at the ski shack customers can rent skis snowboards boots and poles by the day. josh rents a snowboard and a helmet. The expression 52s + 10 represents the toatak cost to rent a snow board for s days plus the fee for the helmet what dose the term 52s represent​

The margin of error is 2.695 for a sample size of 256 and a population variance of 484 at a 95% confidence level.



To find the margin of error, we need to use the formula:
Margin of error = z* (sigma / sqrt(n))
Where z* is the z-score corresponding to the desired confidence level, sigma is the population standard deviation, and n is the sample size.
In this case, we know that the population variance (sigma^2) is 484, which means the standard deviation (sigma) is sqrt(484) = 22.
The sample size (n) is 256.
The confidence level is 95%, which means the z-score is 1.96.
So, plugging these values into the formula, we get:
Margin of error = 1.96 * (22 / sqrt(256))
Simplifying, we get:
Margin of error = 1.96 * (22 / 16)
Margin of error = 2.695


Hence, we use the formula Margin of error = z* (sigma / sqrt(n)), where z* is the z-score corresponding to the desired confidence level, sigma is the population standard deviation, and n is the sample size. The margin of error is 2.695 for a sample size of 256 and a population variance of 484 at a 95% confidence level.

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Each line above the numbers multiply it by 1000. So this number is being multiplied by 1000000.

Each "C" is equal to 100, since there are "CCC" the number is 300.

"XL" is equal to 40, because "X" is equal to 10 and "L" is equal to 50, but since he X is on the left of the L we subtract them.

The number CCCXL is then 340, but there are the two lines. So we have:

Answer:

139.3 m²

Step-by-step explanation:

The figure is composed of a square and a semi circle.

area of square = 10² = 100 m²

area of semi circle = 0.5πr² = 0.5π × 5² = 0.5π × 25 = 12.5π ≈ 39.3 m²

Total area = 100 + 39.3 = 139.3 m²

Answer:

Step-by-step explanation:

Step one:

we are given the radius as

r=2cm, and the number of spheres is 50 in numbers

let us find the volume of one sphere and multiply the area by 50 to get the volume of the 50 spheres

Step two:

\(volume=4/3 \pi r^3\)

\(volume=4/3*3.142 *2^3\\\\volume=100.5/3\\\\volume=33.5\)

The volume of one sphere is 33.5in^3

Hence the volume of the 50 spheres will be

=33.5*50

=1675.7cm^3 approx.

Volume of the melted solution will be 1675.5 cm³.

  Radius of each metal sphere = 2 cm

Expression for the volume of the sphere is given by,

Volume = \(\frac{4}{3}\pi r^{3}\)

Here, r = radius of the sphere

By substituting the value of radius in the expression,

Volume = \(\frac{4}{3}\pi (2)^3\)

             = 33.51 cm³

Volume of the melted solution = Volume of 50 spheres

Volume of 50 spheres = 50 × (33.51)

                                      = 1675.5 cm³

    Therefore, Volume of the melted solution will be 1675.5 cm³.

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Mariela's purchase of a house located in the V Zone of an NFIP participating community means that the house is located in a high amount of risk flood zone and that she is required to purchase flood insurance if she has a mortgage. This insurance will help to protect her investment in the event of a flood.

Mariela must purchase flood insurance if she has a mortgage on her house, which is located in a high amount of risk flood zone in an NFIP participating community. This insurance will help to protect her investment in the event of a flood.

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The temperature of the body was 37 °C at the time of the murder occurred approximately 38 minutes before the csi team's arrival.

Newton's Law of Cooling, states that the rate of change in the temperature of an object is proportional to the difference between the object's temperature and the ambient temperature.

The general form of Newton's Law of Cooling is given by:

dT/dt = -k(T - Tₐ)

where dT/dt represents the rate of change of temperature, T is the temperature of the object, Tₐ is the ambient temperature, and k is a constant.

In this case, we can use the following information

The initial temperature of the body (T₀) = 37 °C

The temperature of the room (Tₐ) = 17 °C

Temperature of the body after 10 minutes (T) = 20 °C

We need to find the time elapsed (t) in minutes before the CSI team's arrival when the murder occurred.

We can set up the following equation using the initial temperature and the temperature after 10 minutes:

20 = (T₀ - Tₐ) × \(e^{-10k}\)

To solve for k, we rearrange the equation:

k = -ln((T - Tₐ) / (T₀ - Tₐ)) / 10

Substituting the given values:

k = -ln((20 - 17) / (37 - 17)) / 10

k ≈ 0.0693

Now, we can use the value of k to find the time (t) when the body's temperature was 37 °C:

37 = (T₀ - Tₐ) ×\(e^{-kt}\)

t = -ln((37 - 17) / (T₀ - Tₐ)) / k

t = -ln((37 - 17) / (37 - 17)) / 0.0693

t ≈ 37.64 minutes

Rounding to the nearest whole minute, the murder occurred approximately 38 minutes before the CSI team's arrival.

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A type II error is a statistical term used to describe the failure to reject a false null hypothesis. It occurs when the null hypothesis is actually false but is not rejected because the statistical test failed to find significant evidence against it.

In other words, it happens when an alternative hypothesis is true, but we fail to reject the null hypothesis.

Types of errors in hypothesis testing

There are two types of errors in hypothesis testing:

Type I error: Rejecting a true null hypothesis

Type II error: Failing to reject a false null hypothesis.Type I error occurs when a true null hypothesis is rejected while Type II error occurs when a false null hypothesis is not rejected. These types of errors are inversely related, meaning that a decrease in one type of error causes an increase in the other.

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Thinking:

Wait so like quadrants as in coordinate planes? If so, all the names of the quadrants are 1,2,3,4.

Answer:

it's 50 g

Step-by-step explanation:

it's like a multiplying assberry kehe

The optimal clutch size is 1.

a)The survivorship of an offspring, S, decreases with the clutch size, C.

Therefore, the formula is given as:

S = 0.5 - 0.1C

We need to find the optimal clutch size for this bird species.

We can do this by differentiating S with respect to C, and equating the result to zero.

That is:S = 0.5 - 0.1C => dS/dC = -0.1

Equating dS/dC to zero gives:-0.1 = 0 => C = 0

This implies that there is no optimal clutch size for this species.

However, since C represents clutch size, it must be a positive integer.

Therefore, we can choose a clutch size of 1, which will result in the highest survivorship of offspring. Hence, the optimal clutch size is

1.b)The optimal clutch size for a species with a survivorship-clutch size relationship of the form S = a - bC can be obtained by following the same procedure as above.

We can differentiate S with respect to C, and equate the result to zero.

That is:S = a - bC => dS/dC = -b

Equating dS/dC to zero gives:-b = 0 => C = 0

This implies that there is no optimal clutch size for this species. However, since C represents clutch size, it must be a positive integer. Therefore, we can choose a clutch size of 1, which will result in the highest survivorship of offspring. Hence, the optimal clutch size is 1.

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The ANOVA table is used to test the acceptability of the model from a statistical perspective. The table breaks down the components of variation in the data into variation between treatments and error or residual variation. The ANOVA table shows how the sum of squares are distributed according to source of variation, and hence the mean sum of squares1.

The ANOVA table has several components including the source of variation, degrees of freedom (df), sum of squares (SS), mean square (MS), F-statistic, and p-value2. The source of variation is divided into two categories: between groups and within groups. Between groups refers to the variation among the sample means for each treatment group, while within groups refers to the variation within each treatment group3.

To set up an ANOVA table for this problem, we first calculate the total sum of squares (SST) which is equal to 10,800. We then calculate the sum of squares due to treatments (SSTR) which is equal to 4560. The sum of squares due to error (SSE) can be calculated by subtracting SSTR from SST which gives us 10,800 - 4560 = 6240. The degrees of freedom for treatments is 2 since there are three methods and one degree of freedom is lost when calculating the mean. The degrees of freedom for error is 27 since there are 30 observations and three degrees of freedom are lost when calculating the means3.

Using α = 0.05, we can test for any significant difference in the means for the three assembly methods by comparing the F-statistic with the critical value from an F-distribution with df1 = 2 and df2 = 27. If F > F critical, then we reject the null hypothesis that there is no significant difference in means.

(a) The ANOVA table for this problem would look like this:

Source        df    SS MS         F

Treatments 2 4560 2280 F = MS(Treatments) / MS(Error)

Error         27 6240 231.11

Total         29 10800  

Plugging in our values, we have:

F = MS(Treatments) / MS(Error) = (4560 / 2) / (6240 / 27) ≈ 4.36

The critical value from an F-distribution with df1 = 2 and df2 = 27 at α = 0.05 is approximately 3.162.

Since F > F critical, we reject the null hypothesis that there is no significant difference in means.

Therefore, our conclusion is there is a significant difference in means for the three assembly methods.

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COMPLETE QUESTION - Three different methods for assembling a product were proposed by an industrial engineer. To investigate the number of units assembled correctly with each method, 30 employees were randomly selected and randomly assigned to the three proposed methods in such a way that each method was used by 10 workers. The number of units assembled correctly was recorded, and the analysis of variance procedure was applied to the resulting data set. The following results were obtained: SST = 10,800; SSTR = 4560. - a. b. Set up the ANOVA table for this problem. Use a = 0.05 to test for any significant difference in the means for the three assembly methods.

The value today of a money machine that will pay $1,488.00 per year for 18 years, with the first payment starting in 2 years, is approximately $16,033.52.

To determine how much Derek will need in his retirement account on his 65th birthday, we can use the concept of present value. Since Derek wants to withdraw $130,476.00 per year for 20 years (from his 66th to 85th birthday) and the interest rate is 9%, we can calculate the present value of this annuity.

By using the present value of an annuity formula, the calculation yields a retirement account balance of approximately $1,187,672.66 on his 65th birthday.

For the second scenario, to find the value today of a money machine that pays $1,488.00 per year for 18 years, starting 2 years from today, we can again use the concept of present value. With an interest rate of 10%, we calculate the present value of this annuity.

Using the present value of an annuity formula, the calculation shows that the value today of this money machine is approximately $16,033.52.In both cases, the present value calculations take into account the time value of money, which means that future cash flows are discounted back to their present value based on the interest rate.

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Answer:

I agree with Nico.

The height is stated that it is 8 (next to the symbol indicating a right angle).

Hope that helps!

Step-by-step explanation:

Answer:

The new ribbon is 511.85 meter long.

The total cost Rs. 6142.2

Step-by-step explanation:

First of all we convert all lengths to the standard unit that is meter.

0.5 km =500 meter

1005 cm = 10.05 meter

1800 mm = 1.8 meter

total length in meters

500+10.05+1.8= 511.85

total cost at the rate of Rs. 12 per meter will be

511.85×12=6142.2

The length of the new ribbon is 511.85m.

The cost of the single ribbon formed is Rs. 6142.20.

Common units of length are Millimeter(mm), centimeter(cm), meter(m), and kilometer(km).

The S.I. unit of length is the meter.

All units relate to the meter in the following way:

1000mm = 1m ⇒ 1mm = 1/1000m ⇒ 1mm = 0.001m

100cm = 1m ⇒ 1cm = 1/100m ⇒ 1cm = 0.01m

1km = 1000m

The lengths of three pieces of ribbon are given as 0.5km, 1005cm, and 1800mm.

We are asked to find the length of the ribbon formed by joining these 3 ribbons.

To find the total length, we need to add them, but to add we need lengths in equal units. So we convert the three lengths into the common unit meter.

0.5km = 0.5 * 1000 m = 500m (∵ 1km = 1000m)

1005cm = 1005 * 0.01m = 10.05m (∵ 1cm = 0.01m)

1800mm = 1800 * 0.001m = 1.8m (∵ 1mm = 0.001m)

∴ The length of the new ribbon = 500 + 10.05 + 1.8 m = 511.85m.

Cost of the ribbon = Rs. 12 per meter

∴ Cost of the single ribbon formed = Rs. 12 * 511.85 = Rs. 6142.20.

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Answer:10

Step-by-step explanation:

Answer:

50 giraffes are in the tub

Step-by-step explanation:

0.10x500=50

hope this helps, have a good day

Answer:

Step-by-step explanation:

First, write it in slope-intercept form.

8x-4=y

Rearrange this to get 8x-y=4.

A=8

B=-1

C=4

The decrease in diameter of the bar due to the applied load is -0.005434905d and the final diameter of the bar is 1.005434905d.

A compressive load of 80,000 lb is applied to a bar with a circular section of 0.75 in diameter and a length of 10 in.

if the modulus of elasticity of the bar material is 10,000 ksi and the Poisson's ratio is 0.3.

We have to determine the decrease in diameter of the bar due to the applied load.

Let d be the initial diameter of the bar and ∆d be the decrease in diameter of the bar due to the applied load, then the final diameter of the bar is d - ∆d.

Length of the bar, L = 10 in

Cross-sectional area of the bar, A = πd²/4 = π(0.75)²/4 = 0.4418 in²

Stress produced by the applied load,σ = P/A

= 80,000/0.4418

= 181163.5 psi

Young's modulus of elasticity, E = 10,000 ksi

Poisson's ratio, ν = 0.3

The longitudinal strain produced in the bar, ɛ = σ/E

= 181163.5/10,000,000

= 0.01811635

The lateral strain produced in the bar, υ = νɛ

= 0.3 × 0.01811635

= 0.005434905'

The decrease in diameter of the bar due to the applied load, ∆d/d = -υ

= -0.005434905∆d

= -0.005434905d

The final diameter of the bar,

d - ∆d = d + 0.005434905d

= 1.005434905d

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The exact values of the remaining trigonometric functions of θ are:

cos θ = 5/13

tan θ = 12/5

csc θ = 13/12

sec θ = 13/5

cot θ = 5/12

Given that sin θ = 12/13 and θ is in quadrant I, we can determine the values of the remaining trigonometric functions as follows:

cos θ:

In quadrant I, cos θ is positive. We can use the Pythagorean identity \(sin^2 θ + cos^2 θ = 1\) to find the value of cos θ:

\(cos^2 θ = 1 - sin^2 θcos^2 θ = 1 - (12/13)^2cos^2 θ = 1 - 144/169cos^2 θ = (169 - 144)/169cos^2 θ = 25/169\)

Since cos θ is positive in quadrant I, we take the positive square root:

cos θ = √(25/169)

cos θ = 5/13

tan θ:

tan θ is the ratio of sin θ to cos θ:

tan θ = sin θ / cos θ

tan θ = (12/13) / (5/13)

tan θ = 12/5

csc θ:

csc θ is the reciprocal of sin θ:

csc θ = 1 / sin θ

csc θ = 1 / (12/13)

csc θ = 13/12

sec θ:

sec θ is the reciprocal of cos θ:

sec θ = 1 / cos θ

sec θ = 1 / (5/13)

sec θ = 13/5

cot θ:

cot θ is the reciprocal of tan θ:

cot θ = 1 / tan θ

cot θ = 1 / (12/5)

cot θ = 5/12

Therefore, the exact values of the remaining trigonometric functions of θ are:

cos θ = 5/13

tan θ = 12/5

csc θ = 13/12

sec θ = 13/5

cot θ = 5/12

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The smaller sample's margin of error will be almost twice as great as the larger sample's margin of error.

Calculating the margin of error for a given sample from a population:

Let the level of significance be   and the standard deviation of the population be σ and the sample size be n, then we have:

MOE(Margin of Error)=  \(z_{a/2}\times\frac{\sigma}{\sqrt n}\)

Using the above formula to calculate the margin of errors for two specified samples

For first sample:

Sample size = n(1) = 1000

MOE(1) = \(z_{a/2}\times\frac{\sigma}{\sqrt {1000}}\)

For second sample:

Sample size = n(2) = 4000

MOE(2) = \(z_{a/2}\times\frac{\sigma}{\sqrt {4000}}\)

(since it was given that they have same point estimates, so same standard deviation)

Their ratios are given by

MOE(2)/MOE(1) = \(\frac{z_{a/2}\times\frac{\sigma}{\sqrt {4000}}}{z_{a/2}\times\frac{\sigma}{\sqrt {1000}}}\)

MOE(2)/MOE(1) = \(\sqrt{\frac{1000}{4000}}\)

MOE(2)/MOE(1) = 1/2

MOE(1) = 2*MOE(2)

Thus, the margin of error from the smaller sample will be about double of the margin of error from the larger sample.

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Answer:

122 and 58

Step-by-step explanation:

Answer: 2/5

Step-by-step explanation:  Dividing two fractions is the same as multiplying the first fraction by the reciprocal (inverse) of the second fraction

Take the reciprocal of the second fraction by flipping the numerator and denominator and changing the operation to multiplication. Then the equation becomes

The, multiply the numerators and denominators and get 12/30, then simply by 6 and get 2/5

Answer:

x=5

Step-by-step explanation:

Answer:

x = 5

Step-by-step explanation:

Original Equation:

2(x + 2) = 14

Use distributive property

2 · x = 2x

2 · 2 = 4

Your equation should look like this now:

2x + 4 = 14

Subtract 4 from both sides.

2x = 10

Divide both sides by 2

x = 5

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According to the given equation :

The given matrix can be represented as:⎡x  3y⎤⎣x  -3y⎦

So, x-determinant = 24 , y-determinant = 0 and system determinant = 4.

Given the system of equations as:

x + 3y = 5

x - 3y = -1

The given matrix can be represented as:

⎡x  3y⎤⎣x  -3y⎦

We need to find the x-determinant, y-determinant and system determinant.

Let us represent each matrix by A11, A12, A21, A22 and so on.

x-determinant: It is the determinant of matrix obtained by replacing the first column by the column on the right-hand side of the given matrix.

x-determinant = |5 -1| |-1 5|

= 5*5 - (-1)*(-1) = 25 - 1 = 24

y-determinant: It is the determinant of matrix obtained by replacing the second column by the column on the right-hand side of the given matrix.

y-determinant = |1 -1| |-1 1|

= 1*1 - (-1)*(-1) = 0

system determinant: It is the determinant of matrix obtained by replacing both the columns by the columns on the right-hand side of the given matrix.

system determinant = |5 -1| |1 -1|

= 5 - 1 = 4

Therefore, the answer to the question is:

x-determinant = 24

y-determinant = 0

system determinant = 4

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Answer:

We can find the system equivalent to this one by replacing one of the equations with a multiply of itself, so:

Let’s multiply y = x-2 by 2

2y=2x-4

A system equivalent to this one is

y = -2x^2 (this remains the same)

2y= 2x-4

Answer:

a) x = 8

b) x = 18

Step-by-step explanation:

Given that

9 : x : : 54 : 48

This may be rewritten as

9/x = 54/48

cross multiply

9 * 48 = 54 * x

Divide both sides by 54

x = 9 * 48/54

= 8

b Given that 2, 6, x are in continued proportion then

2/6 = 6/x

2 * x = 6 * 6

x = 36/2

= 18

The series Σ n=1 to ∞ of \(n! /n^n\) converges.

To determine whether the series Σ n=1 to ∞ of \(n! /n^n\) converges or diverges, we can use the ratio test.

The ratio test states that if the limit of the absolute value of the ratio of the (n+1)th term to the nth term as n approaches infinity is less than 1, then the series converges. If the limit is greater than 1 or does not exist, then the series diverges.

Let \(a_n = n! /n^n\) be the nth term of the series. Then, the ratio of the (n+1)th term to the nth term is:

\(a_(n+1) / a_n = (n+1)! / (n+1)^(n+1) * n^n / n!= (n+1)/n * (n/n+1)^n= (n+1)/n * 1/((1 + 1/n)^n)\)

As n approaches infinity, the second term goes to 1/e by the definition of the exponential function. Therefore,

lim(n→∞) \(a_(n+1) / a_n\) = lim(n→∞) \((n+1)/n * 1/((1 + 1/n)^n)= 1/e < 1\)

Since the limit is less than 1, the series converges by the ratio test.

To explain this result, we can note that n! grows much faster than n^n as n increases. This can be seen by writing n! as a product of factors:

\(n! = n * (n-1) * (n-2) * ... * 2 * 1\)

Each factor is less than or equal to n, so we can write:

\(n!\) ≤ \(n * n * n * ... * n * n = n^n\)

Therefore, \(n! / n^n\) is always less than or equal to 1. As a result, the series converges by the ratio test.

In summary, the series Σ n=1 to ∞ of \(n! /n^n\) converges.

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