Find the inflection points of a normal random variable with mean of u (Mu) and a standard deviation of sigma
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!
Find the inflection points of a normal random variable with
![**Problem Statement:**
Find the inflection points of a normal random variable with a mean of \( \mu \) and a standard deviation of \( \sigma \).
**Explanation:**
In probability theory, the inflection points of a normal distribution are the points where the curvature of the probability density function changes direction. For a normal distribution with mean \( \mu \) and standard deviation \( \sigma \), the inflection points occur at:
\[ \mu - \sigma \quad \text{and} \quad \mu + \sigma \]
These points mark the transition from the distribution curve being concave to convex (or vice versa).](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2F132c2b50-c806-45f9-8baf-5357fa898a21%2Ff903ed4c-3234-463b-af2f-c70c542d41d6%2F7kapqs9_processed.jpeg&w=3840&q=75)
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