Data Display
MTB > set ‘A’ Data DATA> 8(-1 1)
RowAB Y
DATA> end
MTB > set ‘B’
DATA> 4(-1 1)2
DATA> end
MTB > read ‘Y’;
SUBC> file “e:/Kurt/Documents/ise525/Blackboard/mm3- 8y.dat”.
Entering data from file: E:/KURT/DOCUMENTS/ISE525/BLACKBOARD/MM3- 8Y.DAT
16 rows read.
MTB > print c1-c3
MTB >
10
11
12
13
14
15
16
1 2 3 4 5 6 7 8 9
-1 -1 14.037 1 -1 13.880 -1 1 14.821 1 1 14.880 -1 -1 16.165 1 -1 13.860 -1 1 14.757 1 1 14.921 -1 -1 13.972 1 -1 14.032 -1 1 14.843 1 1 14.415 -1 -1 13.907 1 -1 13.914 -1 1 14.878 1 1 14.932
Stat > ANOVA > Balanced ANOVA…
ANOVA: Y versus A, B Factor Information
MTB > ANOVA ‘Y’ = A | B;
Factor Type Levels Values
A Fixed 2 -1,1
B Fixed 2 -1,1
Analysis of Variance for Y
Source DF
A 1
B 1
A*B 1
SS MS 0.4051 0.4051 1.3689 1.3689 0.3147 0.3147 3.8269 0.3189
FP 1.27 0.282 4.29 0.060 0.99 0.340
Error 12 Total 15
5.9157
Model Summary
S R-sq R-sq(adj) 0.564723 35.31% 19.14%
SUBC> MTB >
Means A | B.
Means
ANY -1 8 14.6725 1 8 14.3542
BNY -1 8 14.2209
1 8 14.8059
A*BN -1-14 -1 1 4 1 -1 4 1 1 4
Y 14.5202 14.8247 13.9215 14.7870
Regression Analysis: Y versus A, B, AB Analysis of Variance
MTB > name c4 ‘AB’
MTB > let ‘AB’ = ‘A’*’B’
MTB > Regress;
SUBC> Response ‘Y’;
SUBC> Nodefault;
SUBC> Continuous ‘A’ ‘B’ ‘AB’;
SUBC> Terms A B AB;
SUBC> Constant;
SUBC> Unstandardized;
SUBC> Tmethod;
SUBC> Tanova;
SUBC> Tsummary;
SUBC> Tcoefficients;
SUBC> Tequation;
Source DF Regression 3 A1 B1 AB1 Error 12 Total 15
Adj SS 2.0888 0.4051 1.3689 0.3147 3.8269 5.9157
Adj MS 0.6963 0.4051 1.3689 0.3147 0.3189
F-Value 2.18 1.27 4.29 0.99
P-Value 0.143 0.282 0.060 0.340
Model Summary
SUBC> MTB >
TDiagnostics 0.
S R-sq 0.564723 35.31%
R-sq(adj) 19.14%
R-sq(pred) 0.00%
Coefficients
Term Coef Constant 14.513 A -0.159 B 0.293 AB 0.140
SE Coef 0.141 0.141 0.141 0.141
T-Value 102.80 -1.13 2.07 0.99
P-Value 0.000 0.282 0.060 0.340
VIF
Regression Equation
Y = 14.513-0.159A+0.293B+0.140AB
Fits and Diagnostics for Unusual Observations
Std Obs Y Fit Resid Resid
5 16.165 14.520 1.645 3.36 R R Large residual
1.00 1.00 1.00
Regression Analysis: Y versus B Analysis of Variance
MTB > Regress;
SUBC> Response ‘Y’;
SUBC> Nodefault;
SUBC> Continuous ‘A’ ‘B’ ‘AB’;
SUBC> Terms B;
SUBC> Constant;
SUBC> Unstandardized;
SUBC> Tmethod;
SUBC> Tanova;
SUBC> Tsummary;
SUBC> Tcoefficients;
SUBC> Tequation;
SUBC> TDiagnostics 0.
MTB > Save “E:\Kurt\Documents\ise525\Blackboard\mm3- 8_2018.MPJ”;
SUBC> Project;
SUBC> Replace.
Saving file as: ¡®E:\Kurt\Documents\ise525\Blackboard\mm3- 8_2018.MPJ¡¯
MTB >
Source DF Regression 1 B1 Error 14 Lack-of-Fit 2
F-Value P-Value 0.059 0.059
Pure Error Total
12 15
Model Summary
S 0.569887
R-sq R-sq(adj) 23.14% 17.65%
R-sq(pred) 0.00%
Coefficients
Term Constant B
Coef 14.513 0.292
SE Coef 0.142 0.142
T-Value P-Value 101.87 0.000 2.05 0.059
VIF 1.00
Regression Equation
Y = 14.513 + 0.292 B
Adj SS 1.3689 1.3689 4.5468 0.7199 3.8269 5.9157
Adj MS
1.3689 4.21 1.3689 4.21 0.3248
0.3599 1.13 0.3189
Fits and Diagnostics for Unusual Observations
Std Obs Y Fit Resid Resid
5 16.165 14.221 1.944 3.65 R R Large residual
0.356