Chaya Galloway
2020-10-28
Answered

Hi I need help with thisquestion.
A uniform metal rod, with a mass of 3.1 kg and a length of 1.2 m,is attached to a wall by a hinge at the base. A horizontalwire bolted to the wall 0.51 m above the base of the rod holds therod at an angle of 25 degrees above the horizontal. The wireis attached to the top of the rod. (a) Find the tension inthe wire. (b) Find the horizontal and verticalcomponents of the force exerted on the rod by the hinge.
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l1koV

Answered 2020-10-29
Author has **100** answers

A uniform metal rod, with a mass = 3.1 kg and alength =1.2 m, is attached to a wall by a hinge at thebase.

A horizontal wire bolted to the wall 0.51 m above thebase of the rod holds the rod at an angle of 25 degrees above thehorizontal.

The wire is attached to the top of the rod.

(a) Find the tension in the wire.

(b) Find the horizontal and vertical components ofthe force exerted on the rod by the hinge the total torque acting on the system should bezero hence

(b)the horizontal component of the force exerted on therod by the hinge = 32 N since the horizontal component of the force equalto and opposes so the horizontal wire tension = 32N the vertical components of the forceexerted on the rod by the hinge

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A 230kg crate hangs from the end of a rope of length L=12.0m. You push horizontally on the crate with a varying force F to move it distance d= 4.00m to the side.

a) What is the magnitude of F when the crate is in this final position? What are b) the total work done on it, c) the work done by the gravitational force on the crate, and d) the work done by the pull on the crate fromthe rope? e) Knowing the the crate is motionless before and after its displacement, use the answers to (b), (c), and (d) to find the work your force F does on the crate. f) Why is the work of your force not equal to the product of the horizontal displacement and the answer to (a)?

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Interpreting the differences of two log normal distributions:

I have read a couple of posts, and did not see the exact interpretation, I apologize in advance if this is not in the right location

Purpose: I am preparing a paper on the distribution of litter densities along the shore line in freshwater environnements. The data is collected by hand and the individual pieces are classified and counted by volunteers. These programs exist in many countries and there are fairly large data sets.

The units are expressed as 'pieces of trash/meter(or foot) of shoreline.

Assumptions:

- The data is collected in the same manner

- The volunteers have the same motivations

- There is no (under counting or over counting)

- Accuracy is basically the same across the spectrum

- The math is correct

- The graph below represents the graph of two sets of Data:

MCBP is regional results for Lake Geneva (Switzerland) n=100 samples

SLR results from the 'The Swiss Litter Report' n=365 samples The following code was used to calculate the distributions and present the graphs from a DataFrame in pandas/python 3.6:

df['Density] = df['Total']/df['Length']

df['Logs'] = df['Density'].apply(np.log)#<- skewed data(get it close to norm)

mu, sigma = stats.norm.fit(df['Logs'])

#repeat for df2 to get the second curve

#Build histograms for the two data sets

#plot the two disributions where x = df['Logs']

#and y = stats.norm.pdf(x, loc=mu, scale=sigma)

The resulting two distributions

mu for the the SLR disribution is 0.1564617, which is equal to the 5th percentile of the MCBP distirbution.

I am interpreting this as meaning:

There is a 5% oprobability that a sample from MCBP will be less than the average from SLR.

There is a 95% probability that a sample taken from the MCBP region will be greater than the national average

In general I can expect litter densities to be greater in the MCBP region than in the SLR region

Is this interpretation correct? (It does correlate with observations)

I have read a couple of posts, and did not see the exact interpretation, I apologize in advance if this is not in the right location

Purpose: I am preparing a paper on the distribution of litter densities along the shore line in freshwater environnements. The data is collected by hand and the individual pieces are classified and counted by volunteers. These programs exist in many countries and there are fairly large data sets.

The units are expressed as 'pieces of trash/meter(or foot) of shoreline.

Assumptions:

- The data is collected in the same manner

- The volunteers have the same motivations

- There is no (under counting or over counting)

- Accuracy is basically the same across the spectrum

- The math is correct

- The graph below represents the graph of two sets of Data:

MCBP is regional results for Lake Geneva (Switzerland) n=100 samples

SLR results from the 'The Swiss Litter Report' n=365 samples The following code was used to calculate the distributions and present the graphs from a DataFrame in pandas/python 3.6:

df['Density] = df['Total']/df['Length']

df['Logs'] = df['Density'].apply(np.log)#<- skewed data(get it close to norm)

mu, sigma = stats.norm.fit(df['Logs'])

#repeat for df2 to get the second curve

#Build histograms for the two data sets

#plot the two disributions where x = df['Logs']

#and y = stats.norm.pdf(x, loc=mu, scale=sigma)

The resulting two distributions

mu for the the SLR disribution is 0.1564617, which is equal to the 5th percentile of the MCBP distirbution.

I am interpreting this as meaning:

There is a 5% oprobability that a sample from MCBP will be less than the average from SLR.

There is a 95% probability that a sample taken from the MCBP region will be greater than the national average

In general I can expect litter densities to be greater in the MCBP region than in the SLR region

Is this interpretation correct? (It does correlate with observations)

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When I divided 1 by 9,899, I got two-digit Fibonacci numbers also being carried: $0.0001010203050813213455904636\dots $

When I divided 1 by 89, I got one-digit Fibonacci numbers at the beginning: 0.0112359… (there was originally an eight, but there was carrying and it changed to a nine)

How does all of this happen? There is more than this, you know. What do you think is going on here?

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