Lecture 33

Qi Wang, Department of Statistics

Nov 10, 2017

If a Binomial distribution has a large enough combination of n and p, it behaves much like a Normal distribution, which means we can use the Normal distribution to approximate the original Binomial distribution

- If $X \sim Bin(n, p)$, and $np > 5, n(1 - p) > 5$
- Then we can use $X^\star \sim N(\mu = np, \sigma = \sqrt{np(1-p)})$, to approximate $X$

You may notice that Binomial is Discrete, and Normal is Continuous. This means the approximation comes at a cost of accuracy that we must try to correct. When we use the approximation, we need to perform a continuity correction:

- If youâ€™re looking for: $P(a \le X \le b)$
- Use $P(a - 0.5 < X^\star < b + 0.5)$

If all conditions are satistified, find the Normal approximation to the following probability statement where $X$ follows a Binomial distribution

- $P(4 \le X \le 10)$
- $P(4 < X < 10)$
- $P(X \le 6)$
- $P(X < 5)$
- $P(X \ge 9)$
- $P(X > 8)$

- The Binomial approximation to the Hypergeometric: If $X \sim Bin(n, p)$, and $np > 5, n(1 - p) > 5$, we can use $X^\star \sim N(\mu = np, \sigma = \sqrt{np(1-p)})$, to approximate $X$
- The Poisson approximation to the Binomial: If $X \sim Bin(n, p)$ with
**$n>100$**and $p<0.01$, we can use $X^\star \sim Poisson(\lambda = np)$, to approximate $X$ - The Normal approxmation to the Binomial: If $X \sim Bin(n, p)$, and $np > 5, n(1 - p) > 5$, then we can use $X^\star \sim N(\mu = np, \sigma = \sqrt{np(1-p)})$, to approximate $X$

- Minimum(Min)
- First Quartile(Q1)
- Median(M)
- Third Quartile(Q3)
- Maximum(Max)

Calculate quartiles using the indexing method: i = np, where p is the percentile written as a decimal. If i is not an integer, round i up to the next integer. This is the position of that percentile. If i is an integer, take an average of the $i_{th}$ and $(i + 1)_{st}$ values. That average is the percentile of interest.

Calculate Five Number Summary for the following dataset: $${4.1, 6.2, 10.4, 5.5, 9.7, 21.3, 7.1}$$