Graphical Analysis of Variance (Graphical ANOVA) This set of slides ...
Graphical Analysis of Variance (Graphical ANOVA) This set of slides ... Graphical Analysis of Variance (Graphical ANOVA) This set of slides ...
Motor Vibration The second brand average appears in the extreme right tail. There is clear evidence that the amount of vibration depends on the type of bearing. 40
An Example Using Simulated Data What does this ANOVA plot look like if there really is no difference between the samples? To check, we simulate 40 independent observations from a normal distribution with mean 0 and variance 1. We pretend that the first 10 observations are from 1 sample, the second set of 10 are from another sample, and so on. 41
- Page 1 and 2: Graphical Analysis of Variance (Gra
- Page 3 and 4: A Paper Airplane Experiment Does th
- Page 5 and 6: The Paper Airplane Experiment Data
- Page 7 and 8: Paper Airplane Data > tail(airplane
- Page 9 and 10: Compare Averages for the 3 Groups F
- Page 11 and 12: Compare Averages for the 3 Groups N
- Page 13 and 14: Discussion Some of the possibilitie
- Page 15 and 16: Identifying Within Group Variation
- Page 17 and 18: Identifying Within Group Variation
- Page 19 and 20: Identifying Within Group Variation
- Page 21 and 22: Does paper type have an effect on m
- Page 23 and 24: Does paper type have an effect on m
- Page 25 and 26: Graphically testing whether paper t
- Page 27 and 28: The Graphical Test Histogram of err
- Page 29 and 30: The Graphical ANOVA Procedure 1. Co
- Page 31 and 32: Application of the function to the
- Page 33 and 34: Motor Vibration Example 5 different
- Page 35 and 36: Motor Vibration Data > names(motor)
- Page 37 and 38: Motor Vibration Data > names(motors
- Page 39: Motor Vibration Data Frequency 0 2
- Page 43 and 44: Simulated Data > graphicalANOVA(pre
An Example Using Simulated Data<br />
What does this <strong>ANOVA</strong> plot look like if there really<br />
is no difference between the samples?<br />
To check, we simulate 40 independent<br />
observations from a normal distribution with<br />
mean 0 and variance 1.<br />
We pretend that the first 10 observations are from<br />
1 sample, the second <strong>set</strong> <strong>of</strong> 10 are from another<br />
sample, and so on.<br />
41
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