b:head_first_statistics:estimating_populations_and_samples
Differences
This shows you the differences between two versions of the page.
| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| b:head_first_statistics:estimating_populations_and_samples [2025/11/04 22:36] – [Sampling distribution of sample mean] hkimscil | b:head_first_statistics:estimating_populations_and_samples [2025/11/04 23:18] (current) – [Exercise] hkimscil | ||
|---|---|---|---|
| Line 638: | Line 638: | ||
| < | < | ||
| - | * estimating_populations_and_samples/page1 | + | * b: |
| - | * sand_box:page2 | + | * b: |
| - | * sand_box:page3 | + | |
| </ | </ | ||
| Line 692: | Line 691: | ||
| ===== Using CLT for the binomial distribution ===== | ===== Using CLT for the binomial distribution ===== | ||
| $X \sim B(n, p)$ 에서 $\mu = np$, $\sigma^2 = npq$ 이고, | $X \sim B(n, p)$ 에서 $\mu = np$, $\sigma^2 = npq$ 이고, | ||
| - | n이 30이 넘는 조건에서 이항분포가 정상분포를 이룬다고 하므로 | + | n이 30이 넘는 조건에서 이항분포가 정상분포를 이룬다고 하므로 |
| - | $\overline{X} \sim N(\mu, \frac{\sigma^2}{n})$에 대입해 보면: | + | $\overline{X} \sim N (\mu, \frac{\sigma^2}{n} ) $에 대입해 보면: |
| - | $$\overline{X} \sim N(np, \; pq) $$ | + | $$\overline{X} \sim N \left(np, \; pq \right) $$ |
| {{: | {{: | ||
| Line 738: | Line 737: | ||
| > pnorm(b/a) | > pnorm(b/a) | ||
| [1] 1.053435e-16 | [1] 1.053435e-16 | ||
| - | |||
| </ | </ | ||
| + | |||
| + | |||
| ====== Recap ====== | ====== Recap ====== | ||
| Distribution of **Sample** <fc # | Distribution of **Sample** <fc # | ||
b/head_first_statistics/estimating_populations_and_samples.1762295768.txt.gz · Last modified: by hkimscil