Presentation is loading. Please wait.

Presentation is loading. Please wait.

Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 1.

Published byIlene Gregory Modified over 9 years ago

Similar presentations

Presentation on theme: "Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 1."— Presentation transcript:

1 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 1

2 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 2

3 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 3

4 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 4

5 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 5

6 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 6

7 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 7 A GLM can be related to a standard regression model:

8 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 8

9 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 9

10 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 10

11 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 11

12 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 12

13 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 13

14 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 14

15 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 15

16 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 16

17 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 17

18 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 18

19 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 19

20 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 20

21 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 21

22 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 22 Approximate Methods Estimating missing values Setting dada aside

23 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 23 15.3 The Analysis of Covariance A useful technique for dealing with uncontrollable but observable nuisance factors

24 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 24

25 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 25 The Basic ANCOVA Model The model is a combination of an ANOVA and a regression model The analysis is an “adjusted” ANOVA The procedure actually fits the covariate first, then fids the contribution of the treatment factor over and above the covariate Details of computing are in the text JMP, Minitab, and SAS can do the analysis

26 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 26

27 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 27

28 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 28

29 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 29

30 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 30

31 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 31

32 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 32

33 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 33

34 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 34

35 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 35

36 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 36

37 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 37

38 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 38

39 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 39

40 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 40

41 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 41

42 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 42

43 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 43

44 Design of Experiments with Known Covariates In many situations the covariates are observed along with the response variable(s), so there is no opportunity to use information about the covariates to design the experiment Sometimes the covariates are known in advance, and when this is the case, this information can be used in constructing a design Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 44

45 Motor Oil Additive Experiment Properties of the product depend on two controllable factors They also depend on the viscosity and molecular weight of a raw material used to manufacture the product The raw material properties are known in advance, because there are 40 samples of this material available for use in the experiment Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 45

46 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 46

47 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 47

48 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 48

49 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 49

50 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 50

51 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 51

52 Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 52

Download ppt "Chapter 15Design & Analysis of Experiments 8E 2012 Montgomery 1."

Similar presentations

Chapter 4Design & Analysis of Experiments 7E 2009 Montgomery 1 Experiments with Blocking Factors Text Reference, Chapter 4 Blocking and nuisance factors.

Chapter 4Design & Analysis of Experiments 7E 2009 Montgomery 1 Experiments with Blocking Factors Text Reference, Chapter 4 Blocking and nuisance factors.
1 Chapter 4 Experiments with Blocking Factors. 2 4.1 The Randomized Complete Block Design Nuisance factor: a design factor that probably has an effect.

1 Chapter 4 Experiments with Blocking Factors The Randomized Complete Block Design Nuisance factor: a design factor that probably has an effect.
DOX 6E Montgomery1 Design of Engineering Experiments Part 3 – The Blocking Principle Text Reference, Chapter 4 Blocking and nuisance factors The randomized.

DOX 6E Montgomery1 Design of Engineering Experiments Part 3 – The Blocking Principle Text Reference, Chapter 4 Blocking and nuisance factors The randomized.
1 Design of Engineering Experiments Part 3 – The Blocking Principle Text Reference, Chapter 4 Blocking and nuisance factors The randomized complete block.

1 Design of Engineering Experiments Part 3 – The Blocking Principle Text Reference, Chapter 4 Blocking and nuisance factors The randomized complete block.
Chapter 4 Randomized Blocks, Latin Squares, and Related Designs

Chapter 4 Randomized Blocks, Latin Squares, and Related Designs
Stratification (Blocking) Grouping similar experimental units together and assigning different treatments within such groups of experimental units A technique.

Stratification (Blocking) Grouping similar experimental units together and assigning different treatments within such groups of experimental units A technique.
Chapter 7Design & Analysis of Experiments 8E 2012 Montgomery 1 Design of Engineering Experiments Blocking & Confounding in the 2 k Text reference, Chapter.

Chapter 7Design & Analysis of Experiments 8E 2012 Montgomery 1 Design of Engineering Experiments Blocking & Confounding in the 2 k Text reference, Chapter.
Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles.

Chapter 17 Making Sense of Advanced Statistical Procedures in Research Articles.
Type I and Type III Sums of Squares. Confounding in Unbalanced Designs When designs are “unbalanced”, typically with missing values, our estimates of.

Type I and Type III Sums of Squares. Confounding in Unbalanced Designs When designs are “unbalanced”, typically with missing values, our estimates of.
Design of Engineering Experiments - Experiments with Random Factors

Design of Engineering Experiments - Experiments with Random Factors
Statistics 200b. Chapter 5. Chapter 4: inference via likelihood now Chapter 5: applications to particular situations.

Statistics 200b. Chapter 5. Chapter 4: inference via likelihood now Chapter 5: applications to particular situations.
Analysis of Covariance Goals: 1)Reduce error variance. 2)Remove sources of bias from experiment. 3)Obtain adjusted estimates of population means.

Analysis of Covariance Goals: 1)Reduce error variance. 2)Remove sources of bias from experiment. 3)Obtain adjusted estimates of population means.
Chapter 7 Blocking and Confounding in the 2k Factorial Design

Chapter 7 Blocking and Confounding in the 2k Factorial Design
1 Chapter 7 Blocking and Confounding in the 2 k Factorial Design.

1 Chapter 7 Blocking and Confounding in the 2 k Factorial Design.
Chapter 5Design & Analysis of Experiments 7E 2009 Montgomery 1 Factorial Experiments Text reference, Chapter 5 General principles of factorial experiments.

Chapter 5Design & Analysis of Experiments 7E 2009 Montgomery 1 Factorial Experiments Text reference, Chapter 5 General principles of factorial experiments.
Analysis of Covariance The function of Experimental design is to explain the effect of a IV or DV while controlling for the confounding effect of extraneous.

Analysis of Covariance The function of Experimental design is to explain the effect of a IV or DV while controlling for the confounding effect of extraneous.
Analysis of Covariance Goals: 1)Reduce error variance. 2)Remove sources of bias from experiment. 3)Obtain adjusted estimates of population means.

Analysis of Covariance Goals: 1)Reduce error variance. 2)Remove sources of bias from experiment. 3)Obtain adjusted estimates of population means.
T WO W AY ANOVA W ITH R EPLICATION  Also called a Factorial Experiment.  Factorial Experiment is used to evaluate 2 or more factors simultaneously. 

T WO W AY ANOVA W ITH R EPLICATION  Also called a Factorial Experiment.  Factorial Experiment is used to evaluate 2 or more factors simultaneously. 
T WO WAY ANOVA WITH REPLICATION  Also called a Factorial Experiment.  Replication means an independent repeat of each factor combination.  The purpose.

T WO WAY ANOVA WITH REPLICATION  Also called a Factorial Experiment.  Replication means an independent repeat of each factor combination.  The purpose.
ANCOVA Lecture 9 Andrew Ainsworth. What is ANCOVA?

ANCOVA Lecture 9 Andrew Ainsworth. What is ANCOVA?

Similar presentations

Ads by Google