Download Review of ANOVA Computer Output Interpretation

Review of ANOVA Computer Output
Interpretation
• This is from Page 104 of your text Montgomery 5th edition
section 3-6.
• There are many computer Statistical analysis programs
available,
• Popular ones include:
• EXCEL Data Analysis Add in: Limited
• Design Expert
• Jump
• Minitab
• Also all programs have built in help tutorials if you forget
or get lost with these tests!!
ANOVA Computer Output Steve
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Review of ANOVA Computer Output
Interpretation
• Example used here:
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation:
We are asking question: Are means between treatments
different? And By how much?
• First attack look at graphs:from Design Expert
•
•
•
It appears 20, 25, 30% cotton levels are different than 15 and 35%, By how
much?
Is this due to chance variation?
How do we assign a confidence value to any decision we make?
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert: ANOVA Single Factor
Typical ANOVA Table with Sum of Squares
M for Model
terms
e for error
terms
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
If there were more than one factor or source there would be A, B, C etc listed
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
Design Expert Analysis ANOVA Terms
Model: Terms estimating factor effects. For 2-level factorials: those that "fall off" the normal probability line
of the effects plot.
Sum of Squares: Total of the sum of squares for the terms in the model, as reported in the Effects List for
factorials and on the Model screen for RSM, MIX and Crossed designs.
DF: Degrees of freedom for the model. It is the number of model terms, including the intercept, minus one.
Mean Square: Estimate of the model variance, calculated by the model sum of squares divided by model
degrees of freedom.
F Value: Test for comparing model variance with residual (error) variance. If the variances are close to the
same, the ratio will be close to one and it is less likely that any of the factors have a significant effect on the
response. Calculated by Model Mean Square divided by Residual Mean Square.
Probe > F: Probability of seeing the observed F value if the null hypothesis is true (there is no factor effect).
Small probability values call for rejection of the null hypothesis. The probability equals the
proportion of the area under the curve of the F-distribution that lies beyond the observed F value. The F
distribution itself is determined by the degrees of freedom associated with the variances being compared.
(In "plain English", if the Probe>F value is very small (less than 0.05) then the
terms in the model have a significant effect on the response.)
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
Std Dev: (Root MSe ) Square root of the residual mean square. Consider this to be an
estimate of the standard deviation associated with the experiment.
SQRT(8.06) = 2.84
Mean: Overall average of all the response data.
Grand mean = 15.04
C.V.: Coefficient of Variation, the standard deviation expressed as a percentage of the
mean. Calculated by dividing the Std Dev by the Mean and multiplying by 100.
CV =2.84/15.04 x 100 =18.88%
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
PRESS: Predicted Residual Error Sum of Squares – Basically a measure of how
well the model from this experiment is likely to predict the response in a new
experiment.. Small values are desirable
The PRESS is computed by first predicting where each point should be from a model
that contains all other points except the one in question. The squared residuals
(difference between actual and predicted values) are then summed.
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
R2 Factor A model (% cotton) explains 74.69% variability in response
(Tensile strength)
R-Squared: A measure of the amount of variation around the mean explained by the model.
R2 = 1-(SSresidual / (SSmodel + SSresidual)) =SSmodel/SStotal = 475.76/636.96 = 0.7469
Adjusted R2 Adj R-Squared: A measure of the amount of variation around the mean
explained by the model, adjusted for the number of terms in the model. The adjusted Rsquared decreases as the number of terms in the model increases if those additional terms don’t
add value to the model.
1-((SSresidual / DFresidual) / ((SSmodel + SSresidual) / (DFmodel + DFresidual)))
= 1-((161.2/20)/((475.76+161.2)/(4 + 20))) =1-((161.20/20)/(636.96/24)) = 1(8.06/26.54) = 0.6963
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
Pred R-Squared: A measure of the amount of variation in new data explained by
the model.
1-(PRESS / (SStotal-SSblock) = 1-(251.87/(636.96 – 0) =
1-(251.87/636.96) = 1 – 0.3954 = 0.6046 = 60.46%
(Remember the model we generated accounts for 74% of the observed
variation in tensile strength from the % cotton factor.)
The predicted r-squared and the adjusted r-squared should be within 0.20 of each other. Otherwise
there may be a problem with either the data or the model. Look for outliers, consider
transformations, or consider a different order polynomial.
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
Adequate Precision: Basically a measure of S/N ( signal to noise ratio), It gives
you a factor by which you can judge your model to see if it “adequate” to
navigate through the design space and be able to predict the response. Desire
values > 4.0
Computed by : (Maximum predicted response – Minimum predicted response)/
(Average standard deviation of all predicted responses)
Adequate Precision = (21.60 – 9.80)/(sqrt(MSe/5)) =11.80/(sqrt(8.06/5)) =
11.80/(sqrt(1.6119 ) = 11.80/1.2696 = 9.294
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
Coefficient Estimate: Regression coefficient representing the expected change in response y
per unit change in x when all remaining factors are held constant. In orthogonal designs, it
equals one half the factorial effect.
DF: Degrees of Freedom – equal to one for testing coefficients.
Standard Error: The standard deviation associated with the coefficient estimate.
95% CI High and Low: These two columns represent the range that the true coefficient
should be found in 95% of the time. If this range spans 0 (one limit is positive and the other
negative) then the coefficient of 0 could be true, indicating the factor has no effect.
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:ANOVA Single Factor
Treatment mean = estimate of the effect for each %
cotton level ( treatment)
Standard error = estimate of the sample standard
deviation for that effect (treatment) = sqrt(MSe/n)
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:
ANOVA Single Factor
Differences in pairs of treatment LSD analysis Design Expert VS JUMP
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STAT EASE Design Expert Diagnostics:
ANOVA Single Factor
Design
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics: : ANOVA Single Factor
Leverage is the potential for a design point to influence the fit of
the model coefficients, based on its position in the design space.
Leverages near 1 should be avoided. Replicate the point or add
more design points to reduce leverage.
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics: : ANOVA Single Factor
The Student Residual is the number of standard deviations that
separate the actual and predicted response values. It is the residual
divided by the estimated standard deviation of the residual.
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics: : ANOVA Single Factor
The Cook's distance for this observation--not to be confused with
the distance between the dining room and kitchen. It is a measure
of how much the regression equation changes if this specific run is
deleted. It is roughly a combination of leverage and outlier-T and
can be used to help identify individual runs that may be outliers.
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Diagnostics:
ANOVA Single Factor
The outlier t test checks whether a run is consistent with the other
runs, assuming the chosen model holds. The model coefficients
are calculated based on all of the design points except one. A
prediction of the response at this point is made. The residual is
evaluated using the t-test. A value greater than 3.5 means that this
point should be examined as a possible outlier.
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Create a Model: Change Factor 1
to Numeric
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Create a Model:
Click on Status: Model
Order Cubic; Right click
on Coefficient and make
Model M
Click on Analysis: Note new
menu options: Click on Fit
Summary:
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STAT EASE Design Expert Create a Model:
FIT SUMMARY
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STAT EASE Design Expert Create a Model:
Click on Model;
Next Click on ANOVA
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Create a Model:
ANOVA Screen for Cubic Model Suggested>>
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Create a Model:
ANOVA Screen for Cubic Model Suggested>> Model Equations!!
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Review of ANOVA Computer Output Interpretation
STAT EASE Design Expert Create a Model:
Model Graphic Screen for Cubic Model Suggested
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Review of ANOVA Computer Output Interpretation
JUMP Design Expert Model Fitting
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Review of ANOVA Computer Output Interpretation
JUMP Design Expert Model Fitting
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Review of ANOVA Computer Output Interpretation
JUMP Design Expert Model Fitting 2nd Order
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Review of ANOVA Computer Output Interpretation
JUMP Design Expert Model Fitting 3rd Order
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Journal to save as text in WORD Save as Rich Text Format .RTF
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JUMP: ANOVA Single Factor
JUMP output
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump Summary of Model fit and ANOVA table
Same as Design
Expert!
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump Outputs Mean confidence limits on the estimate
of each treatment mean!
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JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
Multiple Comparisons
There are a variety of methods to test differences in group means (multiple
comparisons) that vary in detail about how to size the test to accommodate different
kinds of multiple comparisons. Fit Y by X automatically produces the standard
analysis of variance and optionally offers the following four multiple comparison tests:
Each Pair, Student’s t computes individual pairwise comparisons using Student’s t
tests. This test is sized for individual comparisons. If you make many pairwise tests,
there is no protection across the inferences, and thus the alpha-size (Type I) error rate
across the hypothesis tests is higher than that for individual tests.
All Pairs, Tukey HSD gives a test that is sized for all differences among the means.
This is the Tukey or Tukey-Kramer HSD (honestly significant difference) test. (Tukey
1953, Kramer 1956). This test is an exact alpha-level test if the sample sizes are the
same and conservative if the sample sizes are different (Hayter 1984).
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
Group 25 % cotton just used as a example
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
Multiple Comparisons
With Best, Hsu’s MCB tests whether means are less than the unknown maximum
(or greater than the unknown minimum). This is the Hsu MCB test (Hsu 1981).
With Control, Dunnett’s tests whether means are different from the mean of a
control group. This is Dunnett’s test (Dunnett 1955).
The three multiple comparisons tests are the ones recommended by Hsu (1989) as
level-5 tests for the three situations: MCA (Multiple Comparisons for All pairs),
MCB (Multiple Comparisons with the Best), and MCC (Multiple Comparisons
with Control).
If you have specified a Block column, then the multiple comparison methods are
performed on data that has been adjusted for the Block means.
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
Red Circles are the ones we are interested in and and the grays
indicate they are different.
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
Diamonds: vertical 95%
confidence interval
Horizontal = mean
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JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
ANOVA Computer Output Steve
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JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Example of large
variation for 35% cotton and small for 15% cotton
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Example of large
mean for 35% cotton and small for 15% cotton
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Example of large
variation for 35% cotton and small for 15% cotton
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
HSD =Honestly Significant Difference
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Tukey-Kramer
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Hsu’s MCB
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Hsu’s MCB
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means Dunnett’s
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Means
ANOVA Computer Output Steve
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Review of ANOVA Computer Output Interpretation
JUMP: ANOVA Single Factor
Jump POST ANOVA Comparison of Variances Tests
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