Now you have everything you need to calculate the correlation coefficient r for the variables and y, using this formula :r= 1/n-1 Σ(xi - x) (yi - y) / sz syenter the correct value for r to two decimal places. keep in mind that the correlation coefficient can range in value between –1 and 1.

The value of dw/dv when u=0 and v=0 is -23.

To find dw/dv when u=0 and v=0, first we need to substitute the given values of u and v into the expressions for x and y, and then take the partial derivative of w with respect to v.

1. Substitute u=0 and v=0 into x and y expressions:
x = 4(0) - 3(0) + 1 = 1
y = 2(0) + (0) - 6 = -6

2. Substitute the values of x and y into the expression for w:
w = x² + y/x = (1)² + (-6)/(1) = 1 - 6 = -5

3. Find the partial derivative of w with respect to v using the chain rule:
dw/dv = (dw/dx)*(dx/dv) + (dw/dy)*(dy/dv)

4. Calculate the derivatives:
dw/dx = 2x - y/x² = 2(1) - (-6)/(1)² = 2 + 6 = 8
dw/dy = 1/x = 1/1 = 1
dx/dv = -3
dy/dv = 1

5. Plug the derivatives back into the expression for dw/dv:
dw/dv = (8)*(-3) + (1)*(1) = -24 + 1 = -23

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The transformation(s) that map the hexagon exactly onto itself is Clockwise rotation about Y by 60°, Reflection across line w, Reflection Across line u, and Counter clockwise rotation about Y by 120°. So, the correct answer is A), B), C) and D).

A regular hexagon has rotational symmetry of order 6 and reflectional symmetry across its 6 lines of symmetry. Therefore, any rotation of the hexagon by an angle which is a multiple of 60 degrees or any reflection across one of its lines of symmetry will map the hexagon exactly onto itself.

From the given options, the following transformations will map the hexagon exactly onto itself Clockwise rotation about Y by 60°, Reflection across line w, Reflection across line u and Counter clockwise rotation about Y by 120°. So, the correct option is A), B), C) and D).

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To find the tangent line approximation for 1 ‾‾‾‾‾√ near =2, we first need to find the derivative of the function f(x) = 1 ‾‾‾‾‾√.

Using the power rule of differentiation, we get: f'(x) = 1/2(x)^(-1/2) Now, we can substitute the value a = 2 and f'(a) = f'(2) = 1/2(2)^(-1/2) = 1/2√2 into the formula: f(x) = f^1(a)(x-a) + f(a) to get the equation of the tangent line at x = 2: y = 1/2√2(x-2) + 1 Therefore, the tangent line approximation for 1 ‾‾‾‾‾√ near =2 is y = 1/2√2(x-2) + 1,

where the slope of the line is given by f'(2) and the point (2,1) lies on the line.  Use the formula for the tangent line approximation: f(x) ≈ f^1(a)(x-a) + f(a). For x near 2, f(x) ≈ (1/2√2)(x-2) + √2. This is the tangent line approximation for f(x) = √x near x = 2.

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We have reduced the standard vertex cover problem to the restricted vertex cover problem with even degree nodes, and this reduction preserves the size of the vertex cover.

The vertex cover, or hitting set, is a subset of that meets every member of. A vertex cover of a graph can be conceived of more simply as a set of vertices such that every edge of has at least one member of as an endpoint. A graph's vertex set is thus always a vertex cover.

To show that the vertex cover problem is NP-Complete even when all vertices are restricted to have even degrees, we can reduce from the standard vertex cover problem.

Suppose we have an instance of the standard vertex cover problem, given by an undirected graph G = (V, E). We will construct an instance of the restricted vertex cover problem, given by an undirected graph G' = (V', E'), where V' = V ∪ W and E' = E ∪ F, such that G has a vertex cover of size k if and only if G' has a vertex cover of size k + |W|.

We construct the set W of new nodes as follows: for each node v in V with odd degree, we add a new node w to W and connect it to v in G'. Now, every node in G' has even degree, except for the nodes in W, which have odd degree.

Suppose we have a vertex cover C of size k in G. We construct a vertex cover C' in G' as follows: for each node v in C, we include v in C'. For each node w in W, we include its neighbor v in C'. Note that |C'| = k + |W|.

Suppose we have a vertex cover C' of size k + |W| in G'. We can construct a vertex cover C in G as follows: for each node v in C' ∩ V, we include v in C. For each node w in C' ∩ W, we include its neighbor v in C. Note that |C| = k, since we include one node in C for each node in W.

Therefore, we have reduced the standard vertex cover problem to the restricted vertex cover problem with even degree nodes, and this reduction preserves the size of the vertex cover. Since the standard vertex cover problem is NP-Complete, we conclude that the restricted vertex cover problem with even degree nodes is also NP-Complete.

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Answer:

C

Step-by-step explanation:

7 + 45/5 = 16

The composite function g(f(x)) = 3x² + 9x - 25. Given that f(x) = x²  + 3x - 8 and g(x) = 3x - 1, we need to find the composite function g(f(x)). This means we'll substitute the entire f(x) function into the g(x) function.

Step 1: Identify f(x) and g(x)
f(x) = x²  + 3x - 8
g(x) = 3x - 1
Step 2: Substitute f(x) into g(x) for the variable x
g(f(x)) = 3(f(x)) - 1
Step 3: Replace f(x) with its expression, which is x^2 + 3x - 8
g(f(x)) = 3(x²  + 3x - 8) - 1
Step 4: Distribute the 3 to each term inside the parentheses
g(f(x)) = 3x²  + 9x - 24 - 1
Step 5: Combine like terms (in this case, just the constants)
g(f(x)) = 3x²  + 9x - 25
So, the composite function g(f(x)) = 3x²  + 9x - 25. If anyone has difficulty with these problems, we recommend reviewing Sections 1.1-1.3 for a better understanding of function compositions and related topics.

To find the function g o f, we need to substitute the function f(x) into the function g(x) wherever we see x. So, g o f(x) = g(f(x)).
First, we find f(x):
f(x) = x²  + 3x - 8
Now we substitute f(x) into g(x):
g(f(x)) = g(x²  + 3x - 8)
= 3(x²  + 3x - 8) - 1
= 3x²  + 9x - 25
Therefore, g o f(x) = 3x²  + 9x - 25.
Given that f(x) = x²  + 3x - 8 and g(x) = 3x - 1, we need to find the composite function g(f(x)). This means we'll substitute the entire f(x) function into the g(x) function.

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The perimeter, in units, of the triangle is x² - 9

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

The three sides of a triangle have lengths of

x units, (x-4) units, and (x² - 2x - 5) units

The perimeter, in units, of the triangle is the sum of the side lenths

So, we have

Perimeter = x + x - 4 + x² - 2x - 5

Evaluate the like terms

So, we have

Perimeter = x² - 9

Hence, the perimeter is x² - 9

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Answer:

158

Step-by-step explanation:

There is a pattern.

77 - 55 = 24

110 - 79 = 31

Which has no pattern.

But when you subtract by 55

110 - 55 = 55

Then you multiply 70 with 2

70 x 2 = 158

Hope this helps!

Glide reflections or Translations, either can  used to create the pattern below, starting with the top figure.

By a transformation of the plane, we mean a map from the set of points in the plane, into the set of the plane, or more colloquially a map from the plane and into the plane.  By a symmetry of the plane we mean a transformation of the plane, which is a bijection, and which is an isometry. that is, it keeps the distance between any two points fixed. The set of all symmetries of the plane form a group, called the symmetry group of the plane. This group is generated by 3 types of elements. [tex]R_L[/tex], which is a reflection about some line [tex]L[/tex] in the plane, [tex]r_\theta[/tex] a rotation about the origin by an angle [tex]\theta[/tex], and [tex]T_v[/tex], a Translation on the plane by a vector [tex]v[/tex]. There is a fourth type of symmetry got by composing a reflection and a translation, called a glide-reflection.[tex]G_{v,L} = T_v \circ R_L[/tex] Together these give all the rigid symmetries of the plane.

To get the the pattern below from the figure above, we can simply translate the arrow four times, and get the pattern. We can also get the pattern by first  reflecting it about the horizontal line midway between the figure and the pattern, followed by 4 translations. cumulatively translation . reflection = glide-reflection, so by four glide reflections we can get the pattern below.

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The formula for the general term a_n of the given sequence. The sequence provided is: 3, 8, 13, 18, ...

Step 1: Identify the pattern
We can see that the difference between consecutive terms is constant:
8 - 3 = 5
13 - 8 = 5
18 - 13 = 5

Step 2: Define the sequence
Since the difference between consecutive terms is constant, this is an arithmetic sequence. The common difference (d) is 5.

Step 3: Find the formula for the general term a_n
The formula for the general term of an arithmetic sequence is:
a_n = a_1 + (n - 1) * d

where a_n is the nth term, a_1 is the first term, n is the position of the term in the sequence, and d is the common difference.

Step 4: Plug in the known values
In our case, a_1 = 3 and d = 5. Plugging these values into the formula, we get:
a_n = 3 + (n - 1) * 5

Step 5: Simplify the formula
a_n = 3 + 5n - 5
a_n = 5n - 2

So the formula for the general term a_n of the sequence is:
a_n = 5n - 2

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The answer for vector ¯ x determined by the coordinate vector [ ¯ x ] b and the given basis b is ¯ x = [x1, x2, ..., xn] · [b1, b2, ..., bn]

To find the vector ¯ x determined by the coordinate vector [ ¯ x ] b and the given basis b, we simply multiply each basis vector by its corresponding coordinate and add the results.

Let's say that the basis b consists of n linearly independent vectors {b1, b2, ..., bn} and that the coordinate vector of ¯ x with respect to b is [ ¯ x ] b = [x1, x2, ..., xn].

Then the vector ¯ x is determined by the coordinate vector [ ¯ x ] b and the basis b is given by:

¯ x = x1b1 + x2b2 + ... + xnbn

This is because each basis vector bi corresponds to a single coordinate xi in the coordinate vector [ ¯ x ] b, and the sum of all of these scalar multiples gives us the vector ¯ x.

Therefore, we can say that the vector ¯ x is determined by the coordinate vector [ ¯ x ] b, and the basis b is given by the formula:

¯ x = [x1, x2, ..., xn] · [b1, b2, ..., bn]

where the dot product of the coordinate vector [x1, x2, ..., xn] and the basis vector matrix [b1, b2, ..., bn] gives us the vector ¯ x.

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The first number game to be lost on one throw is craaps, with the losing numbers being 2, 3, or 12.

In craaps, a dice game, the first roll is called the "come-out roll." If a player rolls a 7 or 11, they win instantly. However, if they roll a 2, 3, or 12, they lose immediately, and this is called "craapping out." These losing numbers are also referred to as "craaps."

If any other number is rolled, it becomes the "point" and the player must roll the same number again before rolling a 7 to win.

The game continues until the player rolls the point number or a 7, at which point the game ends, and a new round begins. The objective is to predict the outcome of the dice roll and bet accordingly, with different betting options available to the players.

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Given that,

Principal amount, P = 2000 dollars

Rate of interest, r = 6.75% = 0.0675

Final amount, A = 2900 dollars

The formula to find the final amount in a compound interest is,

A = P (1 + [tex]\frac{r}{n}[/tex] )^ (nt)

n = number of times interest compounded in a year = 12 (Since compounded monthly.

Substituting the given values,

[tex]2900 = 2000 \huge \text[1 + \huge \text(\dfrac{0.0675}{12} \huge \text)\huge \text]^{(12t)}[/tex]

[tex]2900 = 2000 (1.005625)^{(12t)[/tex]

[tex]2900 = 2000 (1.069628)^t[/tex]

[tex](1.069628)^t = 1.45[/tex]

Taking logarithms on both sides,

[tex]\text{t} =\dfrac{\text{log}(1.45)}{\text{log}(1.069628)}[/tex]

[tex]\boxed{\bold{t = 5.52 \thickapprox 5.5}}[/tex]

Hence the time that the person must keep the money is 5.5 years.

If I were dealing with a data set that fluctuates quarterly, I would recommend using autoregressive models.

option (b) is correct

This is because autoregressive models take into account the pattern of the previous values to predict future values. Simple moving averages and exponential smoothing are better suited for data sets with more consistent trends, while the random walk is not an ideal method for forecasting as it assumes that future values will be equal to the previous value with no pattern or trend. Therefore, autoregressive models would be the most appropriate method for forecasting a quarterly fluctuating data set.

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A better, less biased question would be: "Should companies that provide diesel engines be responsible for any costs of purifying air quality?"

The question, "Should companies that provide diesel fuel engines that pollute the environment pay a tax on each engine to help in the cost of cleaning the air?" is biased due to the use of the word "pollute."

A better, less biased question would be: "Should companies that provide diesel engines be responsible for any costs of purifying air quality?"

This question removes the negative connotation associated with the word "pollute" and focuses on the responsibility of companies to contribute to air quality improvement.

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A better, less biased question would be: "Should companies that provide diesel engines be responsible for any costs of purifying air quality?"

The question, "Should companies that provide diesel fuel engines that pollute the environment pay a tax on each engine to help in the cost of cleaning the air?" is biased due to the use of the word "pollute."

A better, less biased question would be: "Should companies that provide diesel engines be responsible for any costs of purifying air quality?"

This question removes the negative connotation associated with the word "pollute" and focuses on the responsibility of companies to contribute to air quality improvement.

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The slopes and the y-intercept of a linear function that is represented by the table is: D. the slope is 2/5 and the y-intercept is -1/3.

In Mathematics and Geometry, the point-slope form of a straight line can be calculated by using the following mathematical equation (formula):

y - y₁ = m(x - x₁)

Where:

First of all, we would determine the slope of this line;

Slope (m) = (y₂ - y₁)/(x₂ - x₁)

Slope (m) = (-2/15 + 1/30)/(-1/2 + 3/4)

Slope (m) = -0.1/0.25

Slope (m) = -0.4 or 2/5.

At data point (-3/4, -1/30) and a slope of 2/5, a linear function in slope-intercept form for this line can be calculated by using the point-slope form as follows:

y - y₁ = m(x - x₁)

y - (-1/30) = 2/5(x + 3/4)

y = 2x/5 - 1/3

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The explicit formula for the sequence {an} is:

an = 14 + 5n + 100(n-1) for even n
an = -(14 + 5n + 100(n-1)) for odd n

To find the explicit formula for the sequence {an} with the given terms 14, -19, 114, -119, 124, ...,
Step 1: Observe the pattern
We can see that the signs alternate between positive and negative, and the absolute values of the terms follow the pattern 14, 19, 114, 119, 124, ...
Step 2: Identify the explicit formula
The absolute values can be expressed as the sequence: 14, 14 + 5, 14 + 100, 14 + 105, 14 + 200, ...
This suggests a pattern: an = 14 + 5n + 100(n-1) when n is even, and an = -(14 + 5n + 100(n-1)) when n is odd.

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The length of DF, considering the crossing chords in a circle, is given as follows:

DF = 34.9.

A chord of a circle is a straight line segment that connects two points on the circle. Specifically, it is a line segment whose endpoints are on the circle. A chord is often denoted by drawing a line segment between the two endpoints, with the segment passing through the interior of the circle.

When two chords intersect each other, then the products of the measures of the segments of the chords are equal.

Applying the multiplication, the length of DF is given as follows:

11DF = 12 x 32

DF = 12 x 32/11

DF = 34.9.

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The period of the motion of the 2 kg mass suspended from the ideal spring with spring constant 18 N/m and no friction is 2/3 seconds.

This can be calculated using the formula T=2π√(m/k), where T is the period, m is the mass, and k is the spring constant. Plugging in the given values, we get T=2π√(2/18)=2π/3≈2.09 seconds.

However, we are only interested in one full cycle, which is half of the period, so the answer is 2.09/2=1.045 seconds, or approximately 2/3 seconds. This means that the mass will complete one full oscillation in approximately 2/3 seconds.

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The solution for the differential equation is y = ±√((x + 5 ln|x| + c)/x).

To find the general solution for the differentia equation follow these steps:

We begin by separating the variables and integrating:

xy^2y' = x + 5

y^2 dy/dx = (x + 5)/x

Integrating both sides with respect to x:

∫y^2 dy = ∫(x + 5)/x dx

Simplifying the right-hand side:

∫y^2 dy = ∫1 dx + ∫5/x dx

∫y^2 dy = x + 5 ln|x| + c

Now we solve for y:

y^2 = (x + 5 ln|x| + c)/x

y = ±√((x + 5 ln|x| + c)/x)

Thus, the general solution to the differential equation is:

y = ±√((x + 5 ln|x| + c)/x)

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Step-by-step explanation:

lets first find area of trapezium

1/2 x (a+b)x h

1/2 x (8x10) x6 (10 because 6 plus 4)

1/2 x 80 x 6

1/2 x 480

240 cm square is area of trapezium

now to find area of triangle

1/2x b x h

1/2 x 6 x 6

1/2x 36

18cm square

now if u want area of shaded part

trapezium minus triangle

240 minus 18

222cm square

The given statement "To change the contents of a macro, you must use the Record Macro button to step into the macro" is false because one can use other options such open the Visual Basic Editor .

To change the contents of a macro,

It is not necessary we have to use the Record Macro button to step into the macro.

Here one can also open the Visual Basic Editor instead of record Macro button.

In the Visual Basic Editor Project Explorer window.

First we have to open the project folder

And then in the Modules folder we have to select Recorded.

Finally select the module that has the name of the macro.

Here the recorded macro code is going to displayed in the code window.

First one can find the macro in the project window, and then edit the code directly.

Alternatively, one can use the Macro dialog box to have a view and to edit the macro.

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By using bayes’ theorem;

P(h|a) = 0.0625.

We can use Bayes' theorem to calculate P(h|a) as follows:

P(h|a) = P(a|h) * P(h) / P(a)

where P(a) is the total probability of event a, given by:

P(a) = P(a|h) * P(h) + P(a|m) * P(m) + P(a|l) * P(l)

We are given that P(a|h) = 0.2, P(a|m) = 0.3, and P(a|l) = 0.2. We are also given that the events h, m, and l are collectively exhaustive, which means that their probabilities add up to 1. Therefore, we have:

P(m) + P(l) = 0.4 + P(l) = 1 - P(h) = 0.9

Solving for P(l), we get:

P(l) = 0.5

Now we can use Bayes' theorem to calculate P(h|a) as follows:

P(h|a) = P(a|h) * P(h) / P(a)

= 0.2 * 0.1 / (0.2 * 0.1 + 0.3 * 0.4 + 0.2 * 0.5)

= 0.02 / 0.32

= 0.0625

Therefore, P(h|a) = 0.0625.

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Cov(Y1, Y2) = 0; p1(−1)p2(0) = 1/3, where Y1 and Y2 have the joint probability function.

To find the covariance of Y1 and Y2, we need to first find their means:

E(Y1) = (-1)(1/3) + (0)(1/3) + (1)(1/3) = 0

E(Y2) = (0)(1/3) + (1)(1/3) + (0)(1/3) = 1/3

Using the definition of covariance, we have:

Cov(Y1, Y2) = E(Y1Y2) - E(Y1)E(Y2)

To find E(Y1Y2), we use the joint probability function:

E(Y1Y2) = (-1)(0)(1/3) + (0)(1)(1/3) + (1)(0)(1/3) = 0

Therefore, we have:

Cov(Y1, Y2) = E(Y1Y2) - E(Y1)E(Y2) = 0 - (0)(1/3) = 0

To find p1(-1)p2(0), we simply evaluate the joint probability function at (Y1, Y2) = (-1, 0):

p(-1, 0) = 1/3

Therefore, we have:

p1(-1)p2(0) = (1/3)(1) = 1/3

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a) Rejecting H0 at α = 0.05 does not necessarily mean it will be rejected          at α = 0.01.

b) If H0 is rejected at α = 0.01, it will also be rejected at α = 0.05.

a) No, rejecting the null hypothesis (H0) at the α = 0.05 level of significance does not necessarily mean that H0 would be rejected at the α = 0.01 level of significance.

The significance level (α) represents the probability of making a Type I error, which is the incorrect rejection of a true null hypothesis.

A lower significance level means a more stringent criterion for rejecting the null hypothesis. Therefore, if H0 is rejected at α = 0.05, it means that there is sufficient evidence to reject H0 at a relatively less stringent level.

However, this does not automatically imply that the same conclusion would hold at a more stringent level (α = 0.01). Further analysis would be required to make a conclusion at a different significance level.

b) Yes, if H0 is rejected in favor of H1 at the α = 0.01 level of significance, it would also be rejected at the α = 0.05 level of significance.

This is because a lower significance level (α = 0.01) represents a more stringent criterion for rejecting the null hypothesis compared to a higher significance level (α = 0.05).

If the null hypothesis is rejected at α = 0.01, it means that there is strong evidence to reject H0, and the same conclusion would hold at a less stringent level (α = 0.05) as well.

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The t value that forms the boundary of the critical region in the right-hand tail for a one-tailed test with α = .01 is 2.821 for sample size a. n=10, 2.861 for sample size b. n=20, and 2.756 for sample size c. n=30.

To find the t value that forms the boundary of the critical region in the right-hand tail for a one-tailed test with α = .01, we need to consult the t-distribution table. Specifically, we need to find the t value that corresponds to the α/2 = .005 level of significance (since this is a one-tailed test in the right-hand tail, we only need to consider the upper tail of the distribution).
For sample size a (n=10), the degrees of freedom (df) = n-1 = 9. From the t-distribution table with 9 degrees of freedom and α/2 = .005, we find a t value of 2.821.
For sample size b (n=20), the degrees of freedom (df) = n-1 = 19. From the t-distribution table with 19 degrees of freedom and α/2 = .005, we find a t value of 2.861.
For sample size c (n=30), the degrees of freedom (df) = n-1 = 29. From the t-distribution table with 29 degrees of freedom and α/2 = .005, we find a t value of 2.756.
So, the t value that forms the boundary of the critical region in the right-hand tail for a one-tailed test with α = .01 is 2.821 for sample size a, 2.861 for sample size b, and 2.756 for sample size c.

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The forecasted number of aerospace products and parts industry employees in 2007 is approximately 468,000

To find the quadratic function that best fits the given data set, we can use a graphing calculator that supports quadratic regression.

Using the data from the table, we can enter the values into the calculator and perform a quadratic regression to obtain the quadratic function.

Here are the steps to perform quadratic regression on a TI-84 graphing calculator:

The calculator will display the quadratic function that best fits the data in the form of:

[tex]y = ax^2 + bx + c[/tex]

Using the coefficients from the regression, we can plug in the value x = 17 to forecast the value of the function at the indicated point (which corresponds to the year 2007, since 1990 is the reference year).

Using a TI-84 calculator to perform the regression, we obtain the quadratic function:

[tex]y = -33.28x^2 + 1164.15x - 9732.03[/tex]

To forecast the value of the function in 2007, we plug in x = 17 (since 2007 is 17 years after 1990):

[tex]y = -33.28(17)^2 + 1164.15(17) - 9732.03[/tex]

= 468.31

Therefore, the forecasted number of aerospace products and parts industry employees in 2007 is approximately 468,000 (rounded to the nearest whole number).

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The probability that x is more than 12 is approximately 0.5134.

The exponential distribution with mean θ has the probability density function:

f(x) = (1/θ) * exp(-x/θ)

where x ≥ 0.

To find the probability that x is more than 12, we need to integrate the probability density function from 12 to infinity:

P(X > 12) = ∫[12,∞] f(x) dx

= ∫[12,∞] (1/θ) * exp(-x/θ) dx

= [tex][-exp(-x/\theta)]_{[12,\infty]}[/tex]

=[tex]-lim_{[t\rightarrow\infty]} exp(-t/\theta) + exp(-12/\theta)[/tex]

= exp(-12/θ)

where we have used the property that the exponential function approaches zero as its argument approaches negative infinity.

Substituting the given value of θ = 15, we get:

P(X > 12) = exp(-12/15)

≈ 0.5134

Therefore, the probability that x is more than 12 is approximately 0.5134.

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The value of m in the given expression is 2 23/30.

The given expression is 8 1/6 = 5 2/5 + m.

We subtract 5 2/5 on both sides.

8 1/6 - 5 2/5 = m.

8 1/6 can be written as 49/6.

5 2/5 can be written as 27/5.

Now, 49/6 - 27/5 = m.

The Least Common Multiple(LCM) of 6 and 5 is 30.

(49*5 - 27*6)/30 = m.

(245 - 162)/30 = m.

m = 83/30.

m = 2 23/30.

To know more about LCM:https://brainly.com/question/20739723

The complete question is, Find the value of m in the expression 8 1/6 = 5 2/5 + m.