SUMMARY
The discussion centers on the evaluation of the probability mass function defined as $p(x) = p^x(1-p)^{1-x}$ for $x=0.1$ and $0$ otherwise, where $0 \leq p \leq 1$. It is concluded that this does not constitute a valid probability mass function since the sum of $p(x)$ must equal 1, which is not satisfied in this case. The mean of the random variable $X$ is determined to be 0.1, and the variance is established as 0, given that there is only one value for $x$.
PREREQUISITES
- Understanding of probability mass functions
- Familiarity with the concepts of mean and variance
- Knowledge of the properties of random variables
- Basic algebra for manipulating equations
NEXT STEPS
- Study the properties of valid probability mass functions
- Learn about the calculation of mean and variance for discrete random variables
- Explore the implications of non-valid probability distributions
- Investigate the role of parameter $p$ in binomial distributions
USEFUL FOR
Students and professionals in statistics, data science, and mathematics who are looking to deepen their understanding of probability distributions and their properties.