목차

삼원배치법 (모수모형) (반복없음)

데이터 구조

요인 $A$는 모수인자

요인 $B$는 모수인자

요인 $C$는 모수인자

$$ y_{ijk} = \mu + a_{i} + b_{j} + c_{k} + (ab)_{ij} + (ac)_{ik} + (bc)_{jk} + e_{ijk} $$

자료의 구조

인자
$B$
인자
$C$
인자 $A$
$$A_{1}$$ $$A_{2}$$ $$\cdots$$ $$A_{l}$$
$$B_{1}$$ $$C_{1}$$ $$y_{111}$$ $$y_{211}$$ $$\cdots$$ $$y_{l11}$$
$$C_{2}$$ $$y_{112}$$ $$y_{212}$$ $$\cdots$$ $$y_{l12}$$
$$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$
$$C_{n}$$ $$y_{11n}$$ $$y_{21n}$$ $$\cdots$$ $$y_{l1n}$$
$$B_{2}$$ $$C_{1}$$ $$y_{121}$$ $$y_{221}$$ $$\cdots$$ $$y_{l21}$$
$$C_{2}$$ $$y_{122}$$ $$y_{222}$$ $$\cdots$$ $$y_{l22}$$
$$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$
$$C_{n}$$ $$y_{12n}$$ $$y_{22n}$$ $$\cdots$$ $$y_{l2n}$$
$$\vdots$$ $$\vdots$$
$$B_{m}$$ $$C_{1}$$ $$y_{1m1}$$ $$y_{2m1}$$ $$\cdots$$ $$y_{lm1}$$
$$C_{2}$$ $$y_{1m2}$$ $$y_{2m2}$$ $$\cdots$$ $$y_{lm2}$$
$$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$
$$C_{n}$$ $$y_{1mn}$$ $$y_{2mn}$$ $$\cdots$$ $$y_{lmn}$$

$AB$ 2원표

인자
$B$
인자 $A$ 합계
$$A_{1}$$ $$A_{2}$$ $$\cdots$$ $$A_{l}$$
$$B_{1}$$ $$T_{11.}$$ $$T_{21.}$$ $$\cdots$$ $$T_{l1.}$$ $$T_{.1.}$$
$$B_{2}$$ $$T_{12.}$$ $$T_{22.}$$ $$\cdots$$ $$T_{l2.}$$ $$T_{.2.}$$
$$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$
$$B_{m}$$ $$T_{1m.}$$ $$T_{2m.}$$ $$\cdots$$ $$T_{lm.}$$ $$T_{.m.}$$
합계 $$T_{1..}$$ $$T_{2..}$$ $$\cdots$$ $$T_{l..}$$ $$T$$

$AC$ 2원표

인자
$C$
인자 $A$ 합계
$$A_{1}$$ $$A_{2}$$ $$\cdots$$ $$A_{l}$$
$$C_{1}$$ $$T_{1.1}$$ $$T_{2.1}$$ $$\cdots$$ $$T_{l.1}$$ $$T_{..1}$$
$$C_{2}$$ $$T_{1.2}$$ $$T_{2.2}$$ $$\cdots$$ $$T_{l.2}$$ $$T_{..2}$$
$$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$
$$C_{n}$$ $$T_{1.n}$$ $$T_{2.n}$$ $$\cdots$$ $$T_{l.n}$$ $$T_{..n}$$
합계 $$T_{1..}$$ $$T_{2..}$$ $$\cdots$$ $$T_{l..}$$ $$T$$

$BC$ 2원표

인자
$C$
인자 $B$ 합계
$$B_{1}$$ $$B_{2}$$ $$\cdots$$ $$B_{m}$$
$$C_{1}$$ $$T_{.11}$$ $$T_{.21}$$ $$\cdots$$ $$T_{.m1}$$ $$T_{..1}$$
$$C_{2}$$ $$T_{.12}$$ $$T_{.22}$$ $$\cdots$$ $$T_{.m2}$$ $$T_{..2}$$
$$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$ $$\vdots$$
$$C_{n}$$ $$T_{.1n}$$ $$T_{.2n}$$ $$\cdots$$ $$T_{.mn}$$ $$T_{..n}$$
합계 $$T_{.1.}$$ $$T_{.2.}$$ $$\cdots$$ $$T_{.m.}$$ $$T$$
$$T_{i..} = \sum_{j=1}^{m} \sum_{k=1}^{n} y_{ijk}$$ $$\overline{y}_{i..} = \frac{T_{i..}}{mn}$$
$$T_{.j.} = \sum_{i=1}^{l} \sum_{k=1}^{n} y_{ijk}$$ $$\overline{y}_{.j.} = \frac{T_{.j.}}{ln}$$
$$T_{..k} = \sum_{i=1}^{l} \sum_{j=1}^{m} y_{ijk}$$ $$\overline{y}_{..k} = \frac{T_{..k}}{lm}$$
$$T_{ij.} = \sum_{k=1}^{n} y_{ijk}$$ $$\overline{y}_{ij.} = \frac{T_{ij.}}{n}$$
$$T_{i.k} = \sum_{j=1}^{m} y_{ijk}$$ $$\overline{y}_{i.k} = \frac{T_{i.k}}{m}$$
$$T_{.jk} = \sum_{i=1}^{l} y_{ijk}$$ $$\overline{y}_{.jk} = \frac{T_{.jk}}{l}$$
$$T = \sum_{i=1}^{l} \sum_{j=1}^{m} \sum_{k=1}^{n} y_{ijk}$$ $$\overline{\overline{y}} = \frac{T}{lmn} = \frac{T}{N}$$
$$N = lmn$$ $$CT = \frac{T^{2}}{lmn} = \frac{T^{2}}{N}$$

제곱합

개개의 데이터 $y_{ijk}$와 총평균 $\overline{\overline{y}}$의 차이는 다음과 같이 7부분으로 나뉘어진다.

$$\begin{displaymath}\begin{split} (y_{ijk}-\overline{\overline{y}}) &= (\overline{y}_{i..} - \overline{\overline{y}}) + (\overline{y}_{.j.} - \overline{\overline{y}}) + (\overline{y}_{..k} - \overline{\overline{y}}) \\ &+ (\overline{y}_{ij.} - \overline{y}_{i..} - \overline{y}_{.j.} + \overline{\overline{y}}) + (\overline{y}_{i.k} - \overline{y}_{i..} - \overline{y}_{..k} + \overline{\overline{y}}) + (\overline{y}_{.jk} - \overline{y}_{.j.} - \overline{y}_{..k} + \overline{\overline{y}}) \\ &+ (y_{ijk} - \overline{y}_{ij.} - \overline{y}_{i.k} - \overline{y}_{.jk} + \overline{y}_{i..} + \overline{y}_{.j.} + \overline{y}_{..k} - \overline{\overline{y}}) \end{split}\end{displaymath}$$ 양변을 제곱한 후에 모든 $i, \ j, \ k$에 대하여 합하면 아래의 등식을 얻을 수 있다.

$$\begin{displaymath}\begin{split} \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{ijk}-\overline{\overline{y}})^{2} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{i..} - \overline{\overline{y}})^{2} + \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{.j.} - \overline{\overline{y}})^{2} + \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{..k} - \overline{\overline{y}})^{2} \\ &+ \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{ij.} - \overline{y}_{i..} - \overline{y}_{.j.} + \overline{\overline{y}})^{2} \\ &+ \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{i.k} - \overline{y}_{i..} - \overline{y}_{..k} + \overline{\overline{y}})^{2} \\ &+ \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{.jk} - \overline{y}_{.j.} - \overline{y}_{..k} + \overline{\overline{y}})^{2} \\ &+ \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{ijk} - \overline{y}_{ij.} - \overline{y}_{i.k} - \overline{y}_{.jk} + \overline{y}_{i..} + \overline{y}_{.j.} + \overline{y}_{..k} - \overline{\overline{y}})^{2} \end{split}\end{displaymath}$$ 위 식에서 왼쪽 항은 총변동 $S_{T}$이고, 오른쪽 항은 차례대로 $A$의 [[변동]], $B$의 [[변동]], $C$의 [[변동]], $A, \ B$의 [[교호작용]]의 변동, $A, \ C$의 [[교호작용]]의 변동, $B, \ C$의 [[교호작용]]의 변동, [[오차변동]]인 $S_{A}$, $S_{B}$, $S_{C}$, $S_{A \times B}$, $S_{A \times C}$, $S_{B \times C}$, $S_{E}$가 된다.

$$\begin{displaymath}\begin{split} S_{T} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{ijk}-\overline{\overline{y}})^{2} \\ &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}y_{ijk}^{ \ 2} - CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{A} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{i..}-\overline{\overline{y}})^{2} \\ &= \sum_{i=1}^{l}\frac{T_{i..}^{ \ 2}}{mn}-CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{B} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{.j.}-\overline{\overline{y}})^{2} \\ &= \sum_{j=1}^{m}\frac{T_{.j.}^{ \ 2}}{ln}-CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{C} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{..k}-\overline{\overline{y}})^{2} \\ &= \sum_{k=1}^{n}\frac{T_{..k}^{ \ 2}}{lm}-CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{A \times B} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{ij.}-\overline{y}_{i..}-\overline{y}_{.j.}+\overline{\overline{y}})^{2} \\ &= S_{AB} - S_{A} - S_{B} \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{AB} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{ij.}-\overline{\overline{y}})^{2} \\ &= \sum_{i=1}^{l}\sum_{j=1}^{m} \frac{T_{ij.}^{ \ 2}}{n} -CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{A \times C} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{i.k}-\overline{y}_{i..}-\overline{y}_{..k}+\overline{\overline{y}})^{2} \\ &= S_{AC} - S_{A} - S_{C} \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{AC} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{i.k}-\overline{\overline{y}})^{2} \\ &= \sum_{i=1}^{l}\sum_{k=1}^{n} \frac{T_{i.k}^{ \ 2}}{m} -CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{B \times C} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{.jk}-\overline{y}_{.j.}-\overline{y}_{..k}+\overline{\overline{y}})^{2} \\ &= S_{BC} - S_{B} - S_{C} \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{BC} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(\overline{y}_{.jk}-\overline{\overline{y}})^{2} \\ &= \sum_{j=1}^{m}\sum_{k=1}^{n} \frac{T_{.jk}^{ \ 2}}{l} -CT \end{split}\end{displaymath}$$ $$\begin{displaymath}\begin{split} S_{E} &= \sum_{i=1}^{l}\sum_{j=1}^{m}\sum_{k=1}^{n}(y_{ijk}-\overline{y}_{ij.}-\overline{y}_{i.k}-\overline{y}_{.jk}+\overline{y}_{i..}+\overline{y}_{.j.}+\overline{y}_{..k}-\overline{\overline{y}})^{2} \\ &= S_{T}-(S_{A}+S_{B}+S_{C}+S_{A \times B}+S_{A \times C}+S_{B \times C}) \end{split}\end{displaymath}$$ ===== 자유도 ===== $$\nu_{A}=l-1$$ $$\nu_{B}=m-1$$ $$\nu_{C}=n-1$$ $$\nu_{A \times B}=\nu_{A} \times \nu_{B}=(l-1)(m-1)$$ $$\nu_{A \times C}=\nu_{A} \times \nu_{C}=(l-1)(n-1)$$ $$\nu_{B \times C}=\nu_{B} \times \nu_{C}=(m-1)(n-1)$$ $$\nu_{E}=\nu_{T}-(\nu_{A}+\nu_{B}+\nu_{C}+\nu_{A \times B}+\nu_{A \times C}+\nu_{B \times C})=(l-1)(m-1)(n-1)$$ $$\nu_{T}=lmn-1=N-1$$ ===== 평균제곱 ===== $$V_{A}=\frac{S_{A}}{\nu_{A}}$$ $$V_{B}=\frac{S_{B}}{\nu_{B}}$$ $$V_{C}=\frac{S_{C}}{\nu_{C}}$$ $$V_{A \times B}=\frac{S_{A \times B}}{\nu_{A \times B}}$$ $$V_{AB}=\frac{S_{AB}}{\nu_{AB}}$$ $$V_{A \times C}=\frac{S_{A \times C}}{\nu_{A \times C}}$$ $$V_{AC}=\frac{S_{AC}}{\nu_{AC}}$$ $$V_{B \times C}=\frac{S_{B \times C}}{\nu_{B \times C}}$$ $$V_{BC}=\frac{S_{BC}}{\nu_{BC}}$$ $$V_{E}=\frac{S_{E}}{\nu_{E}}$$ ===== 평균제곱의 기대값 ===== $$E(V_{A})=\sigma_{E}^{ \ 2} +mn \sigma_{A}^{ \ 2}$$ $$E(V_{B})=\sigma_{E}^{ \ 2} +ln \sigma_{B}^{ \ 2}$$ $$E(V_{C})=\sigma_{E}^{ \ 2} +lm \sigma_{C}^{ \ 2}$$ $$E(V_{A \times B})=\sigma_{E}^{ \ 2} +n \sigma_{A \times B}^{ \ 2}$$ $$E(V_{A \times C})=\sigma_{E}^{ \ 2} +m \sigma_{A \times C}^{ \ 2}$$ $$E(V_{B \times C})=\sigma_{E}^{ \ 2} +l \sigma_{A \times B}^{ \ 2}$$ $$E(V_{E})=\sigma_{E}^{ \ 2}$$ ===== 분산분석표 ===== ^ [[요인]] ^ [[제곱합]]\\ $SS$ ^ [[자유도]]\\ $DF$ ^ [[평균제곱]]\\ $MS$ ^ $E(MS)$ ^ $F_{0}$ ^ [[기각치]] ^ [[순변동]]\\ $S\acute{}$ ^ [[기여율]]\\ $\rho$ |

$$A$$ $$S_{_{A}}$$ $$\nu_{_{A}}=l-1$$ $$V_{_{A}}=S_{_{A}}/\nu_{_{A}}$$ $$\sigma_{_{E}}^{ \ 2}+mn \ \sigma_{_{A}}^{2}$$ $$V_{_{A}}/V_{_{E}}$$ $$F_{1-\alpha}(\nu_{_{A}} \ , \ \nu_{_{E}})$$ $$S_{_{A}}\acute{}$$ $$S_{_{A}}\acute{}/S_{_{T}}$$
$$B$$ $$S_{_{B}}$$ $$\nu_{_{B}}=m-1$$ $$V_{_{B}}=S_{_{B}}/\nu_{_{B}}$$ $$\sigma_{_{E}}^{ \ 2}+ln \ \sigma_{_{B}}^{2}$$ $$V_{_{B}}/V_{_{E}}$$ $$F_{1-\alpha}(\nu_{_{B}} \ , \ \nu_{_{E}})$$ $$S_{_{B}}\acute{}$$ $$S_{_{B}}\acute{}/S_{_{T}}$$
$$C$$ $$S_{_{C}}$$ $$\nu_{_{C}}=n-1$$ $$V_{_{C}}=S_{_{C}}/\nu_{_{C}}$$ $$\sigma_{_{E}}^{ \ 2}+lm \ \sigma_{_{C}}^{2}$$ $$V_{_{C}}/V_{_{E}}$$ $$F_{1-\alpha}(\nu_{_{C}} \ , \ \nu_{_{E}})$$ $$S_{_{C}}\acute{}$$ $$S_{_{C}}\acute{}/S_{_{T}}$$
$$A \times B$$ $$S_{_{A \times B}}$$ $$\nu_{_{A \times B}}=(l-1)(m-1)$$ $$V_{_{A \times B}}=S_{_{A \times B}}/\nu_{_{A \times B}}$$ $$\sigma_{_{E}}^{ \ 2}+n \ \sigma_{_{A \times B}}^{2}$$ $$V_{_{A \times B}}/V_{_{E}}$$ $$F_{1-\alpha}(\nu_{_{A \times B}} \ , \ \nu_{_{E}})$$ $$S_{_{A \times B}}\acute{}$$ $$S_{_{A \times B}}\acute{}/S_{_{T}}$$
$$A \times C$$ $$S_{_{A \times C}}$$ $$\nu_{_{A \times C}}=(l-1)(n-1)$$ $$V_{_{A \times C}}=S_{_{A \times C}}/\nu_{_{A \times C}}$$ $$\sigma_{_{E}}^{ \ 2}+m \ \sigma_{_{A \times C}}^{2}$$ $$V_{_{A \times C}}/V_{_{E}}$$ $$F_{1-\alpha}(\nu_{_{A \times C}} \ , \ \nu_{_{E}})$$ $$S_{_{A \times C}}\acute{}$$ $$S_{_{A \times C}}\acute{}/S_{_{T}}$$
$$B \times C$$ $$S_{_{B \times C}}$$ $$\nu_{_{B \times C}}=(m-1)(n-1)$$ $$V_{_{B \times C}}=S_{_{B \times C}}/\nu_{_{B \times C}}$$ $$\sigma_{_{E}}^{ \ 2}+l \ \sigma_{_{B \times C}}^{2}$$ $$V_{_{B \times C}}/V_{_{E}}$$ $$F_{1-\alpha}(\nu_{_{B \times C}} \ , \ \nu_{_{E}})$$ $$S_{_{B \times C}}\acute{}$$ $$S_{_{B \times C}}\acute{}/S_{_{T}}$$
$$E$$ $$S_{_{E}}$$ $$\nu_{_{E}}=(l-1)(m-1)(n-1)$$ $$V_{_{E}}=S_{_{E}}/\nu_{_{E}}$$ $$\sigma_{_{E}}^{ \ 2}$$ $$S_{_{E}}\acute{}$$ $$S_{_{E}}\acute{}/S_{_{T}}$$
$$T$$ $$S_{_{T}}$$ $$\nu_{_{T}}=lmn-1$$ $$S_{_{T}}$$ $$1$$

분산분석

인자 $A$에 대한 분산분석

$$F_{0}=\frac{V_{_{A}}}{V_{_{E}}}$$

기각역 : $F_{0} > F_{1-\alpha}(\nu_{_{A}},\nu_{_{E}})$


인자 $B$에 대한 분산분석

$$F_{0}=\frac{V_{_{B}}}{V_{_{E}}}$$

기각역 : $F_{0} > F_{1-\alpha}(\nu_{_{B}},\nu_{_{E}})$


인자 $C$에 대한 분산분석

$$F_{0}=\frac{V_{_{C}}}{V_{_{E}}}$$

기각역 : $F_{0} > F_{1-\alpha}(\nu_{_{C}},\nu_{_{E}})$


인자 $A , \ B$의 교호작용 대한 분산분석

$$F_{0}=\frac{V_{_{A \times B}}}{V_{E}}$$

기각역 : $F_{0} > F_{1-\alpha}(\nu_{_{A \times B}},\nu_{_{E}})$


인자 $A , \ C$의 교호작용 대한 분산분석

$$F_{0}=\frac{V_{_{A \times C}}}{V_{E}}$$

기각역 : $F_{0} > F_{1-\alpha}(\nu_{_{A \times C}},\nu_{_{E}})$


인자 $B , \ C$의 교호작용 대한 분산분석

$$F_{0}=\frac{V_{_{B \times C}}}{V_{E}}$$

기각역 : $F_{0} > F_{1-\alpha}(\nu_{_{B \times C}},\nu_{_{E}})$

각 수준의 모평균의 추정 (주효과만이 유의한 경우)

주효과인 인자 $A, B, C$만이 유의한 경우 교호작용들이 모두 오차항에 풀링되어 버린다.

(단, $S_{E}\acute{}=S_{E}+S_{A \times B}+S_{A \times C}+S_{B \times C}, \ \nu_{E}\acute{}=\nu_{E}+\nu_{A \times B}+\nu_{A \times C}+\nu_{B \times C}, \ V_{E}\acute{}=S_{E}\acute{}/\nu_{E}\acute{}$이다.)

인자 $A$의 모평균에 관한 추정

$i$ 수준에서의 모평균 $\mu(A_{i})$의 점추정

$$\hat{\mu}(A_{i})=\widehat{\mu + a_{i}} = \overline{y}_{i..}$$

$i$ 수준에서의 모평균 $\mu(A_{i})$의 $100(1-\alpha) \% $ 신뢰구간은 아래와 같다.

$\hat{\mu}(A_{i})= \left( \overline{y}_{i..} - t_{\alpha/2}(\nu_{E}\acute{} \ ) \sqrt{\frac{V_{E}\acute{}}{mn}} \ , \ \overline{y}_{i..} + t_{\alpha/2}(\nu_{E}\acute{} \ ) \sqrt{\frac{V_{E}\acute{}}{mn}} \right)$


인자 $B$의 모평균에 관한 추정

$j$ 수준에서의 모평균 $\mu(B_{j})$의 점추정

$\hat{\mu}(B_{j})=\widehat{\mu + b_{j}} = \overline{y}_{.j.}$

$j$ 수준에서의 모평균 $\mu(B_{j})$의 $100(1-\alpha) \% $ 신뢰구간은 아래와 같다.

$\hat{\mu}(B_{j})= \left( \overline{y}_{.j.} - t_{\alpha/2}(\nu_{E}\acute{} \ ) \sqrt{\frac{V_{E}\acute{}}{ln}} \ , \ \overline{y}_{.j.} + t_{\alpha/2}(\nu_{E}\acute{} \ ) \sqrt{\frac{V_{E}\acute{}}{ln}} \right)$


인자 $C$의 모평균에 관한 추정

$k$ 수준에서의 모평균 $\mu(C_{k})$의 점추정

$$\hat{\mu}(C_{k})=\widehat{\mu + c_{k}} = \overline{y}_{..k}$$

$k$ 수준에서의 모평균 $\mu(C_{k})$의 $100(1-\alpha) \% $ 신뢰구간은 아래와 같다.

$$\hat{\mu}(C_{k})= \left( \overline{y}_{..k} - t_{\alpha/2}(\nu_{E}\acute{} \ ) \sqrt{\frac{V_{E}\acute{}}{lm}} \ , \ \overline{y}_{..k} + t_{\alpha/2}(\nu_{E}\acute{} \ ) \sqrt{\frac{V_{E}\acute{}}{lm}} \right)$$


인자 $A$와 $B$ 그리고 $C$의 모평균에 관한 추정

$A$ 인자의 $i$ 수준과 $B$ 인자의 $j$ 수준, $C$ 인자의 $k$ 수준에서의 모평균 $\mu(A_{i}B_{j}C_{k})$의 점추정

$\hat{\mu}(A_{i}B_{j}C_{k})=\widehat{\mu+a_{i}+b_{j}+c_{k}}=\overline{y}_{i..} + \overline{y}_{.j.} + \overline{y}_{..k} - 2 \overline{\overline{y}}$

$A$ 인자의 $i$ 수준과 $B$ 인자의 $j$ 수준, $C$ 인자의 $k$ 수준에서의 모평균 $\mu(A_{i}B_{j}C_{k})$$ 의&nbsp&nbsp $$100(1-\alpha) \% $ 신뢰구간은 아래와 같다.

$\hat{\mu}(A_{i}B_{j}C_{k})= \left( (\overline{y}_{i..} + \overline{y}_{.j.} + \overline{y}_{..k} - 2\overline{\overline{y}}) - t_{\alpha/2}(\nu_{E}\acute{} \ )\sqrt{\frac{V_{E}\acute{}}{n_{e}}} \ , \ (\overline{y}_{i..} + \overline{y}_{.j.} + \overline{y}_{..k} - 2\overline{\overline{y}}) - t_{\alpha/2}(\nu_{E}\acute{} \ )\sqrt{\frac{V_{E}\acute{}}{n_{e}}} \right)$

단, $n_{e}$는 유효반복수이고 $n_{e} = \frac{lmn}{l+m+n-2}$이다.