3) Calculate the variance of the random variable X whose probability distribution is given in he table 10 11 12 13 14 15 P(X = x) 8 | 8 4) Calculate the standard deviation of the random variable X whose probability distribution is given in part 3)
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!
![**Problem 3: Calculating the Variance**
Calculate the variance of the random variable \( X \) whose probability distribution is given in the table below:
\[
\begin{array}{c|cccccc}
x & 10 & 11 & 12 & 13 & 14 & 15 \\
\hline
P(X = x) & \frac{1}{8} & \frac{2}{8} & \frac{1}{8} & \frac{2}{8} & \frac{1}{8} & \frac{1}{8} \\
\end{array}
\]
**Problem 4: Calculating the Standard Deviation**
Calculate the standard deviation of the random variable \( X \) whose probability distribution is given in part 3.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fa6e5354a-af81-4083-9a8b-a2eadb950c57%2F88aea142-d6a0-4fcb-bd11-72dc7b7928a0%2Fgahfhlk_processed.png&w=3840&q=75)
3)
Given data,
|
x |
P(X=x) |
|
10 |
1/8 |
|
11 |
2/8 |
|
12 |
1/8 |
|
13 |
2/8 |
|
14 |
1/8 |
|
15 |
1/8 |
The formula for the expected value of a discrete random variable is,
E(x)=μ=∑x⋅P(X=x)
The formula for the variance of the discrete random variable is,
σ2=∑[(x−μ)2. P(X=x)]
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