ANOVA TABLE

1. ANOVA TABLE Factorial Experiment Completely Randomized Design

2. Anova table for the 3 factor Experiment

3. Sum of squares entries Similar expressions for SSB , and SSC. Similar expressions for SSBC , and SSAC.

4. Sum of squares entries Finally

5. The statistical model for the 3 factor Experiment

6. Anova table for the 3 factor Experiment

7. The testing in factorial experiments • Test first the higher order interactions. • If an interaction is present there is no need to test lower order interactions or main effects involving those factors. All factors in the interaction affect the response and they interact • The testing continues with lower order interactions and main effects for factors which have not yet been determined to affect the response.

8. Examples Using SPSS

9. Example In this example we are examining the effect of • the level of protein A (High or Low) and • the source of protein B (Beef, Cereal, or Pork) on weight gains (grams) in rats. We have n = 10 test animals randomly assigned to k = 6 diets

10. The k = 6 diets are the 6 = 3×2 Level-Source combinations • High - Beef • High - Cereal • High - Pork • Low - Beef • Low - Cereal • Low - Pork

11. Table Gains in weight (grams) for rats under six diets differing in level of protein (High or Low) and s ource of protein (Beef, Cereal, or Pork) Level of Protein High Protein Low protein Source of Protein Beef Cereal Pork Beef Cereal Pork Diet 1 2 3 4 5 6 73 98 94 90 107 49 102 74 79 76 95 82 118 56 96 90 97 73 104 111 98 64 80 86 81 95 102 86 98 81 107 88 102 51 74 97 100 82 108 72 74 106 87 77 91 90 67 70 117 86 120 95 89 61 111 92 105 78 58 82 Mean 100.0 85.9 99.5 79.2 83.9 78.7 Std. Dev. 15.14 15.02 10.92 13.89 15.71 16.55

12. The data as it appears in SPSS

13. To perform ANOVA select Analyze->General Linear Model-> Univariate

14. The following dialog box appears

15. Select the dependent variable and the fixed factors Press OK to perform the Analysis

16. The Output

17. Example – Four factor experiment Four factors are studied for their effect on Y (luster of paint film). The four factors are: 1) Film Thickness - (1 or 2 mils) 2) Drying conditions (Regular or Special) 3) Length of wash (10,30,40 or 60 Minutes), and 4) Temperature of wash (92 ˚C or 100 ˚C) Two observations of film luster (Y) are taken for each treatment combination

18. The data is tabulated below: Regular Dry Special Dry Minutes 92 C 100 C 92C 100 C 1-mil Thickness 20 3.4 3.4 19.6 14.5 2.1 3.8 17.2 13.4 30 4.1 4.1 17.5 17.0 4.0 4.6 13.5 14.3 40 4.9 4.2 17.6 15.2 5.1 3.3 16.0 17.8 60 5.0 4.9 20.9 17.1 8.3 4.3 17.5 13.9 2-mil Thickness 20 5.5 3.7 26.6 29.5 4.5 4.5 25.6 22.5 30 5.7 6.1 31.6 30.2 5.9 5.9 29.2 29.8 40 5.5 5.6 30.5 30.2 5.5 5.8 32.6 27.4 60 7.2 6.0 31.4 29.6 8.0 9.9 33.5 29.5

19. The Data as it appears in SPSS

20. The dialog box for performing ANOVA

21. The output

22. Random Effects and Fixed Effects Factors

23. So far the factors that we have considered are fixed effects factors • This is the case if the levels of the factor are a fixed set of levels and the conclusions of any analysis is in relationship to these levels. • If the levels have been selected at random from a population of levels the factor is called a random effects factor • The conclusions of the analysis will be directed at the population of levels and not only the levels selected for the experiment

24. Example - Fixed Effects Source of Protein, Level of Protein, Weight Gain Dependent • Weight Gain Independent • Source of Protein, • Beef • Cereal • Pork • Level of Protein, • High • Low

25. Example - Random Effects In this Example a Taxi company is interested in comparing the effects of three brands of tires (A, B and C) on mileage (mpg). Mileage will also be effected by driver. The company selects b = 4 drivers at random from its collection of drivers. Each driver has n = 3 opportunities to use each brand of tire in which mileage is measured. Dependent • Mileage Independent • Tire brand (A, B, C), • Fixed Effect Factor • Driver (1, 2, 3, 4), • Random Effects factor

26. The Model for the fixed effects experiment where m, a1, a2, a3, b1, b2, (ab)11 , (ab)21 , (ab)31 , (ab)12 , (ab)22 , (ab)32 , are fixed unknown constants And eijk is random, normally distributed with mean 0 and variance s2. Note:

27. The Model for the case when factor B is a random effects factor where m, a1, a2, a3, are fixed unknown constants And eijk is random, normally distributed with mean 0 and variance s2. bj is normal with mean 0 and variance and (ab)ij is normal with mean 0 and variance Note: This model is called a variance components model

28. The Anova table for the two factor model

29. The Anova table for the two factor model (A, B – fixed) EMS = Expected Mean Square

30. The Anova table for the two factor model (A – fixed, B - random) Note: The divisor for testing the main effects of A is no longer MSError but MSAB.

31. Rules for determining Expected Mean Squares (EMS) in an Anova Table Both fixed and random effects Formulated by Schultz[1] Schultz E. F., Jr. “Rules of Thumb for Determining Expectations of Mean Squares in Analysis of Variance,”Biometrics, Vol 11, 1955, 123-48.

32. The EMS for Error is s2. • The EMS for each ANOVA term contains two or more terms the first of which is s2. • All other terms in each EMS contain both coefficients and subscripts (the total number of letters being one more than the number of factors) (if number of factors is k = 3, then the number of letters is 4) • The subscript of s2 in the last term of each EMS is the same as the treatment designation.

33. The subscripts of all s2 other than the first contain the treatment designation. These are written with the combination involving the most letters written first and ending with the treatment designation. • When a capital letter is omitted from a subscript , the corresponding small letter appears in the coefficient. • For each EMS in the table ignore the letter or letters that designate the effect. If any of the remaining letters designate a fixed effect, delete that term from the EMS.

34. Replace s2 whose subscripts are composed entirely of fixed effects by the appropriate sum.

35. Example: 3 factors A, B, C – all are random effects

36. Example: 3 factors A fixed, B, C random

37. Example: 3 factors A , B fixed, C random

38. Example: 3 factors A , B and C fixed

39. Example - Random Effects In this Example a Taxi company is interested in comparing the effects of three brands of tires (A, B and C) on mileage (mpg). Mileage will also be effected by driver. The company selects at random b = 4 drivers at random from its collection of drivers. Each driver has n = 3 opportunities to use each brand of tire in which mileage is measured. Dependent • Mileage Independent • Tire brand (A, B, C), • Fixed Effect Factor • Driver (1, 2, 3, 4), • Random Effects factor

40. The Data

41. Asking SPSS to perform Univariate ANOVA

42. Select the dependent variable, fixed factors, random factors

43. The Output The divisor for both the fixed and the random main effect is MSAB This is contrary to the advice of some texts

44. The Anova table for the two factor model (A – fixed, B - random) Note: The divisor for testing the main effects of A is no longer MSError but MSAB. References Guenther, W. C. “Analysis of Variance” Prentice Hall, 1964

45. The Anova table for the two factor model (A – fixed, B - random) Note: In this case the divisor for testing the main effects of A is MSAB .This is the approach used by SPSS. References Searle “Linear Models” John Wiley, 1964

46. Crossed and Nested Factors

47. The factors A, B are called crossed if every level of A appears with every level of B in the treatment combinations. Levels of B Levels of A

48. Factor B is said to be nested within factor A if the levels of B differ for each level of A. Levels of A Levels of B

49. Example: A company has a = 4 plants for producing paper. Each plant has 6 machines for producing the paper. The company is interested in how paper strength (Y) differs from plant to plant and from machine to machine within plant Plants Machines

50. Machines (B) are nested within plants (A) The model for a two factor experiment with B nested within A.