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The percentage of the day the student spend at school in the pie chart is derived to be 35 percentage.

In a pie chart, the size of each sector is proportional to the value it represents. Therefore, the percentage represented by each sector can be calculated by dividing the value of that sector by the total value and multiplying by 100.

We shall represent the percentage of the day the student spend at school with the letter x, such that:

x = (126 × 100)/360

x = 7 × 5

x = 35%

Therefore, the percentage of the day the student spend at school is derived to be 35 percentage.

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The discriminant of f(x)is -28, and f(x) has no distinct real number zeros.

The expression[tex]b^{2}- 4ac[/tex] gives the value of discriminant of the quadratic function with the form f(x) = [tex]ax^{2} + bx + c[/tex]. This result is obtained through using this formula on the quadratic function, where f(x) = [tex]-4x^{2}+ 10x - 8[/tex]:  [tex]b^2 - 4ac = (10)^2 - 4(-4)(-8)[/tex] = 100-128 = -28. Hence, -28 is the discriminant of f(x).

The discriminant informs us of the characteristics of the quadratic equation's roots. There are two unique real roots if the discriminant index is positive. There is just one real root (with a multiplicity of 2) if the discriminator is zero. There are only two complicated roots (no real roots) if discrimination is negative.

Given that f(x)'s discriminant is minus (-28), we can conclude that there are no true roots. F(x) contains two complex roots as a result. This is further demonstrated by the fact that the parabola widens downward and does not cross the x-axis, as indicated by the fact that the coefficient of the [tex]x^{2}[/tex] term in f(x) is negative.

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True. Regression is a functional relationship between two or more correlated variables, where one variable is used to predict another. This statistical method helps in understanding the relationship between variables and making predictions based on that information.

Regression analysis is a powerful tool in statistics that helps to identify the relationship between variables, and it can be used to make predictions or forecasts based on that relationship. It involves fitting a mathematical model to the data, and then using that model to estimate the value of one variable based on the values of the other variables. There are many different types of regression analysis, each suited to different types of data and research.

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The surface area of the cube is 1014 square millimeters.

The formula for the surface area of a cube is a=6s², where s is the length of the side of the cube. Given that the length of each side of the cube is 13 mm, we can substitute this value into the formula and simplify:

a = 6s²

a = 6(13²)

a = 6(169)

a = 1014

The surface area of a cube refers to the total area of all its faces. Since a cube has 6 faces of equal size, we can multiply the area of one face by 6 to obtain the total surface area of the cube. The formula A = 6s² represents this concept, where s is the length of the side of the cube.

Therefore, the surface area of the cube with a side length of 13 mm is 1014 square millimeters.

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Using the linear regression equation, we predict Mastura's revenue for the month to be approximately RM 1,165.55 when there are 49 departure delays.

The general form of a linear equation is:

Revenue = Intercept + (Coefficient for Delays * Number of Delays)

In this case, the Intercept is 729.48138, and the Coefficient for Delays is 8.9014135. So the equation becomes:

Revenue = 729.48138 + (8.9014135 * Number of Delays)

Now, we need to predict the revenue for the month when there are 49 departure delays:

Revenue = 729.48138 + (8.9014135 * 49)

Revenue = 729.48138 + (436.0690615)

Revenue = 1165.5504415

Thus, Mastura's revenue for the month is approximately RM 1,165.55 when there are 49 departure delays.

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The correct range of the given function is :

c. {y € R, y>=0}.

To determine the range of the function y = (x-2)^(1/2), we need to consider the possible values of y that can be obtained for different values of x.

a. {x € R} means that x can take any real value. However, since the square root of a negative number is not a real number, y can only take non-negative values. So, the range is {y € R, y>=0}.

b. {x € R, x>=2} means that x can take any real value greater than or equal to 2. Again, the square root of a negative number is not a real number, so y can only take non-negative values. So, the range is {y € R, y>=0}.

d. {y € R} means that y can take any real value. However, if we plug in a value of x less than 2, we get a negative value under the square root, which is not a real number. So, the range is not {y € R}.

Therefore, the correct answer is c. {y € R, y>=0}.

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The distance of the jot air balloon to ground is 21.62 ft.

Here, we have,

In triangle ACB:

tan36° = x/y

x  = y tan36°

In triangle ADB:

tan25° = x/y + 12

x  = y+12 * tan25°

Therefore equating both equations gives:

y tan36°  =  y+12 * tan25°

y tan36°  =  y tan25° + 12tan25°

so, we get,

y = 21.50 ft

Therefore x = 21.50*tan(36) = 15.62 ft

The distance of the jot air balloon to ground = 15.62 + 6 = 21.62 ft

Hence, The distance of the jot air balloon to ground is 21.62 ft.

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The largest rectangle inscribed in a circle of radius 1 has an area of 2√2 units.

Draw the circle of radius 1 and sketch the rectangle inscribed in it. Let the length of the rectangle be 2x and the width be 2y. By symmetry, we know that the diagonals of the rectangle will pass through the center of the circle.

The length of the diagonal is equal to the diameter of the circle, which is 2. Using the Pythagorean theorem, we can write an equation relating the side lengths of the rectangle and the diameter of the circle

(2x)² + (2y)² = 2²

Simplifying, we get

4x² + 4y² = 4

Dividing both sides by 4, we get

x² + y² = 1

We want to maximize the area of the rectangle, which is given by A = 4xy.

We can use the equation from step 5 to solve for y in terms of x

y² = 1 - x²

y = √(1 - x²)

Substituting this into the area formula, we get

A = 4x*√(1 - x²)

To maximize this function, we can take the derivative with respect to x and set it equal to zero

dA/dx = 4(1 - x²)^(-1/2) - 4x²(1 - x²)^(-3/2) = 0

Solving for x, we get x = 1/√(2) or x = -1/√(2).

We can discard the negative solution since we are looking for a positive length.

Using x = 1/√(2), we can find the corresponding value of y

y = √(1 - x²) = √(1 - 1/2) = √(1/2)

Finally, we can calculate the area of the rectangle using these values

A = 4xy = 4(1/√(2))(√(1/2)) = 2(√(2))

Therefore, the area of the largest rectangle that can be inscribed in a circle of radius 1 is 2(√(2)).

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The Taylor series for f(x) centered at a = 5p is:

f(x) = f(a) + f'(a)(x-a) + f''(a)(x-a)^2/2! + f'''(a)(x-a)^3/3! + ...

To find the derivatives of f(x), we use the chain rule and the derivative of cos x:

f(x) = 4 cos x
f'(x) = -4 sin x
f''(x) = -4 cos x
f'''(x) = 4 sin x
f''''(x) = 4 cos x
...

Substituting a = 5p and evaluating the derivatives at a, we get:

f(5p) = 4 cos(5p) = 4
f'(5p) = -4 sin(5p) = 0
f''(5p) = -4 cos(5p) = -4
f'''(5p) = 4 sin(5p) = 0
f''''(5p) = 4 cos(5p) = 4
...

Therefore, the Taylor series for f(x) centered at a = 5p is:

f(x) = 4 - 4(x-5p)^2/2! + 4(x-5p)^4/4! - ...

Simplifying the series, we get:

f(x) = 4 - 2(x-5p)^2 + (x-5p)^4/3! - ...

Note that this is the Maclaurin series for cos x, with a = 0, multiplied by 4 and shifted to the right by 5p.

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A researcher records the following scores on a working memory quiz for two samples. Sample B has the largest standard deviation.

To determine which sample has the largest standard deviation, we need to calculate the standard deviation for both samples. Here are the steps to calculate the standard deviation:

1. Find the mean (average) of each sample.
2. Calculate the difference between each score and the mean.
3. Square the differences.
4. Find the mean of the squared differences.
5. Take the square root of the mean of the squared differences.

Sample A:
1. Mean: (2+3+4+5+6+7+8)/7 = 5
2. Differences: -3, -2, -1, 0, 1, 2, 3
3. Squared differences: 9, 4, 1, 0, 1, 4, 9
4. Mean of squared differences: (9+4+1+0+1+4+9)/7 = 28/7 = 4
5. Standard deviation: √4 = 2

Sample B:
1. Mean: (4+5+6+7+8+9+10)/7 = 7
2. Differences: -3, -2, -1, 0, 1, 2, 3
3. Squared differences: 9, 4, 1, 0, 1, 4, 9
4. Mean of squared differences: (9+4+1+0+1+4+9)/7 = 28/7 = 4
5. Standard deviation: √4 = 2

Both samples have the same standard deviation of 2.

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The left-hand limit and the right-hand limit both do not exist, the limit of sin(1/x) as x approaches 0 does not exist.

(i) To show that the limit of (1/x^2) as x approaches 0 does not exist, we need to show that the limit from the left-hand side and the right-hand side are not equal or they both go to infinity. Let's consider the right-hand limit:

lim x→0+ (1/x^2) = +∞ (the limit goes to infinity)

Now let's consider the left-hand limit:

lim x→0- (1/x^2) = +∞ (the limit goes to infinity)

Since the left-hand limit and the right-hand limit are both infinite and not equal, the limit does not exist.

(ii) To show that the limit of (1/√x^2) as x approaches 0 does not exist, we need to show that the limit from the left-hand side and the right-hand side are not equal or one or both of them goes to infinity. Let's consider the right-hand limit:

lim x→0+ (1/√x^2) = lim x→0+ (1/|x|) = +∞ (the limit goes to infinity)

Now let's consider the left-hand limit:

lim x→0- (1/√x^2) = lim x→0- (1/|x|) = -∞ (the limit goes to negative infinity)

Since the left-hand limit and the right-hand limit are not equal, the limit does not exist.

(iii) To show that the limit of (x+(x)) as x approaches 0 does not exist, we need to show that the limit from the left-hand side and the right-hand side are not equal or one or both of them goes to infinity. Let's consider the right-hand limit:

lim x→0+ (x+(x)) = 0+0 = 0

Now let's consider the left-hand limit:

lim x→0- (x+(x)) = 0+0 = 0

Since the left-hand limit and the right-hand limit are equal, the limit exists and equals 0.

(iv) To show that the limit of sin(1/x) as x approaches 0 does not exist, we need to show that the limit from the left-hand side and the right-hand side are not equal or one or both of them goes to infinity. Let's consider the right-hand limit:

lim x→0+ sin(1/x) does not exist

This is because sin(1/x) oscillates infinitely many times between -1 and 1 as x approaches 0 from the right-hand side, and the limit does not approach any single value.

Now let's consider the left-hand limit:

lim x→0- sin(1/x) does not exist

This is because sin(1/x) oscillates infinitely many times between -1 and 1 as x approaches 0 from the left-hand side, and the limit does not approach any single value.

Since the left-hand limit and the right-hand limit both do not exist, the limit of sin(1/x) as x approaches 0 does not exist.

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a)

The cost per payment is 155.5.

b)

The value of 3 in the standard deviation principle is 1376.711.

We have,

a)

To calculate E(X^ 105), we first need to find the probability distribution of X after applying the deductible of 105.

Any loss below 105 will result in no payment, and any loss above or equal to 105 will result in payment equal to the loss minus the deductible.

Thus, the probability distribution of the payments.

Payment: 0 0 0 29 29 75 75 75 75 105 245 419 419

Probability: 0 0 0 1/12 1/12 1/6 1/6 1/6 1/6 1/12 1/12 1/12 1/12

Using this probability distribution, we can calculate E(X^ 105) as follows:

E(X^ 105) = 0^2 * 0 + 29^2 * (1/12 + 1/12 + 1/12 + 1/12) + 75^2 * (1/6 + 1/6 + 1/6 + 1/6) + 105^2 * (1/12) + 245^2 * (1/12) + 419^2 * (1/12)

= 34390.5833

The cost per payment ex(105) is simply the expected payment per policyholder, which can be calculated as follows:

ex(105) = 29  (1/3) + 75 * (2/3) + 105 * (1/6) + 245 * (1/6) + 419 * (1/6)

= 155.5

b)

The standard deviation principle states that the total cost of claims, including the deductible, should be equal to the product of the standard deviation and the value of the insurance against risk.

In this case, the insurance against risk is the maximum amount that the insurance company is willing to pay per policyholder, which is 105. Thus, we have:

105 + Bo = s(X) x VaR

where s(X) is the standard deviation of X and VaR is the value at risk, which is the amount that the company expects to pay out with a certain probability (usually 99% or 99.5%).

We can solve for Bo as follows:

Bo = s(X) * VaR - 105

Assuming a VaR of 99%, we need to find the 1% percentile of X, which is the value x such that P(X ≤ x) = 0.01.

From the empirical distribution, we can see that the 1% percentile is 94. Thus, we have:

VaR = 105 - 94 = 11

To calculate s(X), we first need to find the mean of X, which is:

mean(X) = (94 + 3104 + 2134 + 4*180 + 210 + 350 + 524)/13 = 224

Using the formula for the sample standard deviation, we get:

s(X) = √((1/12)((94-224)^2 + 3(104-224)^2 + 2*(134-224)^2 + 4*(180-224)^2 + (210-224)^2 + (350-224)^2 + (524-224)^2))

= 142.701

Now,

Bo = 142.701 x 11 - 105

= 1376.711

Thus,

The cost per payment is 155.5.

The value of 3 in the standard deviation principle is 1376.711.

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The estimated values of a, b, and c are approximately 16.32, 0.9555, and 0.6417, respectively.

Solving for y3 using the method of successive approximation:

We start by setting up the first iteration, with h = dx = 0.1:

y1 = 1 (given)

y2 = y1 + h(x1 + y1) = 1 + 0.1(1+1) = 1.2

y3 = y2 + h(x2 + y2) = 1.2 + 0.1(2+1.2) = 1.44

y4 = y3 + h(x3 + y3) = 1.44 + 0.1(3+1.44) = 1.728

And so on.

After several iterations, the values converge to a particular value. In this case, y3 ≈ 1.6273.

Interpolating f(2.6) using Lagrange interpolation:

We have:

f(2.6) = L1(2.6)y1 + L2(2.6)y2 + L3(2.6)y3

where Li(x) = ∏j≠i (x-xj)/(xi-xj)

Evaluating Li(2.6) for i = 1, 2, 3:

L1(2.6) = (2.6-2)(2.6-3) / ((1-2)(1-3)) = 0.25

L2(2.6) = (2.6-1)(2.6-3) / ((2-1)(2-3)) = -0.5

L3(2.6) = (2.6-1)(2.6-2) / ((3-1)(3-2)) = 0.25

Substituting the given values:

f(2.6) ≈ 0.25(2.7183) - 0.5(7.3891) + 0.25(20.0855) ≈ 8.6082

Therefore, f(2.6) ≈ 8.6082.

Estimating the values of a, b, and c using multiple linear regression:

We can rewrite the model as a linear equation by taking the natural logarithm of both sides:

ln(y) = ln(a) + b ln(x) + c ln(e)

We can then use linear regression techniques to estimate the values of ln(a), b, and c. Using the given data and a statistical software, we obtain the following estimates:

ln(a) = 2.7912

b = 0.9555

c = -0.4462

To obtain estimates for a, b, and c themselves, we exponentiate the values of ln(a) and ln(e):

a ≈ e^2.7912 ≈ 16.32

b ≈ 0.9555

c ≈ 0.6417

Therefore, the estimated values of a, b, and c are approximately 16.32, 0.9555, and 0.6417, respectively.

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The domain of a relation is the set of all possible input values that can be used to calculate output values. In the case of the relation f(x) = x - 1, the domain is all real numbers because any real number can be substituted for x in the equation and an output value can be calculated. Therefore, the correct answer to the question is option a: {x|x + R}.

It is important to note that the domain of a relation can be restricted by certain conditions. For example, a square root function may have a domain of only non-negative numbers because taking the square root of a negative number is undefined. Additionally, some functions may have a limited domain due to practical or physical restrictions.

In summary, the domain of a relation is the set of all possible input values, and it is important to consider any restrictions that may apply. The domain of the relation f(x) = x - 1 is all real numbers, and the correct answer is option a: {x|x + R}.
The domain of the relation f(x) = x - 1 is a. {x|x ∈ R}.

In mathematics, a "domain" refers to the set of all possible input values (x-values) for which a given relation (a function or a rule that connects input values with output values) is defined. In this case, the relation is f(x) = x - 1.

Since the given relation is a simple linear function, it is defined for all real numbers (represented by R). There are no restrictions on the input values, as you can subtract 1 from any real number without causing any issues, such as division by zero or square roots of negative numbers.

Therefore, the correct answer is a. {x|x ∈ R}, which means "the set of all x such that x is an element of the set of real numbers." This domain includes all real numbers and can be represented as the entire x-axis on a graph.

In summary, for the relation f(x) = x - 1, the domain is all real numbers, which can be represented by the set notation {x|x ∈ R}.

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According to Pythagorean theorem, the length of BE is 2√(61) units.

To solve this problem, we need to use the Pythagorean Theorem, which tells us that in a right triangle, the square of the hypotenuse (the longest side) is equal to the sum of the squares of the other two sides. In this case, we can see that triangle ABE is a right triangle, with AB as the hypotenuse and BE and AE as the other two sides. Therefore, we can use the Pythagorean Theorem to find the length of BE.

To do this, we first need to find the length of AE. Since triangle ADE is a right triangle with a hypotenuse of length 4 and one leg of length 2, we can use the Pythagorean Theorem to find the length of the other leg, which is AE. Specifically, we have:

AE² + 2² = 4² AE² + 4 = 16 AE² = 12 AE = √(12) = 2√(3)

Now we can use the Pythagorean Theorem again to find the length of BE. Specifically, we have:

BE² + (2√(3))² = AB² BE² + 12 = (2AB)²

[since AB = AC = CD = DE = 4]

BE² + 12 = 16² BE² + 12 = 256 BE² = 244 BE = √(244) = 2√(61)

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The N electron configuration for nitrogen is 1s22s22p3.

To provide a complete electron configuration for nitrogen using the terms "electron" and "orbital," follow these steps:

1. Determine the atomic number of nitrogen (N). The atomic number of nitrogen is 7, meaning it has 7 electrons.
2. Fill the orbitals in order of increasing energy. The order is 1s, 2s, 2p, 3s, 3p, and so on.Following these steps, the electron configuration for nitrogen is 1s^22s^22p^3. This means there are 2 electrons in the 1s orbital, 2 electrons in the 2s orbital, and 3 electrons in the 2p orbital, which sums up to 7 electrons, corresponding to the atomic number of nitrogen.In writing the electron configuration for nitrogen, the first two electrons will go in the 1s orbital. Since 1s can only hold two electrons, the next two electrons for N go in the 2s orbital. The remaining three electrons will go into the 2p orbital.

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Answer: blue

Step-by-step explanation:

blue

Answer:

I’m pretty sure it’s 6.4 feet

Step-by-step explanation:

Based on the diagram, we can see that we have a right triangle with the ladder, the wall, and the distance up the wall that the ladder reaches.

We know that the ladder is 8 feet long and makes a 53 degree angle with the wall. We can use trigonometry to find the height that the ladder reaches up the wall.

The trigonometric function that relates the angle, the opposite side, and the hypotenuse in a right triangle is the sine function.

In this case, the height up the wall is the opposite side and the ladder is the hypotenuse.

To calculate this value, we first need to find the sine of 53 degrees. The sine function is a trigonometric function that relates the ratio of the length of the side opposite an angle to the length of the hypotenuse in a right triangle. In this case, we want to find the sine of the angle that the ladder makes with the wall, which is 53 degrees.

The sine of an angle is calculated by dividing the length of the side opposite the angle by the length of the hypotenuse. In this case, the length of the side opposite the angle is the height up the wall that the ladder reaches, and the length of the hypotenuse is the length of the ladder, which is 8 feet.

So, we can use the sine function to find the height up the wall as follows:

sin(53) = opposite/hypotenuse

sin(53) = opposite/8

To isolate the value of "opposite" on one side of the equation, we can multiply both sides by 8:

8 * sin(53) = opposite

Now, we can substitute the value of sin(53), which is approximately 0.8, into the equation:

opposite = 8 * sin(53)

opposite = 8 * 0.8

opposite ≈ 6.4 feet

Therefore, the distance up the wall that the ladder reaches is closest to 6.4 feet.

Scatterplot would  more appropriate for displaying and describing the relationship between the age and the number of vocabulary words.

We have to compare scatterplot and line graph.

The association between age and vocabulary size would be better represented and explained by a scatter plot.

The link between two continuous variables is shown using a scatter plot; in this instance, age is a continuous variable whereas the quantity of vocabulary items is a discrete variable.

The relationship between the two variables can be visually examined with the use of scatter plots, which also reveal the direction and strength of the association.

Thus, the scatterplot is best choice.

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Answer:

D. shapes with a right angle

Step-by-step explanation:

All shapes, parallelogram, irregular hexagon and the right triangle have or are capable of having a right angle. None of the other answers make sense either.

Hope this helps :)

Answer:

D. shapes with a right angle

Step-by-step explanation:

μA and μB represent the mean number of red blood cells in the blood of infected mice treated with Drug A and Drug B, respectively.

Null hypothesis (H0): Drug A and Drug B have the same effect on decreasing the number of internal parasites in a population of cats, μA = μB.

Alternative hypothesis (Ha): Drug A is better than Drug B at decreasing the number of internal parasites in a population of cats, μA < μB.

μA and μB represent the mean number of internal parasites in the population of cats treated with Drug A and Drug B, respectively.

Null hypothesis (H0): University of Maryland students and University of Denmark students have the same success rate at shooting freethrows, pMD = pDK.

Alternative hypothesis (Ha): University of Maryland students are better than University of Denmark students at successfully shooting freethrows, pMD > pDK.

pMD and pDK represent the proportion of successful freethrows for University of Maryland and University of Denmark students, respectively.

Null hypothesis (H0): There is no difference between Drug A and Drug B in the number of red blood cells in the blood of infected mice, μA = μB.

Alternative hypothesis (Ha): There is a difference between Drug A and Drug B in the number of red blood cells in the blood of infected mice, μA ≠ μB.

μA and μB represent the mean number of red blood cells in the blood of infected mice treated with Drug A and Drug B, respectively.

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A. Theoretical probability of rolling each value for both Diamond and Trevor is 16.67%.

The experimental probability of rolling each value for Diamond is 16.95% for one, 33.20% for two, and 49.84% for three.

The experimental probability of rolling each value for Trevor is 69.38% for one, 24.45% for two, and 6.17% for three. Based on the Law of Large Numbers, Diamond's dice can be assumed to be fair, but Trevor's dice cannot be assumed to be fair.

The theoretical probability of rolling each value for both Diamond and Trevor is 1/6 or 16.67%. To find the experimental probability for Diamond, we divide the number of times each value was rolled by the total number of rolls and multiply by 100%. For example, the experimental probability of rolling one is (217/1280) x 100% = 16.95%.

Based on the Law of Large Numbers, which states that the sample mean will approach the population mean as the sample size increases, we can reasonably assume that Diamond's dice is fair.

However, Trevor's dice cannot be assumed to be fair because the experimental probability of rolling each value is significantly different from the theoretical probability, indicating a potential bias in the dice.

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The circumference of the cookies is 10π which is approximately 31.4 inches.

A circle is simply a closed 2-dimensional curved shape with no corners or edges.

The circumference of a circle is expressed mathematically as;

C = 2πr

Where r is radius and π is constant pi ( π = 3.14 )

Given tha, the radius of the cookies is 5 inches.

So, we can substitute this value into the formula and calculate the circumference:

C = 2πr

C = 2 × 3.14 × 5

C = 31.4 in

Therefore, the circumference is approximately 31.4 inches.

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Kami will have $1,413.60 in the account after 4 years.

(1) Depositing $550 at 3% interest for one year generated a $16.50 profit for Abe.

(2) Jessi returned a $1,500 loan with a quarterly 3% rate and paid $1,511.25 in total.

(3) After keeping a deposit worth $418 at 2% for a year, Heath made an $8.36 profit. In 5.5 years, his account balance grew to $463.98.

(4) By depositing $825.50 at 4%, Pablo saw a $33.02 profit within a year.

(5) Kami put down $1,140 earning a $68.40 annual yield thanks to the 6% interest rate. Four years later, her account balance reached $1,413.60.

Interest = 1,140 * 0.06 * 4 = $273.60

Now, add the interest to the principal:

Total Amount = Principal + Interest = 1,140 + 273.60 = $1,413.60

Kami will have $1,413.60 in the account after 4 years.

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z1 = (78.1 - 83) / (7/√49) ≈ -1.49 and z2 = (85.2 - 83) / (7/√49) ≈ 0.85. Using a standard normal distribution table or calculator, we find that P(-1.49 < z < 0.85) ≈ 0.6924. Therefore, P(78.1 < x < 85.2) ≈ 0.6924.

(a) Since the sample size is large enough (n ≥ 30) and the population standard deviation is known, the central limit theorem can be applied to conclude that the sampling distribution of the sample mean, x, is approximately normal with mean µ = 83 and standard deviation σ/√n = 7/√49 = 1.

(b) To find P(x > 84.9), we need to standardize the value of 84.9 using the formula z = (x - µ) / (σ/√n). Thus, z = (84.9 - 83) / (7/√49) = 1.9. Using a standard normal distribution table or calculator, we find that P(z > 1.9) ≈ 0.0287. Therefore, P(x > 84.9) ≈ 0.0287.

(c) To find P(x ≤ 76.7), we again need to standardize the value of 76.7 using the formula z = (x - µ) / (σ/√n). Thus, z = (76.7 - 83) / (7/√49) = -1.86. Using a standard normal distribution table or calculator, we find that P(z < -1.86) ≈ 0.0317. Therefore, P(x ≤ 76.7) ≈ 0.0317.

(d) To find P(78.1 < x < 85.2), we first standardize the values of 78.1 and 85.2 using the formula z = (x - µ) / (σ/√n). Thus, z1 = (78.1 - 83) / (7/√49) ≈ -1.49 and z2 = (85.2 - 83) / (7/√49) ≈ 0.85. Using a standard normal distribution table or calculator, we find that P(-1.49 < z < 0.85) ≈ 0.6924. Therefore, P(78.1 < x < 85.2) ≈ 0.6924.

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The measure of the angle is <B is 110 degrees

It is important to note that supplementary angles are described as angles that sum up to 180 degrees.

From the information given, we have that;

m<A = x + 26

m>B = 2x + 22

Equate the angles

m<A +m<B = 180

x + 26 + 2x + 22 = 180

collect the like terms

3x = 180 - 48

3x = 132

x = 44

the measure of <B = 2x + 22 = 2(44) + 22 = 88 + 22 = 110 degrees

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The residuals for the sales data are 106, 95, 86, 96, 89, 89, 89, 93, 83, and 94.

Residuals represent the differences between the observed values and the predicted values of the dependent variable. In a regression analysis, the predicted values are estimated using the regression equation, while the observed values are the actual values of the dependent variable.

To compute the residuals in this case, we need to first use the estimated regression equation to predict the values of annual sales based on years of experience for each salesperson. The estimated regression equation is:

Annual Sales ($1,000s) = -30 + 4 x Years of Experience

Using this equation, we can predict the annual sales for each salesperson based on their years of experience. Then, we can subtract the predicted values from the actual values to obtain the residuals.

For example, for the first salesperson who has one year of experience and annual sales of $80, we can predict their annual sales using the regression equation as:

Annual Sales = -30 + 4 x 1 = -26

The residual for this salesperson is then:

Residual = $80 - (-26) = $106

We can repeat this process for each salesperson and obtain the following table:

Years of Experience Annual Sales ($1,000s) Predicted Annual Sales Residuals

1 80 -26 106

3 97 2 95

4 92 6 86

4 102 6 96

6 103 14 89

8 111 22 89

10 119 30 89

10 123 30 93

11 117 34 83

13 136 42 94

So the residuals for the sales data are 106, 95, 86, 96, 89, 89, 89, 93, 83, and 94.

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(a) The equation of the least-squares regression line that describes the relationship between the distance traveled in miles (x) and the pounds of fuel consumed (y) is given by: y = -4702.64 + 21.282x
(b) If the plot shows a random scatter, it indicates that the linear regression model is appropriate.
(c) The y-intercept in the context of the problem is -4702.64, which represents the predicted pounds of fuel consumed when the distance traveled is zero miles.

(a) The equation of the least-squares regression line for predicting the pounds of fuel consumed based on the distance traveled in miles is:

Fuel Consumed (lbs) = -4702.64 + 21.282 Distance Travelled (miles)

where Fuel Consumed and Distance Travelled are the variables used in the equation.

(b) Based on the residual plot, it is appropriate to use the linear regression equation to make predictions. The plot shows that the residuals are randomly scattered around the horizontal line at zero, indicating that there is no pattern or trend in the residuals. This suggests that the linear regression model is a good fit for the data and that the assumptions of linearity and constant variance are not violated.

(c) The y-intercept (-4702.64) represents the estimated pounds of fuel consumed when the distance traveled is zero. However, this value is not statistically meaningful in the context of the problem, as passenger aircraft cannot consume fuel if they do not travel any distance. Therefore, the y-intercept should not be interpreted in this case. The p-value for the intercept is 0.022, which is less than 0.05, indicating that the y-intercept is statistically significant. However, it may not be practically meaningful or interpretable in this context.

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