# Don't know if a solution is possible. Real world problem.

• trueacoustics
In summary, to estimate the probability of receiving the same utility bill, to the cent, two months in a row, you would need to consider various factors such as past bill data, usage patterns, lifestyle changes, and potential external factors. Assuming each month is modeled by a normal distribution and is independent from the next, you could use the formula P(A and B) = P(A)P(B) to calculate the probability. However, this approach is oversimplified and may not be reliable for practical decision making. It is important to thoroughly understand all the factors that could affect the bill price before attempting to estimate the probability.
trueacoustics
How would you find/estimate the probability of receiving the same utility bill, to the cent, two months in a row. This is a continuous distribution with an infinite amount of solution. However, for practicality, I want to make the distribution more finite. In reality, my bill will probably never exceed 150. I won't be able to tell you the mean or median of the distribution, but a very rough estimate of the interquartile range is $40-80. Thanks, Tony trueacoustics said: How would you find/estimate the probability of receiving the same utility bill, to the cent, two months in a row. This is a continuous distribution with an infinite amount of solution. However, for practicality, I want to make the distribution more finite. In reality, my bill will probably never exceed 150. I won't be able to tell you the mean or median of the distribution, but a very rough estimate of the interquartile range is$40-80.

Thanks,

Tony

Hey trueacoustics and welcome to the forums.

Although this might seem like a simple question, it isn't.

The thing for this is to figure out how past bills and usage affects your new bill. If you assume that both bills are independent, then the problem is made much easier. If you assume that your past bill only depends on factors that relate to the previous one, it makes things a little more complex, but still manageable.

The point is you need to first figure out what will affect your bill both on any past data as well as things that don't necessarily relate to the data of your past bills.

Here is a good way to think about this: you could take all your bill data for the past two years and draw some kind of graph. That graph might tell you something useful, but it won't tell you the most important things.

For example you might find that for certain months, you need to use more water, more heating and so on. Now you might argue that this would be reflected in the price of your bill at certain points.

But what for example your electricity company introduces off-peak hours where they charge you significantly less for using power in those times? If you know this then you will pick this out but if you were not aware of this fact you wouldn't know what to think.

So the first thing you need to do is firstly think about what affects your power prices. Does the time of year affect it? What about the day of the week? What about where you live? Does any changes in lifestyle have an impact? (You might be away from home some part of the year)

The point I'm trying to make is not to just think of the data for bill prices because that really in the grand scheme of things doesn't tell you much: if you understand how your actions contribute to the final bill you get each month then you will understand more or less how it is calculated on those principles.

But let's just for the moment say that for your example, that each month is modelling by a normal distribution with mean = m and variance = v and each month is independent from the next.

Basically you will have an interval for your bill price so let's say your interval is [b-d,b+d] where d is some kind of noise correction (for your example I'd make it 2 cents) and b is your bill price.

Then using this you need to calculate P(b - d < A < b + d) x P(b - d < B < b + d) which can be found if you standardize the values based on (m,v) and then use a computer.

But I really really have to stress: this is so simplifying that it's probably useless in a practical sense.

It's the same kind of reason that looking purely at stock price history data is dangerous if you want to actually do serious investing especially if you don't understand anything about how markets work, how things are valued, how to assess a business model and how it makes money and so on.

I never assumed it was simple. That's why I said I don't even know if it could be solved. There are a very large amount of variables which go into the equation, and I won't be able to account for most of them. I think the variable which I really want to consider are these facts, and the rest can be assumed nonexistent:
Three people living in the apartment
10 appliances which produce water
13 appliances that produce electricity
I am just trying to get a very rough estimate.

Everything else is extremely variable and improvable to consider.

Thanks

Tony

trueacoustics said:
I never assumed it was simple. That's why I said I don't even know if it could be solved. There are a very large amount of variables which go into the equation, and I won't be able to account for most of them. I think the variable which I really want to consider are these facts, and the rest can be assumed nonexistent:
Three people living in the apartment
10 appliances which produce water
13 appliances that produce electricity
I am just trying to get a very rough estimate.

Everything else is extremely variable and improvable to consider.

Thanks

Tony

If you think that the use for each month will be modeled by the same distribution and the months are independent then you can use the formula P(A and B) = P(A)P(B) where P(A) is the probability of getting a price range for month A and P(B) is the probability of getting the same price for the next month B.

What you could do is take your past data and get an estimate for your mean and variance and then for your price get the probability based on these estimated parameters.

But I have to ask in what context is this being used? If it's been used in any serious capacity then my advice would be not to use this. If it is just for interest and not to be used for serious decision making then I guess no harm can be done.

The point is if you want to use this for something serious, then it's not a good idea to use the above model. If you want to do this it's probably better if you look at the past bills and think about the usage in terms of what appliances you use and what your friends use over given periods and then get some rule of thumb measure. Certainly the normal distribution is a good way to approximate things like this, but yeah you have to be careful.

I understand the frustration of facing a real-world problem with no clear solution in sight. However, this does not mean that a solution is not possible. In fact, as a scientist, it is our job to find creative and effective ways to solve complex problems.

In this case, the problem at hand is estimating the probability of receiving the same utility bill, to the cent, two months in a row. This is indeed a continuous distribution with an infinite number of solutions. However, to make it more manageable and practical, we can narrow down the distribution to a finite range. In this case, we can estimate that the bill will not exceed $150 and the interquartile range is approximately$40-80.

To find a more accurate estimate, we can gather data from previous utility bills and use statistical analysis to determine the mean and median of the distribution. This will give us a better understanding of the central tendency and variability of the bills. Additionally, we can also look at external factors such as seasonal changes, energy usage patterns, and any potential changes in utility rates.

It is important to note that this estimate will still have some level of uncertainty, as there are many factors that can impact the utility bill. However, by using scientific methods and data analysis, we can come up with a more informed and reliable estimate of the probability of receiving the same utility bill two months in a row.

## 1. Can a solution to a real world problem always be found?

Unfortunately, there is no guarantee that a solution can always be found for every real world problem. Some problems may be too complex or have too many variables to find a definitive solution.

## 2. How do scientists determine if a solution is possible for a real world problem?

Scientists use a variety of methods, such as experimentation, data analysis, and mathematical modeling, to determine if a solution is possible for a real world problem. They also consult with experts in the field and conduct thorough research to gather information and evidence.

## 3. What are some common barriers to finding a solution for a real world problem?

Some common barriers to finding a solution for a real world problem include limited resources, conflicting interests or values, and lack of knowledge or understanding about the problem. Other factors such as time constraints and ethical considerations may also play a role.

## 4. How do scientists approach a problem when they are unsure if a solution is possible?

When scientists are faced with a problem and are unsure if a solution is possible, they often approach it with an open mind and use a combination of critical thinking and creativity to explore potential solutions. They may also collaborate with other experts and conduct further research to gain a better understanding of the problem.

## 5. What can be done if a solution to a real world problem cannot be found?

If a solution cannot be found for a real world problem, scientists may continue to research and explore different approaches in hopes of finding a solution. However, in some cases, it may not be possible to find a solution and alternative strategies may need to be considered, such as managing the problem or finding ways to mitigate its effects.

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