Tarbosaurus needs room to charge its prey.
The enclosure must be at least 300 ft x 70 ft.
The park can give you up to 800 feet of fencing.

The scientific notation of mass 6,200,000 is 6.2 × 10⁶kg and 6.2 × 10⁹g

Scientific notation is a way of expressing numbers that are too large or too small to be conveniently written in decimal form, since to do so would require writing out an unusually long string of digits.

It can be referred to as scientific form or standard index.

A mass of 6,200,00 kg can be written to index form by putting it to base of 10.

6200000/1000000

= 6.2 × 1000000 = 6.2 × 10⁶kg

1 kg = 10³ g

therefore;

6.2 × 10⁶kg = 6.2 × 10⁶kg × 10³

= 6.2 × 10⁹g

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If 2x²-5x+7 is subtracted from 4x²+2x-11, the coefficient of x in the result is 7

A coefficient is a number multiplied by a variable. For example, 6× x = 6x, here, 6 is the coefficient of x and x is the variable.

Subtracting 2x²-5x+7 from 4x²+2x-11

= 4x²+2x-11 -( 2x²-5x+7)

= 4x²+2x -11 - 2x²+5x-7

collecting like terms

4x²-2x²+2x+5x -7 -11

= 2x²+7x-18

Therefore the coefficient of x when 2x²-5x+7 is subtracted from 4x²+2x-11 is 7

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Yes, the data provided is enough evidence to show that the state had a higher proportion of identity theft than 23%.

To determine if the state had a higher proportion of identity theft complaints than the national average of 23%, we will perform a one-sample z-test for proportions at the 9% level of significance.

Step 1: State the null and alternative hypotheses.
H0: p = 0.23 (The proportion of identity theft complaints in the state is equal to the national average.)
H1: p > 0.23 (The proportion of identity theft complaints in the state is higher than the national average.)

Step 2: Determine the sample proportion and sample size.
Sample proportion (p-hat) = 468/1820 ≈ 0.2571
Sample size (n) = 1820

Step 3: Calculate the test statistic.
z = (p-hat - p) / √[(p * (1 - p)) / n]
z ≈ (0.2571 - 0.23) / √[(0.23 * (1 - 0.23)) / 1820] ≈ 1.88

Step 4: Find the critical value and make a decision.
At the 9% level of significance, the critical value (zα) for a one-tailed test is 1.34. Since our test statistic (z ≈ 1.88) is greater than the critical value (zα = 1.34), we reject the null hypothesis.

The data provide enough evidence to conclude that the state had a higher proportion of identity theft complaints than the national average of 23% at the 9% level of significance.

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The caculated value of the function x=-2 is 3

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

A linear graph

From the graph, we have the following readings at x = -2

(-2, 3)

There are several ways to interpret this and some of them are

Hence, the caculated value of the function x=-2 is 3

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The formula for the curvature of a smooth curve in three-dimensional space, parameterized by arc length, in terms of its unit tangent vector T(t) and unit tangent vector N(t), is given by: k = |dT/ds| = |dT/dt| / |dr/dt| where s is the arc length parameter and r(t) is the position vector of the curve.

While it is true that the magnitude of the rate of change of the unit tangent vector with respect to time, |dT/dt|, is related to the curvature, it is not equal to the curvature unless the curve is parameterized by arc length. If the curve is parameterized by some other parameter, such as time or a parameter that does not correspond to arc length, then the curvature formula will involve an additional factor related to the rate of change of the parameter with respect to arc length.

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The volume of the box is given as follows:

V = 199.5 in³.

The volume of a rectangular prism, with dimensions length, width and height, is given by the multiplication of these dimensions, according to the equation presented as follows:

Volume = length x width x height.

The dimensions for this problem, in inches, are given as follows:

10.5, 9.5 and 2.

Hence the volume of the box is given as follows:

V = 10.5 x 9.5 x 2

V = 199.5 in³.

The problem asks for the volume of the box.

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The correct answers are as follows: a) False, b) False, c) True, d) False, e) True, f) False

(a) False. Tossing a biased coin is not a Bernoulli trial as the probability of success (getting a head or tail) is not constant.
(b) False. The statement should be P(X>0) = 0.5.
(c) True. Negative binomial distribution describes the number of failures before a specified number of successes occur, while geometric distribution describes the number of trials until the first success.
(d) False. The expected waiting time is (30+0)/2 = 15.
(e) True. The standard deviation of the sample mean is equal to the population standard deviation divided by the square root of the sample size. So, 10/sqrt(100) = 1, and the standard deviation of the sample mean is 5/sqrt(100) = 0.5.
(f) False. Poisson distribution can be used to approximate binomial distribution under certain conditions such as large sample size and small probability of success.

(a) T: Tossing a biased coin can be treated as a Bernoulli trial, as it has two possible outcomes: success (head) and failure (tail).

(b) F: If X follows a standard normal distribution, then P(X=0) is not equal to 0. Instead, P(X=0) represents the probability density at X=0, which is non-zero.

(c) T: The negative binomial distribution is a generalization of Geometric distribution, as both distributions model the number of trials needed to achieve a certain number of successes.

(d) F: If the waiting time X of a bus follows a uniform distribution of X~U(0, 30), then the expected waiting time is (0+30)/2 = 15, not 30.

(e) T: A population follows a gamma distribution with a mean of 50 and standard deviation of 10. The standard deviation of the sample mean (sample size of 100) can be calculated as σ/√n = 10/√100 = 1, not 5.

(f) F: Poisson distribution can be used to approximate binomial distribution, especially when the number of trials (n) is large, and the probability of success (p) is small.

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The area of the shape which is made up of the semicircle and the rectangle  is 44.13

The area of the semicircle = area of circle  / 2

= πr²/2

= 9 x 3.14 / 2

= 14.13

The area of a reactangle can be defined as length x width

This will give us the area as

6 x 5

Area = 30 cm for the rectangle

The exact area of the shape will be Total Area = (Area of Semicircle) + (Area of Rectangle)

= 30 cm + 14.13

= 44.13 cm

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The length of the wire to the nearest hundredth is 201.06 cm

The wire is bent to form four semicircles, each with a diameter of 32 cm.

The circumference of a semicircle is half of the circumference of a full circle, so the circumference of each semicircle is:

C = πd/2

= π(32 cm)/2

= 16π cm

The total length of wire is four times the circumference of each semicircle:

L = 4C

= 4(16π cm)

= 64π cm

To find the length of the wire to the nearest hundredth, we can use the  π = 3.14:

L = 64(3.14) cm

= 201.06 cm

Therefore, the length of the wire to the nearest hundredth is 201.06 cm

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D. Identifying outliers in a data set can be helpful in uncovering data errors is true regarding data errors

It states that identifying outliers in a data set can be helpful in uncovering data errors. Data errors can occur due to various reasons such as data entry mistakes, data transmission errors, data processing errors, and so on. These errors can lead to incorrect or misleading analysis, which can affect decision-making processes.

Identifying outliers in a data set can help in identifying potential data errors. Outliers are observations that are significantly different from other observations in a data set. Outliers can occur due to various reasons such as measurement errors, sampling errors, or simply due to natural variation in the data. However, outliers can also indicate data errors that need to be corrected.

For example, if a data set contains information about the heights of a group of people, and one observation reports a height of 9 feet, it is likely that this is a data entry error. Identifying this outlier can help in identifying and correcting the error.

Therefore, it is important to identify outliers and investigate them to determine if they are genuine observations or data errors. Removing outliers blindly can lead to the loss of valuable information and can also introduce bias into the analysis. It is important to use statistical methods to identify outliers and investigate them carefully to ensure that the data is accurate and reliable.

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The measure of the shorter base is approximately 28.85.

To solve this problem, we need to use the fact that the line segment joining the midpoints of the diagonals of a trapezoid is parallel to the bases and has a length equal to half the sum of the bases. Let's call the shorter base "x".

We know that the longer base is 97, so the sum of the bases is x + 97.

We also know that the line segment joining the midpoints of the diagonals has a length of 3. Since this line segment is parallel to the bases, it divides the trapezoid into two smaller trapezoids that are similar to the original trapezoid.

Using the similar triangles, we can set up the following equation:

3/x = (x + 97)/97

Cross-multiplying and simplifying, we get:

3*97 = x^2 + 97x

Multiplying out the right side and rearranging, we get:

x^2 + 97x - 291 = 0

Now we can use the quadratic formula to solve for x:

x = (-b ± sqrt(b^2 - 4ac))/2a

Plugging in a=1, b=97, and c=-291, we get:

x = (-97 ± sqrt(97^2 - 4(1)(-291)))/2(1)

x = (-97 ± sqrt(9429))/2

x = (-97 ± 97)/2 or x = (-97 ± sqrt(9429))/2

Since we're looking for the shorter base, we can discard the negative solution:

x = (-97 + sqrt(9429))/2

x ≈ 28.85

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Answer:

look the picture for the representation

thank you

Based on the given information, we know that the medium-sized jet has a fuselage diameter of 3.8 meters and a loaded mass of 85,000 kilograms. The drag on the airplane is primarily due to the cylindrical shape of the fuselage, and its aerodynamic shaping gives it a drag coefficient of 0.37.

To calculate the drag force on the airplane, we can use the formula:

Drag Force = 1/2 x Density x Velocity^2 x Surface Area x Drag Coefficient

The surface area of a cylinder is given by:

Surface Area = 2 x π x (diameter/2) x length

Assuming a length of 30 meters for the fuselage, we can calculate the surface area as:

Surface Area = 2 x π x (3.8/2) x 30 = 426.43 square meters

Using the given drag coefficient of 0.37 and assuming a cruising speed of 800 km/h (or 222.22 m/s), we can calculate the drag force as:

Drag Force = 1/2 x 1.225 kg/m^3 x (222.22 m/s)^2 x 426.43 m^2 x 0.37
Drag Force = 5,641,613 Newtons

Therefore, the drag force on the medium-sized jet is approximately 5.64 million Newtons. This drag force must be overcome by the jet's engines to maintain its cruising speed.

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Using distributive property, the simplification of the expression shows that the given expression is already simplified.

To simplify expressions first expand any brackets, next multiply or divide any terms and use the laws of indices if necessary, then collect like terms by adding or subtracting and finally rewrite the expression.

t - 2 / v - 3

Let's combine the numerator with the denominator

(t - 2)(v - 3) / (v - 3)

Expand the expression using distributive property

tv - 3t - 2v + 6 / (v - 3)

We can factor as;

(t - 2)(v - 3) / (v - 3)

Cancel both sides

(t - 2) / (v - 3)

The expression has already been simplified

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To minimize the cost of the package, we need to find the dimensions that minimize the cost function.

The cost function is the sum of the cost of the side and bottom (made of syrofoam) and the cost of the top (made of paper). Let r be the radius and h be the height of the cylinder. Then the cost function is:

C(r, h) = 0.02(2πrh + πr^2) + 0.05(πr^2)

We need to find the values of r and h that minimize this function subject to the constraint that the volume of the cylinder is 600 cubic centimeters. That is:

V = πr^2h = 600

We can solve for h in terms of r from the volume equation:

h = 600/(πr^2)

Substituting this expression for h in the cost function, we get:

C(r) = 0.02(2πr(600/(πr^2)) + πr^2) + 0.05(πr^2)

= 0.04(600/r) + 0.05πr^2

To minimize C(r), we take the derivative with respect to r and set it equal to zero:

dC/dr = -0.04(600/r^2) + 0.1πr = 0

Solving for r, we get:

r = (300/π)^(1/3) ≈ 5.17 cm

Substituting this value of r into the volume equation, we get:

h = 600/(πr^2) ≈ 2.17 cm

Therefore, the dimensions of the cylinder that minimize the production cost are r ≈ 5.17 cm and h ≈ 2.17 cm, and the minimum cost is:

C(r, h) ≈ $1.24

So, the minimum cost of producing a microwaveable cup-of-soup package in the shape of a cylinder with a volume of 600 cubic centimeters is about $1.24, with a radius of about 5.17 cm and a height of about 2.17 cm.

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To sketch and describe the locus of points in a plane in the interior of a right triangle with sides of 6 in., 8 in., and 10 in. and at a distance of 1 in. from the triangle, we need to first understand what a locus of points is.

A locus of points refers to the set of points that satisfy a given condition.
In this case, the given condition is that the points must be located at a distance of 1 in. from the right triangle with sides of 6 in., 8 in., and 10 in. To visualize this, we can imagine a circle with a radius of 1 in. drawn around each of the three vertices of the triangle.
The locus of points that we are interested in is the region that is enclosed by these three circles. This is because any point that is located within all three circles is at a distance of 1 in. from each of the three sides of the right triangle.
We can see that this region takes the shape of a smaller triangle that is located in the interior of the original right triangle. This smaller triangle has sides that are each 2 in. shorter than the corresponding sides of the original triangle.
To summarize, the locus of points in a plane in the interior of a right triangle with sides of 6 in., 8 in., and 10 in. and at a distance of 1 in. from the triangle is a smaller triangle that is located in the interior of the original triangle. This smaller triangle has sides that are each 2 in. shorter than the corresponding sides of the original triangle.

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Based on the given information, we can conclude that f(2) is an analytic function in a deleted neighborhood of infinity. This means that f(z) has a Laurent expansion in the form of

[tex]f(z) = ..._c-2/z^2 + _c-1/z + c0 + c1z + ... + cnz^n + ...,[/tex]

where the coefficients

[tex]_c-2, _c-1, c0, c1, ...,[/tex]

cn are constants.

The point morc is a singular point of f(z) that can be either removable, a pole of order m, or an essential singular point. The type of singular point depends on the behavior of the Laurent expansion of f(z).

If the Laurent expansion of f(z) contains no positive powers of z, then the point morc is a removable singular point. This means that the singularity can be "filled in" or removed, and the function can be defined at that point.

If the Laurent expansion of f(z) contains only a finite number of positive powers of z, with the highest positive power being m, then the point morc is a pole of order m. This means that the singularity is a simple pole, double pole, triple pole, or higher order pole, depending on the value of m.

If the Laurent expansion of f(z) contains infinitely many positive powers of z, then the point morc is an essential singular point. This means that the singularity cannot be removed or "filled in", and the behavior of the function at that point is very complex.

In summary, the type of singular point at the point morc depends on the behavior of the Laurent expansion of f(z) at that point.

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The maximum value of the objective function within the feasible region occurs at one of the vertices.

To find the maximum value of the objective function within the feasible region, we need to evaluate the objective function at each of the vertices and determine which one gives the highest value.

Let's assume the objective function is of the form f(x,y). To evaluate f at each vertex, we substitute the x and y values into the function. For example, at the vertex (2,0), we evaluate f(2,0) = 2x + 3y, where x=2 and y=0. Similarly, we evaluate f at each of the other vertices as follows

At (10,1): f(10,1) = 2x + 3y = 2(10) + 3(1) = 23

At (8,5): f(8,5) = 2x + 3y = 2(8) + 3(5) = 31

At (0,4): f(0,4) = 2x + 3y = 2(0) + 3(4) = 12

So the maximum value of the objective function f within the feasible region is 31, which occurs at the vertex (8,5).

Note that the feasible region is defined by the set of constraints that limit the values of x and y that satisfy the problem. The vertices of the feasible region are the points where the boundary of the feasible region intersect. The maximum value of the objective function within the feasible region occurs at one of the vertices.

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To create the vector OutOfSpec, we need to compare the values in SqOut with the ideal output values, which can be calculated using the formula (input voltage)^2. We can then use the following steps in Matlab:

1. Create a vector of input voltages from 1 mV to N mV:
inputVoltage = 1:N;

2. Calculate the ideal output values using the formula (input voltage)^2:
idealOutput = inputVoltage.^2;

3. Calculate the percentage difference between the actual and ideal output values:
percentDiff = abs(SqOut - idealOutput) ./ idealOutput * 100;

4. Find the indices of the values in percentDiff that exceed 1%:
outOfSpecIdx = find(percentDiff > 1);

5. Use the outOfSpecIdx vector to extract the input voltages that generated out-of-spec output values:
OutOfSpec = inputVoltage(outOfSpecIdx);

The resulting vector OutOfSpec will contain a list of all inputs that generated an output differing from the ideal value by more than 1%.
To create a vector OutOfSpec in Matlab that contains a list of all inputs that generated an output differing from the ideal value by more than 1%, you can use the following code:

```matlab
SqOut = [0.9985 4.052 8.973 15.81 25.15];
N = length(SqOut);
ideal_output = (1:N).^2;
tolerance = 0.01 * ideal_output;
lower_bound = ideal_output - tolerance;
upper_bound = ideal_output + tolerance;

OutOfSpec = find(SqOut < lower_bound | SqOut > upper_bound);
```

This code first calculates the ideal output values and the 1% tolerance bounds. Then, it uses the 'find' function to identify input values where the corresponding output differs from the ideal value by more than 1%. The result will be stored in the vector OutOfSpec.

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Joule per meter second is the unit of measurement for momentum flux or power per unit area. It is commonly used in physics and engineering to quantify the rate of energy transfer or momentum flow per unit area.

Joule per meter second (J/m^2s) is not the correct unit for momentum flux or power per unit area. The correct unit for momentum flux is Newton per square meter (N/m^2), also known as Pascal (Pa), while the correct unit for power per unit area is watt per square meter (W/m^2). The joule per meter second (J/m^2s) is actually the unit for volumetric energy dissipation rate, which measures the rate at which energy is being dissipated within a fluid volume per unit volume. It is used in the study of fluid dynamics and turbulence.

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Answer:

The answer for x is 20

Step-by-step explanation:

a=1

b=10

<C=60°

cos 60=b/hyp

let hyp be x

cos 60=10/x

x=10/cos 60

x=10/0.5

x=20

The given fraction, 6/2 can be written as a multiple of units fraction, which is calculated out to be is 3/1.

When we write a fraction as a multiple of units fraction, we express it in the form of a fraction whose numerator is a whole number and denominator is 1.

To write 6/2 as a multiple of units fraction, we need to find a fraction which is equivalent to 6/2, but with a denominator of 1.

To do this, we can simplify the fraction 6/2 by dividing the numerator and denominator by their greatest common factor, which is 2.

So, 6/2 = (6 ÷ 2)/(2 ÷ 2) = 3/1

Here, we have divided both numerator and denominator by 2, which gives us an equivalent fraction of 3/1.

Therefore, 6/2 as a multiple of units fraction is 3/1.

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Answer: logarithmic

Step-by-step explanation:

edge2023 ;)

the second one is ratio

Ax=b has a solution for infinitely many b in R3, but Ax=b does not have a solution for all b in R3.

Consider the matrix A:

```
A = [1 2 3;
    4 5 6;
    7 8 9]
```

To find the solutions of Ax=b, we need to solve the system of linear equations:

```
x1 + 2x2 + 3x3 = b1
4x1 + 5x2 + 6x3 = b2
7x1 + 8x2 + 9x3 = b3
```

We can rewrite this system as:

```
x1 + 2x2 + 3x3 - b1 = 0
4x1 + 5x2 + 6x3 - b2 = 0
7x1 + 8x2 + 9x3 - b3 = 0
```

This is an homogeneous system of linear equations, and we can solve it using Gaussian elimination. We find that the rank of A is 2, since the third row is a linear combination of the first two rows. Therefore, the system has either one or infinitely many solutions.

If we solve for x1, x2, and x3 in terms of b1, b2, and b3 using Gaussian elimination, we get:

```
x1 = -b1 + 2b2 - b3
x2 = b1 - b2
x3 = (1/3)b1 - (2/3)b2 + (1/3)b3
```

These expressions show that the solution of Ax=b depends on the values of b1, b2, and b3. If we choose b1 = 1, b2 = 0, and b3 = 0, then we find that Ax=b has a solution. Similarly, if we choose b1 = 0, b2 = 1, and b3 = 0, then we find that Ax=b has a solution. In fact, for any values of b1, b2, and b3 such that b1 - b2 + b3 = 0, the system Ax=b has a solution.

However, if we choose b1 = 1, b2 = 1, and b3 = 1, then we find that Ax=b does not have a solution, since the equation b1 - b2 + b3 = 1 - 1 + 1 = 1 is not satisfied. Therefore, Ax=b has a solution for infinitely many b in R3, but Ax=b does not have a solution for all b in R3.

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The category in the Excel Options dialog box that contains the option to change the user name is "General".

In the Excel Options dialog box, the category that contains the option to change the user name is the General category.

Excel graphing methods, Go to Insert > Line after selecting the data. The type of line chart you want may be chosen from a dropdown menu that appears when you click the icon.

We'll use the fourth 2-D line graph (Line with Markers) for this illustration. Your line graph for the chosen data series will be added by Excel.

What are the primary three graphs?

How to Use Graphs in Science

Bar, circle, and line graphs are the three most often utilized graph kinds. Each form of graph may be used to display a certain kind of data.

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