sampling_distribution
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| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| sampling_distribution [2016/05/17 15:27] – [CLT] hkimscil | sampling_distribution [2025/03/24 08:44] (current) – old revision restored (2016/05/17 15:23) hkimscil | ||
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| * n = 900인 경우? | * n = 900인 경우? | ||
| * n = 1600인 경우? | * n = 1600인 경우? | ||
| - | <WRAP clear /> | + | |
| ===== CLT ===== | ===== CLT ===== | ||
| - | 위에서 언급한 가상의 | + | 위에서 언급한 가상의 샘플평균들의 분포를 구한다면 그 분포곡선은 아래의 성질을 갖게 된다. |
| * $\mu_{\overline{\tiny{X}}} = \mu$ | * $\mu_{\overline{\tiny{X}}} = \mu$ | ||
| * $\sigma_{\overline{X}} = \frac{\sigma}{\sqrt{n}}$ | * $\sigma_{\overline{X}} = \frac{\sigma}{\sqrt{n}}$ | ||
| Line 55: | Line 56: | ||
| (sampling distribution은 [Central Limit Theorem] 을 이해하기 위해서 꼭 필요한 개념이다.) | (sampling distribution은 [Central Limit Theorem] 을 이해하기 위해서 꼭 필요한 개념이다.) | ||
| - | < | + | $\mu=70$ 이며 $\sigma=15$ 인 모집단의 경우에서 n = 100인 샘플을 뽑는다고 가정을 해보면, |
| * $\mu_{\tiny\overline{X}} = \mu = 70$ | * $\mu_{\tiny\overline{X}} = \mu = 70$ | ||
| * $\sigma_{\tiny\overline{X}} = \frac{\sigma}{\sqrt{n}} = \frac{15}{\sqrt{100}} = 1.5$ | * $\sigma_{\tiny\overline{X}} = \frac{\sigma}{\sqrt{n}} = \frac{15}{\sqrt{100}} = 1.5$ | ||
| + | 아래는 읽지 마세요. | ||
| ====== English ====== | ====== English ====== | ||
| I mentioned in the earlier article that the standard error is actually standard deviation of sampling distribution. I would feel safe when I say standard deviation since I covered the concept already. However, I thought you might feel uneasy about " | I mentioned in the earlier article that the standard error is actually standard deviation of sampling distribution. I would feel safe when I say standard deviation since I covered the concept already. However, I thought you might feel uneasy about " | ||
sampling_distribution.1463468277.txt.gz · Last modified: by hkimscil