1. Chapter 12: One-Way ANalysis Of Variance (ANOVA) http://www.luchsinger-mathematics.ch/Var_Reduction.jpg 1

2. 12.1: Inference for One-Way ANOVA - Goals Provide a description of the underlying idea of ANOVA (how we use variance to determine if means are different) Be able to construct the ANOVA table. Be able to perform the significance test for ANOVA and interpret the results. Be able to state the assumptions for ANOVA and use diagnostics plots to determine when they are valid or not. 2

3. ANOVA: Terms Factor: What differentiates the populations Level or group: the number of different populations One-way ANOVA is used for situations in which there is only one factor, or only one way to classify the populations of interest. Two-way ANOVA is used to analyze the effect of two factors. 3

4. Examples: ANOVA In each of the following situations, what is the factor and how many levels are there? 1)Do five different brands of gasoline have an effect on automobile efficiency? 2)Does the type of sugar solution (glucose, sucrose, fructose, mixture) have an effect on bacterial growth? 3)Does the hardwood concentration in pulp (%) have an effect on tensile strength of bags made from the pulp? 4)Does the resulting color density of a fabric depend on the amount of dye used? 4

5. ANOVA: Graphical c) 5

6. Examples: ANOVA What are H 0 and H a in each case? 1)Do five different brands of gasoline have an effect on automobile efficiency? 2)Does the type of sugar solution (glucose, sucrose, fructose, mixture) have an effect on bacterial growth? 6

7. ANOVA: model x ij – i: group or level – I: the total number of levels – j: object number in the group – n i : total number of objects in group i  i x ij =  I +  ij DATA = FIT + RESIDUAL –  ij ~ N(0,  ) 7

8. ANOVA: model (cont) 8

9. ANOVA test statistic 9

10. ANOVA test 10

11. ANOVA test Analysis of variance compares the variation due to specific sources with the variation among individuals who should be similar. In particular, ANOVA tests whether several populations have the same means by comparing how far apart the sample means are with how much variation there is within a sample. 11

12. Formulas for Variances 12

13. Model or Groups Variance 13

14. Error Variance 14

15. Total Variance 15

16. F Distribution http://www.vosesoftware.com/ModelRiskHelp/index.htm#Distributions/ Continuous_distributions/F_distribution.htm 16

17. P-value for an upper-tailed F test shaded area=P- value = 0.05 17

18. ANOVA Table: Formulas SourcedfSSMS (Mean Square) F Model (between) I – 1 Error (within) N – I TotalN – 1 18

19. ANOVA Hypothesis test: Summary H 0 : μ 1 = μ 2 =  = μ I H a : At least one μ i is different Test statistic: P-value:P(F ≥ F test ) has a F ,dfm,dfe distribution 19

20. Conditions for ANOVA 1)We have I independent SRSs, one from each population. We measure the same response variable for each sample. 2)The ith population has a Normal distribution with unknown mean μ i. 3)All the populations have the same standard deviation σ, whose value is unknown. 20

21. ANOVA: Example A random sample of 15 healthy young men are split randomly into 3 groups of 5. They receive 0, 20, and 40 mg of the drug Paxil for one week. Then their serotonin levels are measured to determine whether Paxil affects serotonin levels. The data is on the next slide. Does Paxil affect serotonin levels in healthy young men at a significance level of 0.05? 21

22. ANOVA: Example (cont). Dose0 mg20 mg40 mg 48.6258.6068.59 49.8572.5278.28 64.2266.7282.77 62.8180.1276.53 62.5168.4472.33 22

23. ANOVA: Example (cont). Dose0 mg20 mg40 mg 48.6258.6068.59 49.8572.5278.28 64.2266.7282.77 62.8180.1276.53 62.5168.4472.33overall nini 55515 x̅ i 57.6069.2875.7067.53 sisi 7.6787.8955.460 23

24. ANOVA: Example (cont) 0.Let  1 be the population mean serotonin level for men receiving 0 mg of Paxil. Let  2 be the population mean serotonin level for men receiving 20 mg of Paxil. Let  3 be the population mean serotonin level for men receiving 40 mg of Paxil. 24

25. ANOVA: Example (cont) 1.H 0 :  1 =  2 =  3 The mean serotonin levels are the same at all 3 dosage levels [or, The mean serotonin levels are unaffected by Paxil dose] H A : at least one  I is different The mean serotonin levels of the three groups are not all equal. [or, The mean serotonin levels are affected by Paxil dose] 25

26. ANOVA: Example (cont). (NOT USED) Dose0 mg20 mg40 mg 48.6258.6068.59 49.8572.5278.28 64.2266.7282.77 62.8180.1276.53 62.5168.4472.33overall nini 55515 x̅ i 57.6069.2875.7067.53 sisi 7.6787.8955.460 (n i -1)s i 2 235.78249.32119.24604.34 n i (x̅ i - x̅̅) 2 492.5615.36333.96841.88 X 26

27. ANOVA: Example (cont) 27 0.005321 SourcedfSSMSFP-Value Model2420.948.36 Error1250.36 Total14 841.88 604.34 1446.23

28. Example: ANOVA (cont) 4.This data does give strong support (P = 0.005321) to the claim that there is a difference in serotonin levels among the groups of men taking 0, 20, and 40 mg of Paxil. This data does give strong support (P = 0.005321) to the claim that Paxil intake affects serotonin levels in young men. 28

29. 12.2: Comparing the Means - Goals State why you have to use multi-comparison methods vs. 2-sample t procedures. Be able to state when the Tukey method should be done and perform the method. Be able to state when the Dunnett method should be done. Be able to state when the Bonferroni method should be done and generally state the method. Be able to draw conclusions from the results of the multi-comparison method. 29

30. Advantages/Problems of ANOVA (more than 2 samples) Advantages – Single test – Better estimation of error Disadvantages – Which groups are different? 30

31. Which mean(s) is different? Graphics Contrasts – Planned – pp. 663 - 668 Multiple comparisons – No prior knowledge 31

32. Problems with multiple pairwise t- tests 1.Type I error 2.Estimation of the standard deviation 3.Structure in the groups 32

33. Problem with Multiple t tests 33

34. Overall Risk of Type I Error in Using Repeated t Tests at  = 0.05 34

35. Problems with multiple pairwise t- tests 1.Type I error 2.Estimation of the standard deviation 3.Structure in the groups 35

36. Simultaneous Confidence Intervals 36

37. Multiple Comparison Methods LSD (Fishers) Bonferroni Tukey Dunnet 37

38. Bonferroni Method 38

39. Other Methods 39

40. Procedure: Multiple Comparison 1.Perform the ANOVA test (obtain the ANOVA table); only continue if the results are statistically significant. 2.Select a family significance level, . 3.Select the multiple comparison methodology. 4.Calculate t**. 5.Calculate all of the confidence intervals required by the procedure. 6.Determine which ones are statistically significant. 7.Visually display the results. 8.Write a conclusion in the context of the problem. 40

41. Example: Multiple Comparison A random sample of 15 healthy young men are split randomly into 3 groups of 5. They receive 0, 20, and 40 mg of the drug Paxil for one week. Then their serotonin levels are measured to determine whether Paxil affects serotonin levels. Which dosage would provide the largest change in serotonin levels? 41

42. Example: Multiple Comparison (cont) SourcedfSSMSFP-Value Model2841.88420.948.360.005321 Error12604.3450.36 Total141446.23 Dose0 mg20 mg40 mg 48.6258.6068.59 49.8572.5278.28 64.2266.7282.77 62.8180.1276.53 62.5168.4472.33 x̅ i 57.6069.2875.70 42

43. Example: Multiple Comparison: Dunnett t** = 2.50 Therefore, dosages of both 20 mg and 40 mg of Paxil do raise serotonin levels. 43 i - jx̅ i. - x̅ j. interval 2 – 169.28 – 57.60 = 11.68(0.46, 22.9) 3 - 175.70 – 57.60 = 18.1(6.88, 29.32) 0 mg (control)20 mg40 mg 57.6069.2875.70 different from control

44. Example: Multiple Comparison: Tukey Therefore, 40 mg dosage of Paxil does raise serotonin levels, but a 20 mg dosage of Paxil does not raise serotonin levels. 44 i - jx̅ i. - x̅ j. interval 2 – 169.28 – 57.60 = 11.68(-0.285, 23.645) 3 - 175.70 – 57.60 = 18.1(6.135, 30.065) 3 – 275.70 – 69.28 = 6.42(-5.545, 18.385) 0 mg (control)20 mg40 mg 57.6069.2875.70