Cumulative Distribution Function of Distance

In summary, we are trying to find the cumulative distribution function FD(d) and the probability density function fD(d) for the location of an emergency in a city district that is a square rotated 45 degrees with "radius" r. The ambulance is located at the center of the district and we are looking for the "right-angle" distance D from the ambulance to the emergency. One approach is to define two random variables X and Y for the coordinates of the emergency and use the sum of those to find D. Another approach is to consider the area of infinitesimal circular strips to obtain the PDF of the distance, taking into account when the circle exceeds the square. Both approaches may have their own advantages and potential complications.
  • #1
Kalinka35
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Homework Statement


The location of an emergency is uniformly distributed over a city district. The district is a square rotated 45 degrees with "radius" r (the distance from the center to the top corner is r).
When the emergency occurs, the ambulance is at the center of the district. Let D be the "right-angle" distance from the ambulance to the emergency.
I need to find the cumulative distribution function FD(d) and the probability density function fD(d).

Homework Equations





The Attempt at a Solution


I know that the PDF is the derivative of the CDF so I am trying to find the CDF first.
It seems like you could define two random variables X and Y that give the coordinates of the emergency and the sum of those would give you the right-angle distance, but that seems like it might be overcomplicating things and you might just be able to work with D, the total distance.
Either way I just don't see how you can obtain a CDF.
 
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  • #2
so you could look at it this way...

the area of any region, divided by the total area of the square is the probabilty of emergency happening in that area

so consider a infinitesimal circular strip, between s & s + ds. You could work out the area of the strip to get the pdf of the distance

the only trick being when the circle exceeds the square at given regions. Then just work out the angle of the circle that is actually within the square

Though you could also try the combination of random varibles as you suggest, I'm not sure how it would work out until I tried, could be clean...
 

What is the Cumulative Distribution Function (CDF) of Distance?

The Cumulative Distribution Function (CDF) of Distance is a statistical measure that describes the probability of a random variable (such as distance) being less than or equal to a certain value. It is used to analyze and understand the distribution of distances in a dataset.

How is the CDF of Distance calculated?

The CDF of Distance is calculated by taking the integral (or sum) of the Probability Density Function (PDF) of Distance from negative infinity up to a given distance value. This gives the probability that a data point will have a distance less than or equal to the given value.

What is the significance of the CDF of Distance in data analysis?

The CDF of Distance is an important tool in data analysis as it provides a complete picture of the distribution of distances in a dataset. It allows for comparisons between different datasets and can help identify patterns and outliers in the data.

Can the CDF of Distance be used to make predictions?

Yes, the CDF of Distance can be used to make predictions about future data points. By comparing the CDF of Distance for a current dataset to a historical dataset, we can estimate the probability of a new data point falling within a certain distance range.

How can the CDF of Distance be visualized?

The CDF of Distance can be visualized using a cumulative distribution plot, which shows the probability on the y-axis and the distance on the x-axis. Another common way to visualize the CDF of Distance is by plotting it alongside the PDF of Distance to compare the two distributions.

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