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Analysis of Variance Compare k Quant Populations k ≥ 2

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Presentation on theme: "Analysis of Variance Compare k Quant Populations k ≥ 2"— Presentation transcript:

1 Analysis of Variance Compare k Quant Populations k ≥ 2
Case I: Case II: + + + + + + + + + + + + + + + + + + + + + + + +

2 k – Treatment Groups k ≥ 2 nj = # obs in the jth group
X11 X12 X13 …. X1k X21 X22 X23 …. X2k N = ∑nj …. Treatment Xn11 Xn22 Xn33 …. Xnkk Total T1 T2 T3 …. Tk Xj = ∑Xij/nj Mean X1 X2 X3 …. Xk Model: Xij = µj + eij Random Error - E(eij) = 0 V(eij) = σ2 Overall Mean µ = ∑µj/k All Means Equal: Treatment Effect – τj = (µj - µ) Means Differ:

3 Error – eij = (Xij - µj) Grand Mean –
X is estimator for µ Xj is estimator for µj Consider the Total Variation of X: Total Variation = Within Group Between Groups Total = Error Treatments Total = Residual Model TSS = SSE SST

4 Analysis of Variance (ANOVA) Table
Source df SS MS Expected Treatments k MST = SST/k-1 σ2 + V(τ) Error N-k MSE = SSE/N-k σ2 Total N-1 F Ratio - F = MST/MSE Means Equal : F = 1 Means Differ : F >> 1 H0: All Means Equal HA: Not All Equal R: F > Fα(k-1,N-k) F = MST/MSE

5 Example 1: 1 2 3

6 Machine Formulas: Example 2: 1 2 16 24 20 21 18 22 14 25

7

8 Multiple Comparisons - Determine which Means Differ
A) Group Mean Interval Estimate 1) 2) 3)

9 B) Difference of Two Means
(µ2 - µ1) (µ1 - µ3) (µ2 - µ3)

10 d - e ≤ D ≤ d + e Note: If d > e then A Diff; If d ≤ e then No Diff
Fisher LSD Method (Least Significant Difference) Groups Contrast LSD Decision

11 Tukey HSD Method (Honestly Significant Difference)
Groups Contrast HSD Decision

12 Example 3: Unequal Group Size
21 26

13 Multiple Comparisons LSD Method: Groups d LSD Decision Tukey - HSD Method: Groups d HSD Decision

14 2-Way ANOVA - Complete Randomized Block Design
k Column Groups (Treatments) b Row Groups (Blocks) 1 2 3 … k Sum Ave 1 X11 X12 X13 … X1k B1 R1 2 X21 X22 X23 … X2k B2 R2 b Xb1 Xb2 Xb3 … Xbk Bk Rk Sum T1 T2 T3 … Tk Ave C1 C2 C3 … Ck X

15 Partition the Total Variation of X
Total = Error Blocks Treatments TSS = SSE SSB SST

16 2-Way ANOVA Table: Source df Sum of Squares Mean Square Treatments k MST = SST/k-1 Blocks b MSB = SSB/b-1 Error N-k-b MSE = SSE/N-k-b+1 Total N-1 There are Two Hypothesis Tests: 1) Equal Column (Treatment) Means FTreat = MST/MSE 2) Equal Row (Block) Means FBlock = MSB/MSE

17 Example 4: 2-Way Example Package Ad A B C TV News

18 Multiple Comparisons:

19 Machine Formulas:

20 Example 5: 2-Way is an Extension of Matched Pair Data
Child Before After A B C D 89 93 E 85 89

21 Multiple Comparisons:

22 Example 6: Interaction Effect
Temp A B C Hot Warm Cold

23 Multiple Comparisons:

24 Example 7: Interaction Example
Child Placebo Ritalin Hyper Normal


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