MHB Math Help: Understand How to Compute $F_{X_1}(x)$

AI Thread Summary
The discussion centers on understanding the computation of the cumulative distribution function \( F_{X_1}(x) \) using the binomial distribution. The formula \( F_{X_1}(x) = \sum_{j=1}^n \binom{n}{1} F^1(x) (1-F(x))^{n-1} \) represents the probability of obtaining at least one success in \( n \) trials. It is derived from the complement rule, where \( F_{X_1}(x) \) is calculated as \( 1 - (1 - F(x))^n \). This indicates that the total probability for a specific value \( x \) sums to 1, confirming that \( F_{X_0}(x) = 1 \). The explanation emphasizes the relationship between the binomial distribution and cumulative probabilities.
WMDhamnekar
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Now, I don't understand how did author compute $F_{X_1}(x) = \displaystyle\sum_{j=1}^n \binom{n}{1} F^1(x) (1-F(x))^{n-1} = 1-(1-F(x))^n ?$ (I know L.H.S = R.H.S)

Would any member of Math help board explain me that? Any math help will be accepted.
 
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The formula $\binom nj F^j(x)(1-F(x))^{n-j}$ is the probability mass function of the binomial distribution with parameters $p=F(x)$ and $n$.
Consequently we have that all possibilities for a specific $x$ sum up to $1$.
It implies that $F_{X_0}(x)=1$.
We can use the complement rule $P(A^c)=1-P(A)$ to calculate $F_{X_1}(x)$.
 
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