Two-Factor ANOVA model with n = 1 (no replication)

1. 1. Two-factor ANOVA model with n = 1 (no replication)
   1. 1.1 Two-factor model without interaction
      1. Sum of squares
      2. ANOVA Table
      3. F tests (for main effects)
      4. Estimation of means
   2. 1.2 Example: Insurance
      1. Sum of squares:
   3. 1.3 Checking for the presence of interaction: Tukey's test for additivity
      1. Estimation of \(D\)
2. Contributors

1. Two-factor ANOVA model with n = 1 (no replication)

• For some studies, there is only one replicate per treatment, i.e., n = 1.
• ANOVA model for two-factor studies need to be modified, since
  - the degrees of freedom associated with \(SSE\) will be \((n - 1)ab = 0\);
  - thus the error variance \(\sigma^2\) can not be estimated by \(SSE\) anymore.
• Idea: make the model simpler by assuming the two factors do not interact with each other. Validity of this assumption needs to be checked.

1.1 Two-factor model without interaction

With n = 1.

• Model equation:

\[Y_{ij} = \mu_{..} + \alpha_i + \beta_j + \epsilon_{ij}, i = 1, ..., a, j = 1, ..., b.\]

• Identifiability constraints:

\[\sum_{i=1}^{a}\alpha_i = 0, \sum_{j=1}^{b}\beta_j = 0.\]

• Distributional assumptions: \(\epsilon_{ij}\) are i.i.d. \(N(0,\sigma^2)\)

Sum of squares

Interaction sum of squares now plays the role of error sum of squares.

\[SSAB = n\sum_{i=1}^{a} \sum_{j=1}^{b}(\overline{Y}_{ij.} - \overline{Y}_{i..} - \overline{Y}_{.j.} + \overline{Y}_{...})^2 = \sum_{i=1}^{a} \sum_{j=1}^{b}(\overline{Y}_{ij} - \overline{Y}_{i.} - \overline{Y}_{.j} + \overline{Y}_{..})^2 \]

\(MSAB = \frac{SSAB}{(a-1)(b-1)}\) since \(d.f.(SSAB) = (a-1)(b-1)\).

• In the general two-factor ANOVA model (when n = 1),

\[E(MSAB) = \sigma^2 + \frac{\sum_{i=1}^{a}\sum_{j=1}^{b} (\alpha\beta)^2_{ij}}{(a-1)(b-1)}\]

• Under the model without interaction: \(E(MSAB) = \sigma^2\)
• Thus \(MSAB\) can be used to estimate \(\sigma^2\).

ANOVA Table

ANOVA table for two-factor model without interaction and \(n=1\)

Source of Variation	SS	df	MS
Factor A	\(SSA = b\sum_i(\overline{Y}_{i.} - \overline{Y}_{..})^2\)	\(a - 1\)	\(MSA\)
Factor B	\(SSB = a\sum_j(\overline{Y}_{.j} - \overline{Y}_{..})^2\)	\(b - 1\)	\(MSB\)
Error	\(SSAB = \sum_{i=1}^{a}\sum_{j=1}^{b}(\overline{Y}_{ij} - \overline{Y}_{i.} - \overline{Y}_{.j} + \overline{Y}_{..})^2\)	\((a - 1)(b - 1)\)	\(MSAB\)
Total	\(SSTO = \sum_{i=1}^{a}\sum_{j=1}^{b}(\overline{Y}_{ij} - \overline{Y}_{..})^2\)	\(ab - 1\)

Expected mean squares (under no interaction):

\[E(MSA) = \sigma^2 + \frac{b\sum_{i=1}^{a}\alpha_i^2}{a - 1}, E(MSB) = \sigma^2 + \frac{a\sum_{j=1}^{b}\beta_j^2}{b - 1}, E(MSAB) = \sigma^2\]

F tests (for main effects)

Test factor A main effects: \(H_o: \alpha_1 = ... = \alpha_a = 0\) vs. \(H_a:\) not all \(\alpha_i\)'s are equal to zero.

• \(F_A^* = \frac{MSA}{MSAB} ~ F_{a - 1, (a - 1)(b - 1)}\) under \(H_o\).
• Reject \(H_o\) at level of significance \(\alpha\) if observed \(F_A^* > F(1 - \alpha; a - 1, (a - 1)(b - 1))\).

Test factor B main effects: \(H_o: \beta_1 = ... = \beta_b = 0\) vs. \(H_a:\) not all \(\beta_j\)'s are equal to zero.

• \(F_B^* = \frac{MSB}{MSAB} ~ F_{b - 1, (a - 1)(b - 1)}\) under \(H_o\).
• Reject \(H_o\) at level of significance \(\alpha\) if observed \(F_B^* > F(1 - \alpha; b - 1, (a - 1)(b - 1))\).

Estimation of means

Estimation of factor level means \(\mu_{i.}\)'s , \(\mu_{.j}\)'s.

• Proceed as before, viz., use the unbiased estimator \(\overline{Y}_{i.}\) for \(\mu_{i.}\) and \(\overline{Y}_{.j}\) for \(\mu_{.j}\), but replace \(MSE\) by \(MSAB\) and use the degrees of freedom of \(MSAB\), that is \((a - 1)(b - 1)\). Thus, estimated standard errors:

\[s(\overline{Y}_{i.}) = \sqrt{\frac{MSAB}{b}}, s(\overline{Y}_{.j}) = \sqrt{\frac{MSAB}{a}}.\]

Estimation of treatment means \(\mu_{ij}\)'s.

• \(\mu_{ij} = E(Y_{ij}) = \mu_{..} + \alpha_i + \beta_j = \mu_{i.} + \mu_{.j} - \mu_{..}\)
  Thus, an unbiased estimator: \(\widehat{\mu}_{ij} = \overline{Y}_{i.} + \overline{Y}_{.j} - \overline{Y}_{..}\)
  Estimated standard error:

\[s(\widehat{\mu}_{ij}) = \sqrt{MSAB(\frac{1}{b} + \frac{1}{a} - \frac{1}{ab})} = \sqrt{MSAB(\frac{a + b - 1}{ab})}\]

1.2 Example: Insurance

An analyst studied the premium for auto insurance charged by an insurance company in six cities. The six cities were selected to represent different sizes (Factor A: small, medium, large) and differentregions of the state (Factor B: east, west). There is only one city for each combination of size and region. The amounts of premiums charged for a specific type of coverage in a given risk category for each of the six cities are given in the following table.

Table 1: Numbers in parentheses are \(\widehat{\mu}_{ij} = \overline{Y}_{i.} + \overline{Y}_{.j} - \overline{Y}_{..}\)

		Factor B
	East	West
Factor A	Small 140(135) Medium 210(210) Large 220(225)	100(105) 180(180) 200(195)	\(\overline{Y}_{1.} = 120\) \(\overline{Y}_{2.} = 195\) \(\overline{Y}_{3.} = 210\)
	\(\overline{Y}_{.1} = 190\)	\(\overline{Y}_{.2} = 160\)	\(\overline{Y}_{..} = 175\)

Interaction plot based on the treatment sample means \(Y_{ij}\)'s: no strong interactions.

Sum of squares:

• Here \(a = 3\), \(b = 2\), \(n = 1\).
• \(SSA = 2[(120 - 175)^2 + (195 - 175)^2 + (210 - 175)^2] = 9300.\).
• \(SSB = 3[(190 - 175)^2 + (160 - 175)^2] = 1350\).
• \(SSAB = (140 - 120 - 190 + 175)^2 + ... + (200 - 210 - 160 + 175)^2 = 100\).
• \(SSTO = SSA + SSB + SSAB = 10750\).
  Hypothesis testing:
• Test \(H_o: \mu_{1.} = \mu_{2.} = \mu_{3.}\) (equivalently, \(H_o: \alpha_1 = \alpha_2 = \alpha_3 = 0\)) at level 0.05.
  

  Table 2: ANOVA Table for Insurance example

  Source of Variation	SS	df	MS
  Factor A	\(SSA = 9300\)	\(a - 1 = 2\)	\(MSA = 4650\)
  Factor B	\(SSB = 1350\)	\(b - 1 = 1\)	\(MSB = 1350\)
  Error	\(SSAB = 100\)	\((a - 1)(b - 1) = 2\)	\(MSAB = 50\)
  Total	\(SSTO = 10750\)	\(ab - 1 = 5\)

\(F_A^* = \frac{MSA}{MSAB} = \frac{4650}{50} = 93\) and \(F(0.95; 2, 2) = 19\). Thus reject \(H_o\) at level 0.05.

• Estimation of \(\mu_{ij}\): e.g.,
  \(\widehat{\mu}_{11} = \overline{Y}_{1.} + \overline{Y}_{.1} - \overline{Y}_{..} = 120 + 190 - 175 = 135\).
• Estimation of \(\mu_{i.}\) and \(\mu_{.j}\): e.g.,
  \(\widehat{\mu}_{1.} = \overline{Y}_{1.} = 120\).
  \(s(\overline{Y}_{1.}) = \sqrt{\frac{MSAB}{b}} = \sqrt{\frac{50}{2}} = 5\).
  The 95% C.I. for \(\mu_{1.}\) is:
  \[\overline{Y}_{1.} \pm t(0.975; 2) * s(\overline{Y}_{1.}) = 120 \pm 4.3*5 = (98.5, 141.5).\]

1.3 Checking for the presence of interaction: Tukey's test for additivity

For a two-factor study with \(n = 1\), decide whether or not the two factors are interacting.

• In the no-interaction model, we assume that all \((\alpha\beta)_{ij} = 0\).
• Idea: use a less severe restriction on the interaction effects, by assuming
  \[(\alpha\beta)_{ij} = D\alpha_i\beta_j, i = 1, ... , a, j = 1, ... , b,\]
  where \(D\) is an unknown parameter.
• The model becomes:
  \[Y_{ij} = \mu_{..} + \alpha_i + \beta_j + D\alpha_i\beta_j + \epsilon_{ij}, i = 1, ... , a, j = 1, ..., b,\]
  under the constraints that
  \[\sum_{i = 1}^{a}\alpha_i = \sum_{j = 1}^{b}\beta_j = 0.\]

Estimation of \(D\)

• Multiply \(\alpha_i\beta_j\) on both sides of the equation:
  \[\alpha_i\beta_jY_{ij} = \mu_{..}\alpha_i\beta_j + \alpha_i^2\beta_j + \alpha_i\beta_j^2 + D\alpha_i^2\beta_j^2 + \epsilon_{ij}\alpha_i\beta_j\]
• Sum over all pairs (i, j):
  \[\sum_{i=1}^{a}\sum_{j=1}^{b}\alpha_i\beta_jY_{ij}=D\sum_{i=1}^{a}\sum_{j=1}^{b}\alpha_i^2\beta_j^2 + \sum_{i=1}^{a}\sum_{j=1}^{b}\epsilon_{ij}\alpha_i\beta_j\]
• Then
  \[\widetilde{D} := \frac{\sum_{i=1}^{a}\sum_{j=1}^{b}\alpha_i\beta_jY_{ij}}{(\sum_{i=1}^{a}\alpha_i^2)(\sum_{j=1}^{b}\beta_j^2)} \approx D\]
• We have the following estimates:
  \[\widehat{\alpha}_i = \overline{Y}_{i.} - \overline{Y}_{..}, \widehat{\beta}_j = \overline{Y}_{.j} - \overline{Y}_{..}\]
• Thus, an estimator of \(D\) (which is also the least squares and the maximum likelihood estimator) is given by
  \[\widehat{D} = \frac{\sum_{i=1}^{a}\sum_{j=1}^b ( \overline{Y}_{i.} - \overline{Y}_{..})( \overline{Y}_{.j} - \overline{Y}_{..})Y_{ij}}{(\sum_{i=1}^{a}( \overline{Y}_{i.} - \overline{Y}_{..})^2)(\sum_{j=1}^{b}( \overline{Y}_{.j} - \overline{Y}_{..})^2)}.\]

ANOVA decomposition

\[SSTO = SSA + SSB + SSAB* + SSRem*.\]

• Interaction sum of squares
  \[SSAB* = \sum_{i=1}^{a}\sum_{j=1}^{b}\widehat{D}^2\widehat{\alpha}_i^2\widehat{\beta}_j^2 = \frac{(\sum_{i=1}^{a}\sum_{j=1}^b ( \overline{Y}_{i.} - \overline{Y}_{..})( \overline{Y}_{.j} - \overline{Y}_{..})Y_{ij})^2}{(\sum_{i=1}^{a}( \overline{Y}_{i.} - \overline{Y}_{..})^2)(\sum_{j=1}^{b}( \overline{Y}_{.j} - \overline{Y}_{..})^2)}\]
• Remainder sum of squares
  \[SSREM^* = SSTO - SSA - SSB - SSAB^*\]
• Decomposition of degrees of freedom
  \[df(SSTO) = df(SSA) + df(SSB) + df(SSAB^*) + df(SSRem^*)\]
  \[ab - 1 = (a - 1) + (b - 1) + 1 + (ab - a - b)\]
• Tukey's one degree of freedom test for additivity: \(H_o: D = 0\) (i.e., no interaction) vs. \(H_a: D \neq 0\).
• \(F\) ratio \(F_{Tukey}^{*} = \frac{SSAB^*/1}{SSRem^*/(ab - a - b)}\sim F_{1, ab - a - b}\) under \(H_o\).
• Decision rule: reject \(H_o: D = 0\) at level of significance \(\alpha\) if \(F_{Tukey}^{*} > F(1 - \alpha; 1, ab - a - b)\).

Example: Insurance

• \(\sum_{ij}(\overline{Y}_{i.} - \overline{Y}_{..})( \overline{Y}_{.j} - \overline{Y}_{..})Y_{ij} = -13500.\)
• \(\sum_{i=1}^{a}( \overline{Y}_{i.} - \overline{Y}_{..})^2 = 4650\), and \(\sum_{j=1}^{b}( \overline{Y}_{.j} - \overline{Y}_{..})^2 = 450.\)
• \(SSAB^* = \frac{(-13500)^2}{4650 * 450} = 87.1.\)
• \(SSRem^* = 10750 - 9300 - 1350 - 87.1 = 12.9.\)
• \(ab - a - b = 3*2 - 3 - 2 = 1.\)
• \(F\)-ratio for Tukey's test:
  \[F_{Tukey}^{*} = \frac{SSAB^*/1}{SSRem^*/1} = \frac{87.1}{12.9} = 6.8.\]
• When \(\alpha = 0.05, F(0.95; 1, 1) = 161.4 > 6.8.\)
• Thus, we can not reject \(H_o: D = 0\) at the 0.05 level, and we conclude that there is no significant interaction between the two factors.
• Indeed, the p-value is \(p = P(F_{1,1} > 6.8) = 0.23\) which is not at all significant.

Contributors

• Scott Brunstein (UCD)
• Debashis Paul (UCD)