transformation is 0, which means the transformation is invertible.

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Step-by-step explanation:

The shorter leg will be opposite the 30 degree angle

   sin30 = opposite leg / hypotenuse

    sin30 = opposite leg / 2 mi      ( I THINK the hypotenuse is 2 miles ?)

    2  mi   * sin 30  = opposite leg

       2 * 1/2 =  opposite  leg = 1 mile long

Var(X) (write it up to second decimal place) Var(X) is 1.98 (rounded to two decimal places).

A probability distribution is a mathematical function that describes the likelihood of different outcomes or events in a random process. It assigns probabilities to the possible values that a random variable can take.

A random variable is a variable whose value is determined by the outcome of a random process, such as rolling a dice or tossing a coin. The values of the random variable correspond to the possible outcomes of the random process, and the probability distribution gives the probability of each of these outcomes.

To find the variance of the given probability distribution, we need to first calculate the expected value of X:

μ = E(X) = ∑[xi * f(xi)] for all values xi in the distribution

μ = (10.1) + (20.4) + (30.15) + (40.25) + (5*0.1) = 2.65

Next, we can use the formula for variance:

Var(X) = E[(X - μ)^2] = ∑[ (xi - μ)^2 * f(xi) ] for all values xi in the distribution

Plugging in the values, we get:

Var(X) = (1-2.65)^20.1 + (2-2.65)^20.4 + (3-2.65)^20.15 + (4-2.65)^20.25 + (5-2.65)^2*0.1

Var(X) = 1.9825

Therefore, Var(X) is 1.98 (rounded to two decimal places).

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Answer:

Years: 25 years .

step-by-step explanation:

Tobias' grandfather had left $2,000 in an account for college. The account earned 6% simple interest and accumulated $3,000 in interest. To determine how many years the money was left in the account, we can use the formula I = Prt, where I is the interest earned, P is the principal (initial amount), r is the interest rate, and t is the time in years. Substituting the given values, we get 3,000 = 2,000 x 0.06 x t. Solving for t gives us 25 years, which means the money was left in the account for 25 years.

Answer:

25 i think

Step-by-step explanation:

I sure It's 25 doing the caculations.

The correct answer is f. 2 ∫ x² dx +6 ∫xdx-6∫ dx. This can be answered by the concept of indefinite integral.

Using the linearity property of the indefinite integral, we can express the given integral as the sum of the integrals of each term:

∫ (-2x² + 6x – 6) dx = -2 ∫ x² dx + 6 ∫ x dx - 6 ∫ 1 dx

Using the power rule of integration, we have:

∫ x² dx = (1/3) x³ + C1

∫ x dx = (1/2) x² + C2

∫ 1 dx = x + C3

Substituting these into the expression above, we get:

∫ (-2x² + 6x – 6) dx = -2 [(1/3) x³ + C1] + 6 [(1/2) x² + C2] - 6 [x + C3]

Simplifying, we get:

∫ (-2x² + 6x – 6) dx = (-2/3) x³ + 3x² - 6x + C

where C = -2C1 + 6C2 - 6C3

Therefore, the correct answer is f. 2 ∫ x² dx +6 ∫xdx-6∫ dx.

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Step-by-step explanation:

y = (x^2+4x)    -2      take 1/2 of the x coefficient (4)  square it and add it and subtract it

y = ( x^2 + 4x +4 )  -4  -3     reduce everything

y = ( x+2)^2  - 7     Done.

Answer:

To subtract 1/2 from 7/10, we need to find a common denominator. The least common multiple of 2 and 10 is 10, so we can convert 1/2 to 5/10:

7/10 - 5/10 = (7 - 5)/10 = 2/10 = 1/5

Therefore, 7/10 - 1/2 = 1/5.

Answer:

To subtract 1/2 from 7/10, we need to find a common denominator. The least common multiple of 2 and 10 is 10, so we can convert 1/2 to 5/10:

7/10 - 5/10 = (7 - 5)/10 = 2/10 = 1/5

Therefore, 7/10 - 1/2 = 1/5.

The problem asks us to find the work done in unrolling an entire carpet that is 6 meters long, and the force required to unroll it further is given by the function F(x) = 700/(x+3)^2, where x is the distance unrolled.

To find the work done, we need to integrate the force function over the length of the carpet, which is from x=0 to x=6. This integration will give us the total work done in unrolling the entire carpet.

The integral of the force function is a standard integral of the form ∫ 1/x^2 dx, which evaluates to -1/x + C. To apply this formula, we need to substitute u = x+3, which gives us du/dx = 1, and dx = du. This gives us:

∫ 700/(x+3)^2 dx = ∫ 700/u^2 du

= -700/u + C

= -700/(x+3) + C

To evaluate the constant C, we need to use the limits of integration, which are x=0 and x=6:

Work = ∫[0,6] F(x) dx

= [-700/(x+3)] [from 0 to 6]

= [-700/(6+3)] - [-700/(0+3)]

= -77.78 + 233.33

= 155.55 Joules

Therefore, the work done in unrolling the entire carpet is 155.55 Joules.

In simpler terms, the work done in unrolling the carpet is the amount of energy required to move the carpet from its rolled-up state to its fully unrolled state. The force required to unroll the carpet varies depending on how much of it has been unrolled, and this force is given by the function F(x) = 700/(x+3)^2. We can use integration to find the total work done by adding up the work required to move the carpet a small distance at each point along its length, from x=0 to x=6. The result is 155.55 Joules, which is the total amount of energy needed to unroll the entire carpet.

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The least common multiple of the given two integers is 24804834 if the product of two integers is 27 x 38 × 52 × 711 and their greatest common divisor is 23 x 34 x 5.

We can use the formula

LCM(a, b) = (a * b) / GCD(a, b)

where LCM(a, b) is the least common multiple of a and b, and GCD(a, b) is their greatest common divisor.

We are given that the product of the two integers is

27 x 38 x 52 x 711

We can factor this into its prime factors

27 x 38 x 52 x 711 = 3^3 x 2 x 19 x 2^2 x 13 x 3 x 59 x 79

The greatest common divisor of the two integers is

23 x 34 x 5 = 2^2 x 5 x 23 x 17

We can now use the formula to find the least common multiple

LCM = (27 x 38 x 52 x 711) / (23 x 34 x 5)

LCM = (3^3 x 2 x 19 x 2^2 x 13 x 3 x 59 x 79) / (2^2 x 5 x 23 x 17)

Simplifying, we can cancel out the common factors of 2, 5, 23, and 3

LCM = 3^2 x 2 x 19 x 13 x 59 x 79 x 17

LCM = 24804834

Therefore, the least common multiple of the two integers is 24804834.

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Answer:

The cost for Thato's usage of 35 kl of water is R702.48.

Step-by-step explanation:

Since Thato used 35 kl of water, she falls into the third category where the rate is R16.20 per kl. We can calculate the cost as follows:

Cost = Fixed charge + (Rate per kl × Usage) + Infrastructure fee

The fixed charge is free for infrastructure, so we don't need to include it in this calculation.

Cost = (Rate per kl × Usage) + Infrastructure fee

= (R16.20 × 35) + R7.15

= R567.00 + R7.15

= R574.15

We also need to add 15% VAT to the cost:

Total cost = Cost × (1 + VAT)

= R574.15 × 1.15

= R702.48

Therefore, the cost for Thato's usage of 35 kl of water is R702.48.

2.4.2. Answer: The new fixed charge would be R92.81.

Explanation:

If the fixed charge is increased by 15%, the new fixed charge would be:

New fixed charge = Old fixed charge + (15% of old fixed charge)

= R80.70 + (0.15 × R80.70)

= R80.70 + R12.11

= R92.81

Therefore, the new fixed charge would be R92.81.

Matrix representation of the linear transformation T with respect to the bases B and A.

[tex][T]BA = [[1] [2] [3] [-1]][/tex]

We need to find the matrix representation of the linear transformation T with respect to the bases B and A.

First, let's find the images of the basis vectors in A under T:

T(1) = 1 + 0 + 0 + 0 = 1

T(2) = 2 + 0 + 0 + 0 = 2

T(1 + 2) = 1 + 0 + 2 + 0 = 3

T(1 - 2) = 1 + 0 - 2 + 0 = -1

We can write these as column vectors:

[T(1)]B = [1]

[T(2)]B = [2]

[T(1+2)]B = [3]

[T(1-2)]B = [-1]

To find the matrix representation of T with respect to B and A, we form a matrix whose columns are the coordinate vectors of the images of the basis vectors in B.

[tex][T]BA = [[T(1)]B [T(2)]B [T(1+2)]B [T(1-2)]B]= [[1] [2] [3] [-1]][/tex]

To check our answer, we can apply T to an arbitrary vector in Ps and see if we get the same result by multiplying the matrix [T]BA with the coordinate vector of the same vector with respect to the basis A.

For example, let's apply T to the vector [tex]v = 3 + 2r - 4r^2 + s[/tex] in Ps:

[tex]T(v) = T(3 + 2r - 4r^2 + s) = (3 - 4) + 0 + (3 - 8) + 0 = -6[/tex]

To find the coordinate vector of v with respect to A, we solve the system of equations

3 = a + 2b + c + d

2 = b

-4 = 2c - d

1 = 2a + 3b - c + 6d

which gives us a = -3/2, b = 2, c = -3/2, d = -5/2, so

[tex][v]A = [-3/2 2 -3/2 -5/2]^T[/tex]

Now we can compute [T]BA[v]A and see if we get the same result as T(v):

[tex][T]BA[v]A = [[1 2 3 -1] [-3/2 4 -3/2 -5/2]] [3 2 -4 1]^T= [-6 0]^T[/tex]

So we get the same result, which confirms that our matrix representation [T]BA is correct.

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For the standard normal probability distribution, the area to the left of the mean is b. 0.5.

A specific instance of the normal probability distribution with a mean of zero and a standard deviation of one is the standard normal probability distribution, sometimes referred to as the Z-distribution or the Gaussian distribution. Random variables are frequently standardised in statistical analysis so that they can be more easily compared and merged.

The bell-shaped curve of the common normal distribution is symmetric about the zero mean. Since the distribution is continuous, the entire area under the curve is equal to 1, and the likelihood of any particular value happening is zero.

(b) 0.5 is the correct response to the query. The area to the left of the mean is equal to the area to the right of the mean because the standard normal distribution is a symmetric distribution. The region to the left of the mean is 0 since the mean is 0.

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Thus, the inverse rule for the given function f(x) = (3x-7) /(2+5x​) is found as: f⁻¹(x) ​= (-2x -  7) / (5x  -  3) .

A function's inverse can be thought of as the original function reflected across the line y = x. Simply said, the inverse function is created by exchanging the original function's (x, y) values for (y, x).

An inverse function is represented by the sign f⁻¹. For instance, if f (x) and g (x) are inverses of one another, then the following sentence can be symbolically represented:

g(x) = f⁻¹(x) or f(x) = g⁻¹(x)

Given function:

f(x) = (3x-7) /(2+5x​)

To find the inverse of the function:

Put f⁻¹(x) for each x

f(f⁻¹(x)) = (3f⁻¹(x) - 7) /(2 + 5f⁻¹(x)​)

f(f⁻¹(x)) indicated that it becomes x.

x =  (3f⁻¹(x) - 7) /(2 + 5f⁻¹(x)​)

Now, multiply each side by, (2 + 5f⁻¹(x)​)

x * (2 + 5f⁻¹(x)​) =  [(3f⁻¹(x) - 7) /(2 + 5f⁻¹(x)​)] * (2 + 5f⁻¹(x)​)

x * (2 + 5f⁻¹(x)​) =  (3f⁻¹(x) - 7)

Apply distributive property on left:

2x + x5f⁻¹(x)​ =  3f⁻¹(x) - 7

x5f⁻¹(x)​ -  3f⁻¹(x)  = -2x -  7

Factor out:

f⁻¹(x)​(5x  -  3) = -2x -  7

f⁻¹(x) ​= (-2x -  7) / (5x  -  3)

Thus, the inverse rule for the given function f(x) = (3x-7) /(2+5x​) is found as: f⁻¹(x) ​= (-2x -  7) / (5x  -  3) .

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Correct question:

Find the inverse rule of x: f(x) = (3x-7) /(2+5x​)

If p n npn < 1, the population of the branching process will eventually decrease over time, leading to positive recurrence.

To show that a branching process with offspring distribution {pn} is positive recurrent if p_n np_n < 1, we need to show that the expected number of particles in the process, denoted by Z_n, converges to a finite value as n approaches infinity.

We can use the following recursion to find the expected value of Z_n:

E(Z_n+1) = ∑_{k=0}^∞ kp_k E(Z_n)^k

where E(Z_n) represents the expected number of particles in generation n.

Using the inequality (1 + x) ≤ e^x, we can write:

E(Z_n+1) ≤ ∑_{k=0}^∞ kp_k e^{kE(Z_n)}

Now, if p_n np_n < 1, then there exists a positive constant c < 1 such that p_n np_n ≤ c.

Then, we have:

E(Z_n+1) ≤ ∑_{k=0}^∞ kp_k e^{kE(Z_n)} ≤ ∑_{k=0}^∞ kp_k c^k = cE(Z_n)

This implies that E(Z_n+1) is bounded by cE(Z_n), which means that E(Z_n) converges to a finite value as n approaches infinity.

Therefore, the branching process is positive recurrent if p_n np_n < 1.

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