find the length of the missing sides in the triangle. use 45 -45 -90 triangle theorem. write your answers as integers, in radical form, or as decimals rounded to the nearest tenth. The diagram is not drawn to scale

The pros and cons of Kernel Density Estimation (KDE) over histogram has been compared and advantage and disadvantage to each has been given.

Kernel Density Estimation (KDE) and histograms are both methods used for visualizing and estimating probability density functions.

KDE offers advantages such as its ability to capture smooth and continuous distributions, while histograms have benefits like simplicity and ease of interpretation.

However, KDE can be computationally intensive, and histograms may suffer from binning bias.

Kernel Density Estimation (KDE) is a non-parametric method used to estimate the probability density function of a random variable. It offers several advantages over histograms.

Firstly, KDE is able to capture smooth and continuous distributions, providing a more accurate representation of the underlying data.

Additionally, KDE does not rely on binning, allowing for a more precise estimation of the density at any point.

Furthermore, KDE can handle missing or irregularly spaced data.

On the other hand, histograms have their own advantages. They are simple and intuitive, making them easy to understand and interpret.

Histograms also tend to be less computationally intensive compared to KDE, making them suitable for large datasets.

Moreover, histograms can reveal the presence of outliers or gaps in the data due to the discrete nature of the bins.

However, there are also drawbacks to consider. KDE can be computationally intensive, especially for large datasets, requiring more processing power and time compared to histograms.

This can be a limitation when dealing with real-time or interactive applications. In contrast, histograms suffer from binning bias, where the choice of bin size can affect the visual representation and interpretation of the data.

Selecting an inappropriate bin size can lead to either over-smoothing or over-detailing the data distribution.

In summary, KDE offers advantages in capturing smooth distributions and avoiding binning bias, but it may be computationally intensive.

Histograms, on the other hand, are simple and computationally efficient but can suffer from binning bias.

The choice between KDE and histograms depends on the specific characteristics of the data and the objectives of the analysis.

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The  volume of the largest right circular cone that can be inscribed in a sphere of radius r is  8/27 (volume of sphere)

Let r serve as the foundation. the volume of the area, and radius x is the separation between the base and the sphere's center. Height h of the cone = R + x

∴    V= 1/3πr² h= π/ 3 (R² −x²)(R + x)

       = π/3​ (R² + R² x − Rx² −x² )

∴   dV/ dx​ = π​ /3 [R²−2Rx−3x² ]

  d²V/ dx²​ = π/3​ [−2R−6x]

For max or min V dV/dx​ =0

∴   R² −2Rx−3x² =0

⇒(R + x)(x−3x)=0    

2) x=−R, x​/3 but x = −R

When x= R/3 d² V/dx² <0  V is max only when x= R/3

​∴   Max V= 1/3π(R² − R²/9 )(R+ R/3 )

= 32πR³/81

= 8/27 ( 4/3 πR³)

= 8/27 (volume of sphere)

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Given that there is only one digit left and the sum is more than 10, the third digit must be 7, which it is. and the pass code to open anything it is guarding is 5 5 7, which may be inputted.

To decode the code, we know that the number is more than 10 and has three digits: . Moreover, we are aware that the value of the ones digit is 10 times that of the tenths digit. The code is 5 5 7, as may be inferred from the provided hints.

Given that the code contains two 5s, the first digit must be 5. As the value of the ones digit is 10 times that of the tenths number, the second digit must likewise be 5, which is 5. The number is bigger than 10, hence the third digit must be 7, as it is the only one still present.

In order to open whatever it is guarding, the code, which is 5 5 7, may be input.

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A = P(1 + r/n)^(nt)

Where:

A = the amount of money after t years

P = the principal amount (in this case, $4000)

r = the annual interest rate (as a decimal)

n = the number of times the interest is compounded per year (in this case, twice a year, so n = 2)

t = the number of years

Plugging in the given values, we get:

A = 4000(1 + 0.02/2)^(2t)

Simplifying, we get:

A = 4000(1.01)^2t

Therefore, the function that models the situation is:

A = 4000(1.01)^(2t)

Answer:

C

Step-by-step explanation:

Answer:

102 beads will be used for each necklace.

Step-by-step explanation:

3,264/32=102

Answer:

D. Horizontally compressed by a factor of 1/3

Answer:

$4.50

Step-by-step explanation:

18 divided by 4

1) The line integral value is : ∫F dr = 6

2) The line integral value is : ∫F dr = 6

3) The line integral value is : ∫F dr = 6

Here we are given:

\(F.dr = (3x + 2y)dx + (2x -2z)dy + (3x -2y)dz,\)

where\(\vec{F}\) is a conservative field

So,

f(x, y , z) = \(\int\limits (3x + 2y)dx + (2x -2z)dy + (3x -2y)dz,\)

f(x , y , z) = (3zx +2yx) + (2xy - 2zy) + (3xz - 2yz) + c(x , y , z)

\(f(x, y, z) = (6xz + 4xy - 4yz) + c(x, y, z)\)

Now substitute the values of x , y ,z ,

1)

Line segment from (0, 0 , 0) to (1,1 ,1)

∫F dr = f(1,1,1) - f(0,0,0)

= |6 + 4 - 4 |- |0 + 0 - 0|

∫F dr = 6

2)

Line segment from (0,0,0) to (0,0,1) to (1,1,1)

First we take (0,0,0) to (0,0,1)

\(\int _c \vec{F}.\vec{dr}=f(0,0,1)-f(0,0,0) =[6(0)(1)+4(0)(0)-4(0)(1)]-[6(0)(0)+4(0)(0)-4(0)(0)] =[0+0-0]-[0+0-0]\)

∫F dr = 0

\(\Rightarrow \int _{c}\vec{F}.\vec{dr}=0+6 [F.dr = 6\)

3)

Line segment from (0,0,0) to (1,0,0) to (1,1,0) to (1,1,1)

First we take (0,0,0) to (1,0,0)

\(\int _c \vec{F}.\vec{dr}=f(1,0,0)-f(0,0,0) =[6(1)(0)+4(1)(0)-4(0)(0)]-[6(0)(0)+4(0)(0)-4(0)(0)] =[0+0-0]-[0+0-0]\int _{c}\vec{F}.\vec{dr}=0\)

Next we take (1,0,0) to (1,1,0)

\(\int _c \vec{F}.\vec{dr}=f(1,1,0)-f(1,0,0) = [6(1)(0) + 4(1)(1) − 4(1)(0)] - [6(1)(0) + 4(1)(0) — 4(0)(0)] = [0+4-0] - [0+0-0]\int _{c}\vec{F}.\vec{dr}=4\)

Lastly we take (1,1,0) to (1,1,1)

\(F.dr = f(1,1,1) ƒ(1,1,0) = [6(1)(1) +4(1)(1) − 4(1)(1)] - [6(1)(0) + 4(1)(1) — 4(1)(0)] =[6+4-4]-[0+4-0]\int _{c}\vec{F}.\vec{dr}=2\)

Adding the three results we get

\(\Rightarrow \int _{c}\vec{F}.\vec{dr}=0+4+2\\\\\int\limits F.dr = 6\)

Therefore we see that the Line integral for the three cases comes out to be same between the initial and final points since it is independent of the path taken.

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Therefore, the probability that the average daily revenue of the sample is between $2450 and $2460 is approximately 0.0443 or 4.43%.

The distribution of the sample means of size n = 100 from a population with mean μ = $2500 and standard deviation σ = $300 can be approximated by a normal distribution with mean = μ = $2500 and standard deviation = σ/√n = $300/√100 = $30.

Thus, we need to find the probability that the sample mean falls between $2450 and $2460.

Z-score for $2450:

z = (2450 - 2500) / 30 = -1.67

Z-score for $2460:

z = (2460 - 2500) / 30 = -1.33

Using a standard normal distribution table or calculator, we can find the probabilities associated with these z-scores:

P(z < -1.67) = 0.0475

P(z < -1.33) = 0.0918

Therefore, the probability that the average daily revenue of the sample is between $2450 and $2460 is:

P(-1.67 < z < -1.33) = P(z < -1.33) - P(z < -1.67)

= 0.0918 - 0.0475

= 0.0443

So, the probability is approximately 0.0443 or 4.43%.

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In order to calculate the expected number of people who will get sick in each group, if the first option is chosen, we are given that 500,000 people will be vaccinated at random and we need to calculate the expected number of sick people in group 1 in cell b10.

To calculate the expected number of sick people in group 1, we can use the following formula:Expected number of sick people in group

\(1 = (Number of people in group 1 / Total number of people) x Number of people who get sick= (200,000/500,000) x 20,000= 8,000\)

Therefore, the expected number of sick people in group 1 is 8,000.In order to calculate the expected number of sick people in group 2,

we can use the following formula:Expected number of sick people in group

\(2 = (Number of people in group 2 / Total number of people) x Number of people who get sick= (300,000/500,000) x 20,000= 12,000\)

Therefore, the expected number of sick people in group 2 is 12,000.To calculate the expected total number of sick people, we can simply add the expected number of sick people in group 1 and group 2:

\(Expected total number of sick people = Expected number of sick people in group 1 + Expected number of sick people in group 2= 8,000 + 12,000= 20,000\)

Therefore, the expected total number of sick people is 20,000.

Thus, we have calculated the expected number of people who will get sick in each group and the expected total number of sick people if the first option is chosen.

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Given that sin 30 degree = 1/2 and cos 30 degree = Squareroot 3/2the value of cot(30°) is  = Squareroot 3

Sine 30°=1/2

Cos 30° = √3/2

Sin 30 degrees has a value of 0.5. Sin 30 can also be expressed in radians as sin /6. The triangle's angles and side length are related by the trigonometric function, often known as an angle function.

The sine function, cosine function, and tangent function are the three trigonometric ratios that are the most well-known.

The ratio of the adjacent side to the hypotenuse is referred to as the cosine function. If the right triangle's angle is 30 degrees, then the cosine value at this angle, or the value of Cos 30 degrees, is expressed as 3/2.

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Answer:

3

Step-by-step explanation:

Answer:

I am not sure but I think its is $1.13

There are 12 cups inside a 3-quart container

Step-by-step explanation:

To prove the given statement: n ∏[j=1 to k] (j + 1)(j + 2) · · · (j + k − 1) = n(n + 1)(n + 2) · · · (n + k)/(k + 1), we can use mathematical induction.

For k = 1, the left-hand side (LHS) and right-hand side (RHS) of the equation become:

LHS: n ∏[j=1 to 1] (j + 1) = n(1 + 1) = 2n

RHS: n(n + 1)/(1 + 1) = n(n + 1)/2

The LHS and RHS are equal for k = 1, so the equation holds for the base case.

Assume that the equation holds for k = m, where m is a positive integer.

We need to show that the equation also holds for k = m + 1.

LHS: n ∏[j=1 to m+1] (j + 1)(j + 2) · · · (j + m) = n ∏[j=1 to m] (j + 1)(j + 2) · · · (j + m) * (m + 1)(m + 2) · · · (m + m) (1)

RHS: n(n + 1)(n + 2) · · · (n + m+1)/(m + 1 + 1) = n(n + 1)(n + 2) · · · (n + m+1)/(m + 2)

Using the inductive hypothesis, we can rewrite the LHS of equation (1) as:

LHS = (n(n + 1)(n + 2) · · · (n + m)/(m + 1)) * (m + 1)(m + 2) · · · (m + m)

Notice that the first part of the LHS matches the RHS. Therefore, we have:

LHS = RHS = (n(n + 1)(n + 2) · · · (n + m)/(m + 1)) * (m + 1)(m + 2) · · · (m + m)

Simplifying, we get:

LHS = RHS = n(n + 1)(n + 2) · · · (n + m)(m + 1)(m + 2) · · · (m + m)/(m + 1)

We can cancel out (m + 1) terms from the numerator and denominator:

LHS = RHS = n(n + 1)(n + 2) · · · (n + m) * (m + 2) · · · (m + m) = n(n + 1)(n + 2) · · · (n + m+1)/(m + 2)

And finally, dividing both sides by (m + 2), we have:

LHS = RHS = n(n + 1)(n + 2) · · · (n + m+1)/(m + 2)

This completes the inductive step.

Since the equation holds for the base case and the inductive step, we can conclude that the statement is true for all positive integers k and n by mathematical induction.

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Answer: 3/10

Step-by-step explanation: times

Answer:

18

Step-by-step explanation:

\(\frac{1}{5}\) × 60 = 12 ← number who chose basketball

\(\frac{1}{2}\) × 60 = 30 ← number who chose volleyball

That is a total of 12 + 30 = 42 students

Since the remaining students chose soccer, then

60 - 42 = 18 ← number who chose soccer

Answer:

(4x^{(6)})^{(\frac{(3)}{(2)})}

(4x(6))((2)(3))

Step-by-step explanation:

I think that's the answer

Answer:

x=5

h = 5 sqrt 2

Step-by-step explanation:

isosceles right triangles are always 45-45-90, ratio of those triangles are always x:x:x*sqrt 2

The standard deviation of the sampling distribution of X, the number of people in an SRS of size 50 that would vote for the incumbent governor, is approximately 3.52. This means that if many samples of size 50 were taken from the population, the number of people who would respond that they would vote for the incumbent would typically vary by about 3.52 from the mean of 23.

The standard deviation of the sampling distribution of X can be calculated using the formula \(\sqrt{p(1-p)/n}\), where p is the proportion of the population that would vote for the incumbent (0.46 in this case) and n is the sample size (50 in this case). Plugging in these values, we get sqrt(0.46(1-0.46)/50) ≈ 0.0715.

The standard deviation represents the average amount of variation or spread we would expect to see in the sampling distribution of X. In this case, it tells us that if we were to take many samples of size 50 from the population, the number of people who would respond that they would vote for the incumbent would typically vary by about 3.52 (0.0715 multiplied by the square root of 50) from the mean of 23 (0.46 multiplied by 50).

Therefore, the correct statement is: If you were to take many samples of size 50 from the population, the number of people who would respond that they would vote for the incumbent would typically vary by about 3.52 from the mean of 23.

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Step-by-step explanation:

First you divide 529 and 23. You can divide 5w9 by 23 in a fraction like this 529/23 on to Desmos scientific calculator or just 529÷23 on a calculator. If it is a decimal, round it up if the tenth, hundredth, or thousandth is 5, 6, 7, 8, 9 or round down if the number is 4, 3, 2, and 1. The answer to 529÷23 is 23. And if you estimate it, it'll be 20. The next part may be a bit confusing but if you estimate 23, you'll get 25.

Then you estimate 529 and 23, you'll get 530 and 20. And if you divide them, you'll get 26.5. And if you estimate that, you'll get 27. So that means is 2 or 7 off from the estimated from the equation 529÷23. So that means the reasonable answer is probably 25 or 27.

Tell me if I'm right or not, and I'm sorry if I'm wrong because I don't know what answer I explained in the two paragraphs I wrote is right or wrong.

Step-by-step explanation:

100% = 33,000 ft

1% = 100%/100 = 33000/100 = 330 ft

6% = 1% × 6 = 330 × 6 = 1980 ft

that means the plane has to go down 1980 ft to

33,000 - 1980 = 31,020 ft

and then it goes up by 5000 ft :

31,020 + 5000 = 36,020 ft

that is the final altitude.

Answer:

The final altitude of the airplane is 36,020

Step-by-step explanation:

I took the test

To find the selling price that will yield the maximum profit, we need to find the vertex of the quadratic function given by the profit equation y = -5x² + 286x - 2275.The x-coordinate of the vertex can be found using the formula:

x = -b/2a

where a = -5 and b = 286.

x = -b/2a

x = -286/(2(-5))

x = 28.6

So, the selling price that will yield the maximum profit is $28.60 (rounded to the nearest cent).

Therefore, the widgets should be sold for $28.60 to maximize the company's profit.

Hope I helped ya...

Answer:

29 cents

Step-by-step explanation:

The amount of profit, y, made by the company selling widgets, is related to the selling price of each widget, x, by the given equation:

\(y=-5x^2+286x-2275\)

The maximum profit is the y-value of the vertex of the given quadratic equation. Therefore, to find the price of the widgets that maximises profit, we need to find the x-value of the vertex.

The formula to find the x-value of the vertex of a quadratic equation in the form y = ax² + bx + c is:

\(\boxed{x_{\sf vertex}=\dfrac{-b}{2a}}\)

For the given equation, a = -5 and b = 286.

Substitute these into the formula:

\(\implies x_{\sf vertex}=\dfrac{-286}{2(-5)}\)

\(\implies x_{\sf vertex}=\dfrac{-286}{-10}\)

\(\implies x_{\sf vertex}=\dfrac{286}{10}\)

\(\implies x_{\sf vertex}=28.6\)

Assuming the value of x is in cents, the widget should be sold for 29 cents (to the nearest cent) to maximise profit.

Note: The question does not stipulate if the value of x is in cents or dollars. If the value of x is in dollars, the price of the widget should be $28.60 to the nearest cent.

If the price elasticity of demand is equal to 0.02/0.1, then the demand for apples is inelastic and if the price elasticity of demand is equal to 0.2/5, then the demand for apples is elastic.

The price elasticity of demand is calculated using the midpoint method with formula:

Price Elasticity of Demand = (Percent Change in Quantity Demanded) / (Percent Change in Price)

The value of the price elasticity of demand tells us whether the demand for a product is elastic (sensitive to price changes), inelastic (insensitive to price changes), or unitary elastic (neither elastic nor inelastic). A value of price elasticity of demand greater than 1 indicates that the demand for the product is elastic, meaning that a small change in price leads to a large change in the quantity demanded. A value of price elasticity of demand less than 1 indicates that the demand for the product is inelastic, meaning that a small change in price leads to a small change in the quantity demanded.

If the price elasticity of demand is equal to 0.02/0.1, then the demand for apples is inelastic between points X and Y, meaning that a small change in price leads to a small change in the quantity demanded. If the price elasticity of demand is equal to 0.2/5, then the demand for apples is elastic between points X and Y, meaning that a small change in price leads to a large change in the quantity demanded.

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Answer:

that seems very hard i dont think there is a inverse maybe there is but it dont seem like it