1 / 47

Experimental Statistics Spring 2006 - week 6

Experimental Statistics Spring 2006 - week 6. Chapter 15: Factorial Models (15.5). STIMULUS EXAMPLE: Personal computer presents stimulus, and person responds. Study of how RESPONSE TIME is effected by a WARNING given prior to the stimulus: 2-factors of interest:

nissim-gallagher
Download Presentation

Experimental Statistics Spring 2006 - week 6

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Experimental Statistics Spring 2006 - week 6 Chapter 15: Factorial Models (15.5)

  2. STIMULUS EXAMPLE: Personal computer presents stimulus, and person responds. Study of how RESPONSE TIME is effected by a WARNING given prior to the stimulus: 2-factors of interest: Warning Type --- auditory or visual Time between warning and stimulus -- 5 sec, 10 sec, or 15 sec. 

  3. Auditory Visual .204 .257 .170 .279 .190 .269 .167 .282 .182 .255 .187 .274 .192 .256 .200 .281 .216 .258 5 sec WarningTime 10 sec 15 sec Note:“Sort of like RCB” -- what is the difference? Question: How would you randomize? - 18 subjects - 1 subject

  4. Stimulus Data Observed data (response time) Replication Level of Factor A Level of Factor B (warning type) (time)

  5. 2-Factor ANOVA Data Factor B FactorA

  6. A Possible Model for STIMULUS Data i = type, j = time Note: so according to this model Note: The model assumes that the difference between types is the same for all times

  7. Hypothetical Cell Means Auditory Visual 5 10 15

  8. Similarly i.e. the model says the difference between times j and j' is the same for all types We may not want to make these assumptions!!

  9. Hypothetical Cell Means Auditory Visual 5 10 15 Auditory Visual 5 10 15

  10. Model for 2-factor Design where

  11. Sum-of-Squares Breakdown (2-factor ANOVA) SSA SSB SSAB SSE

  12. 2-Factor ANOVA Table(2-Factor Completely Randomized Design) Source SS df MS F Main Effects A SSA a -1 B SSB b- 1 Interaction AB SSAB (a -1)(b- 1) Error SSE ab(n -1) Total TSS abn -1 See page 900

  13. Hypotheses: Main Effects: Interactions:

  14. Stimulus Data -- SAS data stimulus; input type$ time response; datalines; A 5 .204 A 5 .170 A 5 .190 A 10 .167 A 10 .182 A 10 .187 A 15 .192 A 15 .200 A 15 .216 V 5 .257 V 5 .279 V 5 .269 V 10 .282 V 10 .255 V 10 .274 V 15 .256 V 15 .281 V 15 .258 ; PROCGLM; CLASSES type time; MODEL response=type time type*time; means type/lsd; means time/lsd; TITLE‘Stimulus Data'; run;

  15. GLM Output The GLM Procedure Dependent Variable: response Sum of Source DF Squares Mean Square F Value Pr > F Model 5 0.02837783 0.00567557 32.24 <.0001 Error 12 0.00211267 0.00017606 Corrected Total 17 0.03049050 R-Square Coeff Var Root MSE response Mean 0.930711 5.798365 0.013269 0.228833 Source DF Type I SS Mean Square F Value Pr > F type 1 0.02745606 0.02745606 155.95 <.0001 time 2 0.00026533 0.00013267 0.75 0.4917 type*time 2 0.00065644 0.00032822 1.86 0.1972

  16. Testing Procedure 2 factor CRD Design Step 1. Test for interaction. Step 2. (a) IFthere IS NOT a significant interaction - test the main effects (b) IF there IS a significant interaction - compare cell means

  17. Stimulus Example Test for Interaction: Therefore we DO NOT reject the null hypothesis of no interaction.

  18. Stimulus Data

  19. Stimulus Example Test for Interaction: Therefore we DO NOT reject the null hypothesis of no interaction. Thus - based on the testing procedure, we next test for main effects.

  20. Testing Main Effects: For each main effect (i.e. A and B) Note:I’ll use LSD from this point on unless otherwise noted. In General: where N denotes the # of observations involved in the computation of a marginal mean.

  21. Auditory Visual .204 .257 .170 .279 .190 .269 .167 .282 .182 .255 .187 .274 .192 .256 .200 .281 .216 .258 5 sec WarningTime 10 sec 15 sec

  22. Stimulus Example Test for Main Effects: A (type): B (time): Thus, there is a significant effect due to type but not time - i.e. we can use LSD to compare marginal means for type - we will do this here for illustration although MC not needed when there are only 2 groups

  23. GLM Output -- Comparing “Types” The GLM Procedure t Tests (LSD) for response NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 0.000176 Critical Value of t 2.17881 Least Significant Difference 0.0136 Means with the same letter are not significantly different. t Grouping Mean N type A 0.267889 9 V B 0.189778 9 A

  24. GLM Output -- Comparing “Times” The GLM Procedure t Tests (LSD) for response NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 0.000176 Critical Value of t 2.17881 Least Significant Difference 0.0167 Means with the same letter are not significantly different. t Grouping Mean N time A 0.233833 6 15 A A 0.228167 6 5 A A 0.224500 6 10

  25. Pilot Plant Data Variable = Chemical Yield Factors: A – Temperature (160, 180) B – Catalyst (C1 , C2) 160 C1 59 160 C1 61 160 C1 50 160 C1 58 180 C1 74 180 C1 70 180 C1 69 180 C1 67 160 C2 50 160 C2 54 160 C2 46 160 C2 44 180 C2 81 180 C2 85 180 C2 79 180 C2 81

  26. Pilot Plant Data Variable = Chemical Yield Factors: A – Temperature (160, 180) B – Catalyst (C1 , C2) Temperature 59 74 61 70 50 69 58 67 50 81 54 85 46 79 44 81 Catalyst

  27. Pilot Plant -- Probability Plot of Residuals

  28. DATA one; INPUT temp catalyst$ yield; datalines; 160 C1 59 160 C1 61 . . . 180 C2 79 180 C2 81 ; PROCGLM; class temp catalyst; MODEL yield=temp catalyst temp*catalyst; Title 'Pilot Plant Example -- 2-way ANOVA'; MEANS temp catalyst/LSD; RUN; PROCSORT;BY temp catalyst; PROCMEANS; BY temp catalyst; OUTPUTOUT=cells MEAN=yield; RUN;

  29. Pilot Plant -- GLM Output Pilot Plant Example -- 2-way ANOVA General Linear Models Procedure Dependent Variable: YIELD Sum of Mean Source DF Squares Square F Value Pr > F Model 3 2525.0000000 841.6666667 58.05 0.0001 Error 12 174.0000000 14.5000000 Corrected Total 15 2699.0000000 R-Square C.V. Root MSE YIELD Mean 0.935532 5.926672 3.8078866 64.250000 Source DF Type I SS Mean Square F Value Pr > F TEMP 1 2116.0000000 2116.0000000 145.93 0.0001 CATALYST 1 9.0000000 9.0000000 0.62 0.4461 TEMP*CATALYST 1 400.0000000 400.0000000 27.59 0.0002

  30. RECALL: Testing Procedure 2 factor CRD Design Step 1. Test for interaction. Step 2. (a) IFthere IS NOT a significant interaction - test the main effects (b) IF there IS a significant interaction - compare cell means

  31. Pilot Plant Example Test for Interaction: Therefore we reject the null hypothesis of no interaction - and conclude that there is an interaction between temperature and catalyst. Thus, we DO NOT test main effects

  32. Since there is a significant interaction, we do not test for main effects! - instead compare “Cell Means” - NOTE: interaction plot is a plot of the cell means

  33. Pilot Plant Data Variable = Chemical Yield Factors: A – Temperature (160, 180) B – Catalyst (C1 , C2) Temperature 59 74 61 70 50 69 58 67 50 81 54 85 46 79 44 81 Catalyst

  34. Pilot Plant Data -- cell means Temperature 57.0 70.0 48.5 81.5 Catalyst

  35. Comparing Cell Means: If there is significant interaction, then we compare the a x b cell means using the criteria below. Procedure similar to that for comparing marginal means: where N denotes the # of observations involved in the computation of a cell mean.

  36. GLM Output -- Comparing “Temps” The GLM Procedure t Tests (LSD) for yield NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate.   Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 14.5 Critical Value of t 2.17881 Least Significant Difference 4.1483 Means with the same letter are not significantly different. t Grouping Mean N temp A 75.750 8 180 B 52.750 8 160 - disregard

  37. GLM Output -- Comparing “Catalysts” The GLM Procedure   t Tests (LSD) for yield NOTE: This test controls the Type I comparisonwise error rate, not the experimentwise error rate. Alpha 0.05 Error Degrees of Freedom 12 Error Mean Square 14.5 Critical Value of t 2.17881 Least Significant Difference 4.1483 Means with the same letter are not significantly different. t Grouping Mean N catalyst A 65.000 8 C2 A A 63.500 8 C1 - disregard

  38. Note: - SAS does not provide a comparison of cell means

  39. Pilot Plant Data -- cell means Temperature LSD: 57.0 70.0 48.5 81.5 C a t a l y s t MSE = N = LSD = C2/160 C1/160 C1/180 C2/180 48.5 57.0 70.0 81.5

  40. Testing Procedure Revisted 2 factor CRD Design Step 1. Test for interaction. Step 2. (a) IFthere IS NOT a significant interaction - test the main effects (b) IF there IS a significant interaction - compare a x b cell means (by hand) Main Idea:We are trying to determine whether the factors effect the response either individually or collectively.

More Related