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Question 1
Helen believes that the random variable $C$, representing cloud cover from the large data set, can be modeled by a discrete uniform distribution. (a) Write down the... show full transcript
Step 1
Answer
The random variable is modeled by a discrete uniform distribution, which can be defined as follows:
For a discrete uniform distribution where can take values , the probability mass function is given by:
P(C = c_i) = rac{1}{n} for .
In this case, if we assume can take values from to , then the probability for each value is given by:
P(C = c) = rac{1}{n+1} where .
Step 2
Answer
To find the probability that the cloud cover is less than 50%, we need to consider the range of values that can take.
If we assume the maximum value b of is (representing 100% cloud cover), then the values less than are .
Thus, we have a total of favorable outcomes. The total number of possible outcomes is (from to ).
Therefore, the probability that cloud cover is less than 50% is:
P(C < 50) = rac{50}{101} \approx 0.495
Step 3
Answer
Given that Helen's model predicts a distribution of cloud cover that aligns with a uniform probability, but data from 2015 reveals that only of the days had cloud cover of less than %, it indicates that Helen's model may not accurately represent the true distribution of cloud cover. The model suggests a higher occurrence of lower cloud cover than observed in actual data, revealing a potential discrepancy.
This discrepancy calls into question the reliability of a discrete uniform model in this context, suggesting that a different probability distribution might be more suitable for representing the data.
Step 4
Answer
To refine Helen's model, one option may be to use a different probability distribution that accommodates the observed data more accurately. A possible refinement could involve:
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