Probability of a value of x, given a mean value of x bar and standard deviation s

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SUMMARY

The discussion focuses on calculating the probability of aquifer water levels dropping between 660 ft and 641 ft, given a mean level of 680 ft and a standard deviation of 30 ft. It emphasizes the inadequacy of assuming a normal distribution due to the dependency of water levels over time, suggesting the use of time series modeling instead. For extreme events, the conversation highlights the relevance of extreme value theory in assessing probabilities outside typical ranges.

PREREQUISITES
  • Understanding of normal distribution and its limitations
  • Knowledge of time series analysis techniques
  • Familiarity with extreme value theory
  • Basic statistics, including mean and standard deviation calculations
NEXT STEPS
  • Research time series modeling techniques using R or Python
  • Study extreme value theory applications in environmental statistics
  • Learn about statistical software tools like R's 'forecast' package for time series analysis
  • Explore methods for estimating probabilities in non-normal distributions
USEFUL FOR

This discussion is beneficial for environmental scientists, hydrologists, and statisticians involved in water resource management and those interested in modeling and predicting extreme events in time series data.

moonman239
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Let's say I monitors my local aquifer (Edwards) using well J-17 over the past 8 summers (not counting this summer). I found that the mean water level/day is 680 ft above sea level, with a standard deviation of 30. Assuming a normal distribution, how do I find the probability that the aquifer level will drop to between 660 and 641 ft sometime this summer, at which time Stage 1 drought restrictions are in place?
 
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Also, how do I find the probability that the level will be exactly 641 ft?
 
Assuming a normal distribution won't do you any good. The water levels in the aquifer at different days are certainly dependent so you need to model them as a time series. One you have estimated a suitable time series model from your observations, the question of how likely the water level is to drop to below a certain threshold can be answered in different ways.
One thing which usually comes up in this context is that the target region for which you want to estimate the probability is well outside the typical range of the time series under consideration and reaching it is therefore considered an extreme event, which then leads to the discipline called extreme value theory for time series.
 

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