第一章 minitab 概要 第二章管理数据 第三章操作和计算数据...
DESCRIPTION
目录. 第一章 Minitab 概要 第二章管理数据 第三章操作和计算数据 第四章使用数据分析和质量工具 第五章基本操作示例 第六章做一个简单分析 第七章高级 Minitab 第八章质量管理和改善 第九章实验设计. 前言. MINITAB 是为质量改善、教育和研究应用领域提供统计软件和服务的先导。是一个很好的质量管理和质量设计的工具软件，更是持续质量改进的良好工具软件。 - PowerPoint PPT PresentationTRANSCRIPT
PowerPoint MINITAB MINITAB 3MLG Six Sigma
486 486
35 MB
CD-ROM
*
SAS,SPSS,MINITAB.MINITAB1972,MINITABEXCEL
1-1 Minitab
1-2 Minitab
History window
4
projectFile Open Project.
projectFile Save Project
projectProjectdatawork- sheetgraph---projectProjectproject
project
.
2 Info window
3 Session windowHistory window
. Editor Worksheet Descriptions
3
*
MINITABproject
-----numerictext/date/timecolumnsconstant(matrices)
2. (numeric )(text)/(date/time)
1235
:“Test number 4”
/Jan-1-2000 3/13/199909:30:22AM),5/13/2000 09:30:25 AM).MINITAB
3.
T
D
/
B.Info window
C.Session window
Manip Display Data
Columns, constants, and matrices to display Acid1 Acid2
OK
*
1. Calc Make Patterned Data Simple Set of Numbers.
2. Store patterned data in
3. From first value(,To last valueIn steps of
4. List each value ()List the whole sequence ()
2).
1.2345671.231.231.234567
1).
B. ..
—
10015
B.Specify which rows to include/excludeRows that match Condition(
C. Condition 100 Sales > 100
B. By variablesok.MINITAB
*
B. Stack the following columns
C. Stock the stacked data in
B. Code data from columns
C. Into columnsgender“gender” gender
D. Original values 1:12112).
E. New
F. 8
C. Expression
1.: File Open Worksheet Data PULSE.MTW
2. Calc Calculator
4. Expression pulse2-pulse1
——
——One-way ANOVA
B. Variables.
4. By variables
Variable Sex N Mean Median TrMean StDev
Weight 1 57 158.26 155.00 157.61 18.64
2 35 123.80 122.00 123.74 13.37
Variable Sex SE Mean Minimum Maximum Q1 Q3
Weight 1 2.47 123.00 215.00 145.00 170.00
2 2.26 95.00 150.00 115.00 131.00
By variables sexsex=1)sex=2meanmean=123.80)
*
t
B. Variables Minitab
3. Variables Pulse1 OK
Session window
T Confidence Intervals
Pulse1 92 72.87 11.01 1.15 (70.59, 75.15)
B. Variables MINITAB
*
3. Variables Height Weight . OK
Session window
Correlations: Height, Weight
*
B. Response ,(Y)
C. Predictors ,(X)
2. Stat Regression Regression
3. Response WEIGHT Predictors Height OK
Regression Analysis: Weight versus Height
The regression equation is
Weight = - 205 + 5.09 Height
Constant -204.74 29.16 -7.02 0.000
Height 5.0918 0.4237 12.02 0.000
S = 14.79 R-Sq = 61.6% R-Sq(adj) = 61.2%
Analysis of Variance
Residual Error 90 19692 219
Total 91 51284
9 72.0 195.00 161.87 2.08 33.13 2.26R
25 61.0 140.00 105.86 3.62 34.14 2.38R
40 72.0 215.00 161.87 2.08 53.13 3.63R
84 68.0 110.00 141.50 1.57 -31.50 -2.14R
R denotes an observation with a large standardized residual
*
one-way ANOVA
4. Graphs
OK
One-way Analysis of Variance
Source DF SS MS F P
Sex 1 25755 25755 90.80 0.000
Error 90 25529 284
Based on Pooled StDev
Level N Mean StDev --+---------+---------+---------+----
1 57 158.26 18.64 (--*-)
2 35 123.80 13.37 (---*--)
6.bin
5.txt
*
B.
b. Frequencies are in
*
3. Classification variables Smokers Activity
4. Column percents OK
Tabulated Statistics: Smokes, Activity
Rows: Smokes Columns: Activity
1 100.00 33.33 31.15 23.81 30.43
2 -- 66.67 68.85 76.19 69.57
All 100.00 100.00 100.00 100.00 100.00
Cell Contents --
% of Col
Smokers 121=2=3=01/31/4
B.
1. Single column Subgroup size
2. Subgroups across rows of Option
μσμσ
Minitab μσ
(SPC)X-RX )R
*
1. FASTENER.MTW
2. Stat Controlharts Xbar-R
3. Single column Weights Subgroup size 5
4. Historical mean 2.4 OK
Test Results for Xbar Chart
TEST 1. One point more than 3.00 sigmas from center line.
Test Failed at points: 10
*
7.txt
*
POPLAR1.MTW
2. DATA
DiameterHeightWeight15——
*
Calc Make Patterned Data Simple Set of umbers
Store patterned data in Site Minitab C4
*
Diameter DiameterHeightWeight Select
By variable Site Minitab
Graphs
*
2 10 3.028 3.250 3.041 1.284
Height 1 10 4.098 4.120 4.175 1.103
2 10 4.255 4.865 4.351 1.254
Weight 1 10 0.3090 0.2050 0.2863 0.2528
2 10 0.399 0.380 0.356 0.366
Variable site SE Mean Minimum Maximum Q1 Q3
Diameter 1 0.290 1.060 4.090 2.120 3.245
2 0.406 1.180 4.770 1.488 4.053
Height 1 0.349 1.850 5.730 3.518 4.853
2 0.396 2.200 5.540 2.775 5.143
Weight 1 0.0800 0.0200 0.7800 0.1575 0.4600
2 0.116 0.030 1.110 0.063 0.648
(boxplot)21
2
1
1.0
0.5
0.0
site
W
e
i
g
h
t
2
1
6
5
4
3
2
site
H
e
i
g
h
t
*
1. Calc Calculator
in variable D2H .
3. Expression C1**2*C2 OK
3. X D2H OK
100
50
0
1.0
0.5
0.0
D2H
W
e
i
g
h
t
2. Variables Weight D2H OK
P-Value = 0.000
*
. POPLAR2.MTW
Predictors D2H
Graphs
Residuals for Plots Standardized
Residuals Plots Histogram of residuals Normal plot of residuals
Residuals versus the variables D2H
OK
S = 0.1298 R-Sq = 83.3% R-Sq(adj) = 82.4%
Analysis of Variance
Residual Error 18 0.3031 0.0168
Total 19 1.8187
12 126 1.1100 0.9756 0.0717 0.1344 1.24 X
15 74 0.0700 0.5779 0.0374 -0.5079 -4.09R
R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
34.bin
39.txt
40.txt
*
Enter column number or name WEIGHT
Enter row number 12 OK12 weight
Weight D2H12150.070.7
*
Regression Analysis
S = 0.03880 R-Sq = 98.5% R-Sq(adj) = 98.4%
Analysis of Variance
Residual Error 18 0.0271 0.0015
Total 19 1.8379
12 126 1.11000 1.06492 0.02142 0.04508 1.39 X
17 107 0.79000 0.90858 0.01740 -0.11858 -3.42R
R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
,, ,,
41.bin
42.txt
43.txt
*
3. PredictorX D2H OK
Site1Site2Site1Site21Treatment12Treatment23Treatment34Treatment4
Project
3.
*
Weight 298 1.099 1.640 1.994 10.255 0.594
Variable Minimum Maximum Q1 Q3
Weight -99.000 6.930 0.597 3.455
Weight-99-99
-99
-99
Vaibles Weight.
1. ManipCode Numberic to Numeric
2. Code data from columns Weight
3. Into columns Weight
4. Original values -99
5. New * OK Weight –99 *
2. Variables Age OK
Summary Statistics for Discrete Variables
Age Count
3 147
4 151
N= 298
147151
Tally
*
2. Variables age OK
Minitab age poplar3.mtwage34Minitab2poplar3.mtwage=3poplar3.mtwage=4 poplar4.mtwage=4 4YROLDS.MTW
*
3. X TreatmentMinitab treatment weight(boxplot)
4. OK
5. Edit Edit Last Dialog
6. X Site
7. OK
3. Mode Site|Treatmnt Minitab Shift + \“”
4. OK
SiteTreatmentSitep0.2190.05“”Treatmentp0.000
——“”——
General Linear Model
Treatmen fixed 4 1 2 3 4
Analysis of Variance for Weight, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
Site 1 3.112 2.424 2.424 1.52 0.219
Treatmen 3 78.005 78.275 26.092 16.39 0.000
Site*Treatmen 3 10.509 10.509 3.503 2.20 0.091
Error 140 222.873 222.873 1.592
Total 147 314.498
22 0.35000 2.91200 0.28213 -2.56200 -2.08R
42 0.64000 3.34167 0.29739 -2.70167 -2.20R
43 0.16000 3.34167 0.29739 -3.18167 -2.59R
52 0.66000 3.52250 0.28213 -2.86250 -2.33R
64 2.36000 4.90889 0.29739 -2.54889 -2.08R
69 2.12000 4.90889 0.29739 -2.78889 -2.27R
72 5.82000 3.34167 0.29739 2.47833 2.02R
R denotes an observation with a large standardized residual.
*
3. X Year(boxplot) Data DisplayIQRange BoxMinitab(boxplot)25%~75%Outlier Symbol* IQ Range Box
4. Data display display
5. Display CI Box
6.
7. For each Graph MinitabMinitab
8. Options
*
1.
*
A.POPLAR3
B.
C.2
9. Submit Commands
*
*
XR5
100205
R
3. Subgroup size 5 OK
*
Xbar
1. Stat Control Charts Xbar
4. Tests
6. OK
*
7. Window Session
TEST 1. One point more than 3.00 sigmas from center line.
Test Failed at points: 8
TEST 6. 4 out of 5 points more than 1 sigma from center line
(on one side of CL).
Test Failed at points: 12 13
3 sigma4/5 1 sigma
2. Variables Length
3. Graph
XRSUPP1 SUPP2
5. OK
599.5600Supplier1 Range Range 1.36mm
2 Single column Supp1
3 subgroup size 5 OK
*
Supplier2 X-bar R 3.720supplier13supplier2supplier1supplier2
*
supplier1
2. Variables Supp1 OK
*
8. OK
2. Single column Supp1
3. Subgroup size 5
4. Lower spec 598
5. Upper spec 602
6. Options
*
Display
Available DesignsDesigns
*
5. Number of factors 3
6. Designs
8 Number of replicates 2
9. OK
1. Factors
3. Store design in worksheet OK
4.
2.“yield”
66 661029865541076853665585108895263
2. Responses Yield
5. OK
Term Effect Coef StDev Coef T P
Constant 74.81 2.561 29.21 0.000
TEMP 1.38 0.69 2.561 0.27 0.795
PRESSURE 14.12 7.06 2.561 2.76 0.025
CATALYST -30.37 -15.19 2.561 -5.93 0.000
TEMP*PRESSURE -0.13 -0.06 2.561 -0.02 0.981
TEMP*CATALYST -1.13 -0.56 2.561 -0.22 0.832
PRESSURE*CATALYST -13.37 -6.69 2.561 -2.61 0.031
TEMP*PRESSURE*CATALYST -0.13 -0.06 2.561 -0.02 0.981
Analysis of Variance for yield (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 3 4496.19 4496.19 1498.73 14.28 0.001
2-Way Interactions 3 720.69 720.69 240.23 2.29 0.155
3-Way Interactions 1 0.06 0.06 0.06 0.00 0.981
Residual Error 8 839.50 839.50 104.94
Pure Error 8 839.50 839.50 104.94
Total 15 6056.44
*
0
1-1
*
2. Terms
3.
Include terms in the model up through order 2ABC Availble Terms
Selected terms ATemp < ATempAvailble Terms
ABAC Availble Terms
4. OK
6. Histogram,Normal plotResiduals versus fitsResiduals versus order. OK
7. OK
Fractional Factorial Fit
Term Effect Coef StDev Coef T P
Constant 74.81 2.107 35.51 0.000
PRESSURE 14.13 7.06 2.107 3.35 0.006
CATALYST -30.37 -15.19 2.107 -7.21 0.000
PRESSURE*CATALYST -13.38 -6.69 2.107 -3.17 0.008
Analysis of Variance for yield (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 2 4488.62 4488.62 2244.31 31.60 0.000
2-Way Interactions 1 715.56 715.56 715.56 10.08 0.008
Residual Error 12 852.25 852.25 71.02
Pure Error 12 852.25 852.25 71.02
Total 15 6056.44
16
*
4.
Availble BPressure > BPressure Selected
CCatalyst Selected OK
5. Interaction Setup
486 486
35 MB
CD-ROM
*
SAS,SPSS,MINITAB.MINITAB1972,MINITABEXCEL
1-1 Minitab
1-2 Minitab
History window
4
projectFile Open Project.
projectFile Save Project
projectProjectdatawork- sheetgraph---projectProjectproject
project
.
2 Info window
3 Session windowHistory window
. Editor Worksheet Descriptions
3
*
MINITABproject
-----numerictext/date/timecolumnsconstant(matrices)
2. (numeric )(text)/(date/time)
1235
:“Test number 4”
/Jan-1-2000 3/13/199909:30:22AM),5/13/2000 09:30:25 AM).MINITAB
3.
T
D
/
B.Info window
C.Session window
Manip Display Data
Columns, constants, and matrices to display Acid1 Acid2
OK
*
1. Calc Make Patterned Data Simple Set of Numbers.
2. Store patterned data in
3. From first value(,To last valueIn steps of
4. List each value ()List the whole sequence ()
2).
1.2345671.231.231.234567
1).
B. ..
—
10015
B.Specify which rows to include/excludeRows that match Condition(
C. Condition 100 Sales > 100
B. By variablesok.MINITAB
*
B. Stack the following columns
C. Stock the stacked data in
B. Code data from columns
C. Into columnsgender“gender” gender
D. Original values 1:12112).
E. New
F. 8
C. Expression
1.: File Open Worksheet Data PULSE.MTW
2. Calc Calculator
4. Expression pulse2-pulse1
——
——One-way ANOVA
B. Variables.
4. By variables
Variable Sex N Mean Median TrMean StDev
Weight 1 57 158.26 155.00 157.61 18.64
2 35 123.80 122.00 123.74 13.37
Variable Sex SE Mean Minimum Maximum Q1 Q3
Weight 1 2.47 123.00 215.00 145.00 170.00
2 2.26 95.00 150.00 115.00 131.00
By variables sexsex=1)sex=2meanmean=123.80)
*
t
B. Variables Minitab
3. Variables Pulse1 OK
Session window
T Confidence Intervals
Pulse1 92 72.87 11.01 1.15 (70.59, 75.15)
B. Variables MINITAB
*
3. Variables Height Weight . OK
Session window
Correlations: Height, Weight
*
B. Response ,(Y)
C. Predictors ,(X)
2. Stat Regression Regression
3. Response WEIGHT Predictors Height OK
Regression Analysis: Weight versus Height
The regression equation is
Weight = - 205 + 5.09 Height
Constant -204.74 29.16 -7.02 0.000
Height 5.0918 0.4237 12.02 0.000
S = 14.79 R-Sq = 61.6% R-Sq(adj) = 61.2%
Analysis of Variance
Residual Error 90 19692 219
Total 91 51284
9 72.0 195.00 161.87 2.08 33.13 2.26R
25 61.0 140.00 105.86 3.62 34.14 2.38R
40 72.0 215.00 161.87 2.08 53.13 3.63R
84 68.0 110.00 141.50 1.57 -31.50 -2.14R
R denotes an observation with a large standardized residual
*
one-way ANOVA
4. Graphs
OK
One-way Analysis of Variance
Source DF SS MS F P
Sex 1 25755 25755 90.80 0.000
Error 90 25529 284
Based on Pooled StDev
Level N Mean StDev --+---------+---------+---------+----
1 57 158.26 18.64 (--*-)
2 35 123.80 13.37 (---*--)
6.bin
5.txt
*
B.
b. Frequencies are in
*
3. Classification variables Smokers Activity
4. Column percents OK
Tabulated Statistics: Smokes, Activity
Rows: Smokes Columns: Activity
1 100.00 33.33 31.15 23.81 30.43
2 -- 66.67 68.85 76.19 69.57
All 100.00 100.00 100.00 100.00 100.00
Cell Contents --
% of Col
Smokers 121=2=3=01/31/4
B.
1. Single column Subgroup size
2. Subgroups across rows of Option
μσμσ
Minitab μσ
(SPC)X-RX )R
*
1. FASTENER.MTW
2. Stat Controlharts Xbar-R
3. Single column Weights Subgroup size 5
4. Historical mean 2.4 OK
Test Results for Xbar Chart
TEST 1. One point more than 3.00 sigmas from center line.
Test Failed at points: 10
*
7.txt
*
POPLAR1.MTW
2. DATA
DiameterHeightWeight15——
*
Calc Make Patterned Data Simple Set of umbers
Store patterned data in Site Minitab C4
*
Diameter DiameterHeightWeight Select
By variable Site Minitab
Graphs
*
2 10 3.028 3.250 3.041 1.284
Height 1 10 4.098 4.120 4.175 1.103
2 10 4.255 4.865 4.351 1.254
Weight 1 10 0.3090 0.2050 0.2863 0.2528
2 10 0.399 0.380 0.356 0.366
Variable site SE Mean Minimum Maximum Q1 Q3
Diameter 1 0.290 1.060 4.090 2.120 3.245
2 0.406 1.180 4.770 1.488 4.053
Height 1 0.349 1.850 5.730 3.518 4.853
2 0.396 2.200 5.540 2.775 5.143
Weight 1 0.0800 0.0200 0.7800 0.1575 0.4600
2 0.116 0.030 1.110 0.063 0.648
(boxplot)21
2
1
1.0
0.5
0.0
site
W
e
i
g
h
t
2
1
6
5
4
3
2
site
H
e
i
g
h
t
*
1. Calc Calculator
in variable D2H .
3. Expression C1**2*C2 OK
3. X D2H OK
100
50
0
1.0
0.5
0.0
D2H
W
e
i
g
h
t
2. Variables Weight D2H OK
P-Value = 0.000
*
. POPLAR2.MTW
Predictors D2H
Graphs
Residuals for Plots Standardized
Residuals Plots Histogram of residuals Normal plot of residuals
Residuals versus the variables D2H
OK
S = 0.1298 R-Sq = 83.3% R-Sq(adj) = 82.4%
Analysis of Variance
Residual Error 18 0.3031 0.0168
Total 19 1.8187
12 126 1.1100 0.9756 0.0717 0.1344 1.24 X
15 74 0.0700 0.5779 0.0374 -0.5079 -4.09R
R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
34.bin
39.txt
40.txt
*
Enter column number or name WEIGHT
Enter row number 12 OK12 weight
Weight D2H12150.070.7
*
Regression Analysis
S = 0.03880 R-Sq = 98.5% R-Sq(adj) = 98.4%
Analysis of Variance
Residual Error 18 0.0271 0.0015
Total 19 1.8379
12 126 1.11000 1.06492 0.02142 0.04508 1.39 X
17 107 0.79000 0.90858 0.01740 -0.11858 -3.42R
R denotes an observation with a large standardized residual
X denotes an observation whose X value gives it large influence.
,, ,,
41.bin
42.txt
43.txt
*
3. PredictorX D2H OK
Site1Site2Site1Site21Treatment12Treatment23Treatment34Treatment4
Project
3.
*
Weight 298 1.099 1.640 1.994 10.255 0.594
Variable Minimum Maximum Q1 Q3
Weight -99.000 6.930 0.597 3.455
Weight-99-99
-99
-99
Vaibles Weight.
1. ManipCode Numberic to Numeric
2. Code data from columns Weight
3. Into columns Weight
4. Original values -99
5. New * OK Weight –99 *
2. Variables Age OK
Summary Statistics for Discrete Variables
Age Count
3 147
4 151
N= 298
147151
Tally
*
2. Variables age OK
Minitab age poplar3.mtwage34Minitab2poplar3.mtwage=3poplar3.mtwage=4 poplar4.mtwage=4 4YROLDS.MTW
*
3. X TreatmentMinitab treatment weight(boxplot)
4. OK
5. Edit Edit Last Dialog
6. X Site
7. OK
3. Mode Site|Treatmnt Minitab Shift + \“”
4. OK
SiteTreatmentSitep0.2190.05“”Treatmentp0.000
——“”——
General Linear Model
Treatmen fixed 4 1 2 3 4
Analysis of Variance for Weight, using Adjusted SS for Tests
Source DF Seq SS Adj SS Adj MS F P
Site 1 3.112 2.424 2.424 1.52 0.219
Treatmen 3 78.005 78.275 26.092 16.39 0.000
Site*Treatmen 3 10.509 10.509 3.503 2.20 0.091
Error 140 222.873 222.873 1.592
Total 147 314.498
22 0.35000 2.91200 0.28213 -2.56200 -2.08R
42 0.64000 3.34167 0.29739 -2.70167 -2.20R
43 0.16000 3.34167 0.29739 -3.18167 -2.59R
52 0.66000 3.52250 0.28213 -2.86250 -2.33R
64 2.36000 4.90889 0.29739 -2.54889 -2.08R
69 2.12000 4.90889 0.29739 -2.78889 -2.27R
72 5.82000 3.34167 0.29739 2.47833 2.02R
R denotes an observation with a large standardized residual.
*
3. X Year(boxplot) Data DisplayIQRange BoxMinitab(boxplot)25%~75%Outlier Symbol* IQ Range Box
4. Data display display
5. Display CI Box
6.
7. For each Graph MinitabMinitab
8. Options
*
1.
*
A.POPLAR3
B.
C.2
9. Submit Commands
*
*
XR5
100205
R
3. Subgroup size 5 OK
*
Xbar
1. Stat Control Charts Xbar
4. Tests
6. OK
*
7. Window Session
TEST 1. One point more than 3.00 sigmas from center line.
Test Failed at points: 8
TEST 6. 4 out of 5 points more than 1 sigma from center line
(on one side of CL).
Test Failed at points: 12 13
3 sigma4/5 1 sigma
2. Variables Length
3. Graph
XRSUPP1 SUPP2
5. OK
599.5600Supplier1 Range Range 1.36mm
2 Single column Supp1
3 subgroup size 5 OK
*
Supplier2 X-bar R 3.720supplier13supplier2supplier1supplier2
*
supplier1
2. Variables Supp1 OK
*
8. OK
2. Single column Supp1
3. Subgroup size 5
4. Lower spec 598
5. Upper spec 602
6. Options
*
Display
Available DesignsDesigns
*
5. Number of factors 3
6. Designs
8 Number of replicates 2
9. OK
1. Factors
3. Store design in worksheet OK
4.
2.“yield”
66 661029865541076853665585108895263
2. Responses Yield
5. OK
Term Effect Coef StDev Coef T P
Constant 74.81 2.561 29.21 0.000
TEMP 1.38 0.69 2.561 0.27 0.795
PRESSURE 14.12 7.06 2.561 2.76 0.025
CATALYST -30.37 -15.19 2.561 -5.93 0.000
TEMP*PRESSURE -0.13 -0.06 2.561 -0.02 0.981
TEMP*CATALYST -1.13 -0.56 2.561 -0.22 0.832
PRESSURE*CATALYST -13.37 -6.69 2.561 -2.61 0.031
TEMP*PRESSURE*CATALYST -0.13 -0.06 2.561 -0.02 0.981
Analysis of Variance for yield (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 3 4496.19 4496.19 1498.73 14.28 0.001
2-Way Interactions 3 720.69 720.69 240.23 2.29 0.155
3-Way Interactions 1 0.06 0.06 0.06 0.00 0.981
Residual Error 8 839.50 839.50 104.94
Pure Error 8 839.50 839.50 104.94
Total 15 6056.44
*
0
1-1
*
2. Terms
3.
Include terms in the model up through order 2ABC Availble Terms
Selected terms ATemp < ATempAvailble Terms
ABAC Availble Terms
4. OK
6. Histogram,Normal plotResiduals versus fitsResiduals versus order. OK
7. OK
Fractional Factorial Fit
Term Effect Coef StDev Coef T P
Constant 74.81 2.107 35.51 0.000
PRESSURE 14.13 7.06 2.107 3.35 0.006
CATALYST -30.37 -15.19 2.107 -7.21 0.000
PRESSURE*CATALYST -13.38 -6.69 2.107 -3.17 0.008
Analysis of Variance for yield (coded units)
Source DF Seq SS Adj SS Adj MS F P
Main Effects 2 4488.62 4488.62 2244.31 31.60 0.000
2-Way Interactions 1 715.56 715.56 715.56 10.08 0.008
Residual Error 12 852.25 852.25 71.02
Pure Error 12 852.25 852.25 71.02
Total 15 6056.44
16
*
4.
Availble BPressure > BPressure Selected
CCatalyst Selected OK
5. Interaction Setup