The survival time (in days) of a white rat that was subjected to a certain level of X-ray radiation is a random variable X~ Gam(4, 3). Use Theorem 3.3.2 to find: РСХ < 12) P(12
Continuous Probability Distributions
Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.
Normal Distribution
Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!
![### 3.3.2 Gamma Distribution CDF for Positive Integer \( n \)
If \( X \sim \text{GAM}(\theta, n) \), where \( n \) is a positive integer, then the Cumulative Distribution Function (CDF) can be written as:
\[ F(x; \theta, n) = 1 - \sum_{i=0}^{n-1} \frac{(x/\theta)^i}{i!} e^{-x/\theta} \]
(Equation 3.3.12)
In this context:
- \( \theta \) (theta) is the scale parameter.
- \( n \) is the shape parameter, which is a positive integer.
- \( x \) is the variable of interest.
- \( i \) is an index variable that iterates from 0 to \( n-1 \).
The expression inside the summation is the normalized incomplete gamma function, which, together with the exponential term, represents the probability that a gamma-distributed random variable \( X \leq x \).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa512e5ed-0cb1-424e-ac94-5a6d2cf189ee%2F6761d90f-f766-4018-9052-844f94563288%2Ffoukan9.jpeg&w=3840&q=75)
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