8.1. Recreate the simulated data from Exercise 7.2:
set.seed(200)
simulated <- mlbench.friedman1(200, sd = 1)
simulated <- cbind(simulated$x, simulated$y)
simulated <- as.data.frame(simulated)
colnames(simulated)[ncol(simulated)] <- "y"a.
model1 <- randomForest(y ~ ., data = simulated, importance = TRUE, ntree = 1000)
rfImp1 <- varImp(model1, scale = FALSE)rfImp1## Overall
## V1 8.83890885
## V2 6.49023056
## V3 0.67583163
## V4 7.58822553
## V5 2.27426009
## V6 0.17436781
## V7 0.15136583
## V8 -0.03078937
## V9 -0.02989832
## V10 -0.08529218Did the random forest model significantly use the uninformative predictors (V6 – V10)?
No, by usinng the random forest model on this set of data, we see that the model prefers the informative predictors, not the less informative predictors- as we had seen in exercise 7.2
b.
set.seed(200)
simulated$duplicate1 <- simulated$V1 + rnorm(200) * .1
cor(simulated$duplicate1, simulated$V1)## [1] 0.9497025set.seed(200)
model2 <- randomForest(y ~ ., data = simulated, importance = TRUE, ntree = 1000)rfImp2 <- varImp(model2, scale = FALSE)
rfImp2## Overall
## V1 6.106963692
## V2 6.202910805
## V3 0.628919280
## V4 7.019864830
## V5 2.239542012
## V6 0.106378124
## V7 -0.114253349
## V8 -0.090321828
## V9 0.009518246
## V10 -0.036864866
## duplicate1 4.039784189c.
#set.seed(200)
#control1 <- cforest_control(ntree = 1000, mtry = 5,replace = FALSE)
#cforest1 = cforest(y ~., data =simulated[, 1:11], controls = control1)
#cforest2 = cforest(y ~., data =simulated, controls = cforest_control(ntree=1000))#rfImp4 <- varImp(cforest1)
#rfImp5 <- varImp(cforest2)#rfImp4#rfImp5d. Repeat this process with different tree models, such as boosted trees and Cubist. Does the same pattern occur?
set.seed(200)
bagging1 <- bagging(y ~ ., simulated, nbagg = 50)
bagging2 <- bagging(y ~ ., simulated, nbagg = 50)rfImp6 <- varImp(bagging1)
rfImp7 <- varImp(bagging2)rfImp6## Overall
## duplicate1 1.4877500
## V1 1.7358507
## V10 0.7213331
## V2 2.0965374
## V3 1.1592776
## V4 2.6585339
## V5 2.3026767
## V6 0.9360849
## V7 0.9016297
## V8 0.5748244
## V9 0.5598549rfImp7## Overall
## duplicate1 1.6511974
## V1 1.7706522
## V10 0.6162109
## V2 2.0331616
## V3 1.0915525
## V4 2.7078564
## V5 2.3171837
## V6 0.8015329
## V7 0.8611544
## V8 0.5547294
## V9 0.7360808set.seed(200)
cubist1 <- cubist(x = simulated[, 1:10], y = simulated$y, committees = 100)
cubist2 <- cubist(x = simulated[, names(simulated) != "y"], y = simulated$y, committees = 100)rfImp8 <- varImp(cubist1)
rfImp9 <- varImp(cubist2)rfImp8## Overall
## V1 71.5
## V3 47.0
## V2 58.5
## V4 48.0
## V5 33.0
## V6 13.0
## V7 0.0
## V8 0.0
## V9 0.0
## V10 0.0rfImp9## Overall
## V1 71.0
## V3 46.5
## V2 59.0
## V4 48.0
## V5 32.5
## V6 12.0
## V8 1.0
## V7 0.0
## V9 0.0
## V10 0.0
## duplicate1 0.08.2. Use a simulation to show tree bias with different granularities.
set.seed(200)
x <- rep(0:1, each=500)
x1 <- rnorm(500, mean=0, sd=5)
x2 <- rnorm(500, mean=0, sd=10)
y <- x + x1
treebias <- data.frame(Y=y, X1=x1, X2=x2)
rparttree <- rpart(y ~ ., treebias)
rparttree## n= 1000
##
## node), split, n, deviance, yval
## * denotes terminal node
##
## 1) root 1000 23611.55000 0.5939734
## 2) Y< 0.6305264 505 4445.25200 -3.1668850
## 4) Y< -4.068518 164 886.19690 -6.6296520
## 8) Y< -7.641759 42 141.36840 -9.9689050 *
## 9) Y>=-7.641759 122 115.27610 -5.4800740 *
## 5) Y>=-4.068518 341 646.81290 -1.5015070
## 10) Y< -1.543008 165 96.34454 -2.7326260 *
## 11) Y>=-1.543008 176 65.93273 -0.3473326 *
## 3) Y>=0.6305264 495 4736.50700 4.4308080
## 6) Y< 5.597269 352 660.17210 2.8525120
## 12) Y< 3.034683 202 99.34679 1.8432850 *
## 13) Y>=3.034683 150 78.01047 4.2116040 *
## 7) Y>=5.597269 143 1041.12300 8.3158460
## 14) Y< 10.7493 124 196.44500 7.4335880 *
## 15) Y>=10.7493 19 118.24620 14.0737400 *plot(as.party(rparttree))
8.3 In stochastic gradient boosting the bagging fraction and learning rate will govern the construction of the trees as they are guided by the gradient. Although the optimal values of these parameters should be obtained through the tuning process, it is helpful to understand how the magnitudes of these parameters affect magnitudes of variable importance. Figure 8.24 provides the variable importance plots for boosting using two extreme values for the bagging fraction (0.1 and 0.9) and the learning rate (0.1 and 0.9) for the solubility data. The left-hand plot has both parameters set to 0.1, and the right-hand plot has both set to 0.9:
a. Why does the model on the right focus its importance on just the first few of predictors, whereas the model on the left spreads importance across more predictors?
This is because stochastic gradiant boosting focus on bagging fraction and learning rate. For the model on the right, there was a longer time to learn - therefore the model only focused on important predictors. The model on the left has more bagging fractions and uses more data in model construction, which would explain why there is a spread across predictors.
b. Which model do you think would be more predictive of other samples?
I think that a model with a smaller learning rate and bagging fraction (left) will be more robust and look at the magnitude of each parameters and it’s effect on variable importance.
c. How would increasing interaction depth affect the slope of predictor importance for either model in Fig. 8.24?
Increasing the interaction depth would spread out the importance of of the predictors more. The slope of each model would become steeper.
8.7 Refer to Exercises 6.3 and 7.5 which describe a chemical manufacturing process. Use the same data imputation, data splitting, and pre-processing steps as before and train several tree-based models.
data("ChemicalManufacturingProcess")
predictors <- subset(ChemicalManufacturingProcess,select= -Yield)
yield <- subset(ChemicalManufacturingProcess,select="Yield")
samples = dim(predictors)[1]
features = dim(predictors)[2]
missing_rows = apply(predictors, 1, function(x) sum(is.na(x)))
for(x in 1:features ){
blanks = is.na(predictors[,x] )
predictors[blanks,x] = missing_rows[x]
}
training_data <- createDataPartition(yield$Yield,
p = 0.75,
list = FALSE)
train_predictors <- predictors[training_data,]
train_yield <- yield[training_data,]
test_predictors <- predictors[-training_data,]
test_yield <- yield[-training_data,]Classification and Regression Tree
set.seed(200)
tuneGrid1 <- expand.grid(maxdepth= seq(1,10,by=1))
tree1 <- train(x = train_predictors, y = train_yield, method = "rpart2",
metric = "Rsquared", tuneGrid = tuneGrid1, trControl = trainControl(method = "boot", number = 25))tree1_pred = predict(tree1, test_predictors)
postResample(pred = tree1_pred, obs = test_yield)## RMSE Rsquared MAE
## 1.5773000 0.3322552 1.3103436Rule-Based Cubist (CUBE)
set.seed(200)
tuneGrid2 <- expand.grid(committees = c(1,5,10,20,50,100), neighbors = c(0,1,3,5,7))
tree2 <- train(x = train_predictors, y = train_yield, method = "cubist",
metric = "Rsquared", tuneGrid = tuneGrid2, trControl = trainControl(method = "boot", number = 25))tree2_pred = predict(tree2, test_predictors)
postResample(pred = tree2_pred, obs = test_yield)## RMSE Rsquared MAE
## 1.0360057 0.6282869 0.7138540GBM
set.seed(200)
tuneGrid3 <- expand.grid(interaction.depth = seq(1, 7, by = 2), n.trees = seq(100, 1000, by = 50), shrinkage = c(0.01, 0.1), n.minobsinnode=c(3))
tree3 <- train(x = train_predictors, y = train_yield, method = "gbm", metric = "Rsquared", tuneGrid = tuneGrid3, verbose = FALSE)tree3_pred = predict(tree3, test_predictors)
postResample(pred = tree3_pred, obs = test_yield)## RMSE Rsquared MAE
## 1.2377127 0.4822295 0.9538097random forest
set.seed(200)
tuneGrid4 <- expand.grid(mtry=seq(2,14,by=2))
tree4 <- train(x = train_predictors, y = train_yield, method = "rf",
metric = "Rsquared", tuneGrid = tuneGrid4, trControl = trainControl(method = "boot", number = 25))tree4_pred = predict(tree4, test_predictors)
postResample(pred = tree4_pred, obs = test_yield)## RMSE Rsquared MAE
## 1.1569418 0.5257388 0.9329094The CUBE model has the best RMSE score of the 4 models.
Which predictors are most important in the optimal tree-based regression model? Do either the biological or process variables dominate the list? How do the top 10 important predictors compare to the top 10 predictors from the optimal linear and nonlinear models?
summary(tree2)##
## Call:
## cubist.default(x = x, y = y, committees = param$committees)
##
##
## Cubist [Release 2.07 GPL Edition] Sun Apr 28 18:29:30 2019
## ---------------------------------
##
## Target attribute `outcome'
##
## Read 132 cases (58 attributes) from undefined.data
##
## Model 1:
##
## Rule 1/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 0.943]
##
## outcome = -18.942 + 0.66 ManufacturingProcess09
## + 0.183 ManufacturingProcess32
##
## Model 2:
##
## Rule 2/1: [32 cases, mean 38.130, range 35.25 to 39.84, est err 0.924]
##
## if
## BiologicalMaterial03 <= 66.61
## BiologicalMaterial11 <= 145.07
## ManufacturingProcess13 > 34.2
## then
## outcome = 34.909 + 0.0058 ManufacturingProcess16
## - 0.264 BiologicalMaterial06 - 0.0134 ManufacturingProcess20
## + 0.0078 ManufacturingProcess19 + 0.048 ManufacturingProcess32
## - 0.066 BiologicalMaterial02 + 0.049 BiologicalMaterial03
## - 0.18 ManufacturingProcess13 + 0.09 BiologicalMaterial05
##
## Rule 2/2: [11 cases, mean 40.308, range 37.92 to 42.58, est err 1.153]
##
## if
## BiologicalMaterial03 <= 66.61
## BiologicalMaterial11 > 145.07
## ManufacturingProcess13 > 34.2
## then
## outcome = 85.725 - 0.834 BiologicalMaterial03
## + 0.00307 ManufacturingProcess16 + 0.141 BiologicalMaterial11
## - 0.0071 ManufacturingProcess20 - 0.33 ManufacturingProcess13
## - 0.67 BiologicalMaterial09 + 0.0041 ManufacturingProcess19
## + 0.026 ManufacturingProcess32 - 0.035 BiologicalMaterial02
## + 0.05 BiologicalMaterial05
##
## Rule 2/3: [41 cases, mean 40.782, range 38.37 to 43.88, est err 0.958]
##
## if
## BiologicalMaterial03 > 66.61
## ManufacturingProcess13 > 34.2
## then
## outcome = -66.567 + 0.0236 ManufacturingProcess26
## + 0.02808 ManufacturingProcess16
## - 0.0556 ManufacturingProcess20 - 0.341 BiologicalMaterial02
## + 0.016 ManufacturingProcess19 + 0.099 ManufacturingProcess32
## + 0.1 BiologicalMaterial03 - 0.37 ManufacturingProcess13
## + 0.19 BiologicalMaterial05
##
## Rule 2/4: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.647]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 97.382 - 2.54 ManufacturingProcess13
## + 0.891 ManufacturingProcess01 + 0.0124 ManufacturingProcess04
## + 0.19 ManufacturingProcess30 + 0.02 BiologicalMaterial11
## + 0.0012 ManufacturingProcess19 - 0.04 ManufacturingProcess17
## - 0.001 ManufacturingProcess20 + 0.02 BiologicalMaterial05
##
## Rule 2/5: [13 cases, mean 43.175, range 41.45 to 46.34, est err 1.847]
##
## if
## BiologicalMaterial11 > 150.06
## ManufacturingProcess13 <= 34.2
## then
## outcome = 128.705 - 3.16 ManufacturingProcess13
## + 0.0172 ManufacturingProcess04 + 0.029 BiologicalMaterial11
##
## Model 3:
##
## Rule 3/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 0.952]
##
## outcome = -26.622 + 0.76 ManufacturingProcess09
## + 0.203 ManufacturingProcess32
##
## Model 4:
##
## Rule 4/1: [10 cases, mean 39.238, range 35.25 to 42.73, est err 2.837]
##
## if
## BiologicalMaterial12 <= 19.1
## then
## outcome = -127.207 + 11.44 BiologicalMaterial12
## - 1.43 ManufacturingProcess17 + 0.007 ManufacturingProcess32
##
## Rule 4/2: [53 cases, mean 39.723, range 35.25 to 43.44, est err 0.826]
##
## if
## ManufacturingProcess17 > 34.6
## then
## outcome = 21.278 + 0.242 ManufacturingProcess32
## - 0.57 ManufacturingProcess17
##
## Rule 4/3: [43 cases, mean 40.057, range 37.14 to 44.35, est err 1.231]
##
## if
## BiologicalMaterial02 <= 56.87
## BiologicalMaterial12 > 19.1
## ManufacturingProcess17 <= 34.6
## ManufacturingProcess39 > 6.7
## then
## outcome = 177.656 - 10.48 ManufacturingProcess39
## - 2.87 ManufacturingProcess17 + 0.499 BiologicalMaterial03
## + 0.08 BiologicalMaterial12 - 0.005 ManufacturingProcess02
##
## Rule 4/4: [79 cases, mean 40.537, range 37.14 to 46.34, est err 1.268]
##
## if
## ManufacturingProcess17 <= 34.6
## then
## outcome = 63.569 - 1.54 ManufacturingProcess17
## + 0.69 BiologicalMaterial12 + 0.083 ManufacturingProcess32
## + 0.00013 ManufacturingProcess26
##
## Rule 4/5: [25 cases, mean 41.253, range 38.37 to 46.34, est err 0.956]
##
## if
## BiologicalMaterial02 > 56.87
## ManufacturingProcess17 <= 34.6
## ManufacturingProcess39 > 6.7
## then
## outcome = 82.516 - 2.25 ManufacturingProcess17
## + 0.619 BiologicalMaterial02 - 0.78 ManufacturingProcess39
## + 0.12 BiologicalMaterial12 - 0.008 ManufacturingProcess02
##
## Model 5:
##
## Rule 5/1: [37 cases, mean 38.699, range 35.25 to 41.42, est err 0.710]
##
## if
## ManufacturingProcess09 <= 44.92
## then
## outcome = 5.13 + 0.00583 ManufacturingProcess26
## - 0.00724 ManufacturingProcess25 + 0.172 BiologicalMaterial03
## + 0.077 ManufacturingProcess32 + 0.23 ManufacturingProcess09
##
## Rule 5/2: [95 cases, mean 40.799, range 37.51 to 46.34, est err 1.283]
##
## if
## ManufacturingProcess09 > 44.92
## then
## outcome = 11.337 + 0.00802 ManufacturingProcess26
## - 0.00993 ManufacturingProcess25
## + 0.032 ManufacturingProcess04 + 0.41 ManufacturingProcess10
## - 0.38 ManufacturingProcess17 + 0.084 ManufacturingProcess32
## - 0.22 ManufacturingProcess13 - 0.025 ManufacturingProcess24
## + 0.05 ManufacturingProcess09
##
## Rule 5/3: [51 cases, mean 41.848, range 38.81 to 46.34, est err 1.505]
##
## if
## ManufacturingProcess09 > 44.92
## ManufacturingProcess32 > 158
## then
## outcome = -191.395 + 0.0915 ManufacturingProcess04
## - 0.333 ManufacturingProcess28 + 1 ManufacturingProcess09
## + 0.255 ManufacturingProcess32 + 0.44 ManufacturingProcess42
## + 0.0154 ManufacturingProcess20 - 0.55 ManufacturingProcess13
## - 0.081 ManufacturingProcess24 + 0.112 BiologicalMaterial03
##
## Model 6:
##
## Rule 6/1: [39 cases, mean 38.451, range 35.25 to 42.58, est err 0.886]
##
## if
## BiologicalMaterial11 <= 145.37
## ManufacturingProcess32 <= 159
## then
## outcome = 37.808 - 0.014 ManufacturingProcess16
## + 0.013 ManufacturingProcess15 - 0.49 ManufacturingProcess17
## + 0.29 BiologicalMaterial05 - 0.26 ManufacturingProcess13
## + 0.045 ManufacturingProcess32
##
## Rule 6/2: [53 cases, mean 39.723, range 35.25 to 43.44, est err 1.284]
##
## if
## ManufacturingProcess17 > 34.6
## then
## outcome = 33.803 + 0.146 ManufacturingProcess32
## - 0.7 ManufacturingProcess13 - 0.5 ManufacturingProcess17
## + 0.0043 ManufacturingProcess15
##
## Rule 6/3: [37 cases, mean 40.032, range 38.2 to 43.12, est err 0.809]
##
## if
## BiologicalMaterial11 > 145.37
## ManufacturingProcess32 <= 159
## then
## outcome = 130.901 + 0.577 ManufacturingProcess01
## - 0.00238 ManufacturingProcess16 - 0.172 BiologicalMaterial11
## - 0.0142 ManufacturingProcess18 - 0.075 ManufacturingProcess24
## - 0.08 ManufacturingProcess17 + 0.0017 ManufacturingProcess15
## + 0.05 BiologicalMaterial05
##
## Rule 6/4: [14 cases, mean 41.761, range 38.95 to 44.35, est err 2.045]
##
## if
## BiologicalMaterial02 <= 56.36
## ManufacturingProcess17 <= 34.6
## ManufacturingProcess32 > 159
## then
## outcome = 112.03 - 0.991 BiologicalMaterial02
## + 0.918 BiologicalMaterial03 - 2.4 ManufacturingProcess17
## + 0.006 ManufacturingProcess32 - 0.03 ManufacturingProcess13
## + 0.0004 ManufacturingProcess15
##
## Rule 6/5: [17 cases, mean 42.160, range 39.74 to 46.34, est err 1.501]
##
## if
## BiologicalMaterial02 > 56.36
## ManufacturingProcess17 <= 34.6
## ManufacturingProcess32 > 159
## then
## outcome = -56.709 + 0.731 BiologicalMaterial02
## - 2.25 ManufacturingProcess17 + 0.209 ManufacturingProcess32
## + 0.0208 ManufacturingProcess20
##
## Model 7:
##
## Rule 7/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.026]
##
## outcome = -10.549 + 0.83 ManufacturingProcess09
## + 0.15 ManufacturingProcess32 - 0.81 BiologicalMaterial12
## + 0.108 BiologicalMaterial06
##
## Model 8:
##
## Rule 8/1: [76 cases, mean 39.221, range 35.25 to 43.12, est err 1.394]
##
## if
## ManufacturingProcess32 <= 159
## then
## outcome = 42.89 + 11.05 ManufacturingProcess34
## - 0.054 ManufacturingProcess35 + 0.59 BiologicalMaterial08
## - 0.24 ManufacturingProcess17 - 0.56 BiologicalMaterial09
##
## Rule 8/2: [46 cases, mean 39.505, range 37.51 to 43.12, est err 1.150]
##
## if
## BiologicalMaterial03 > 65.05
## ManufacturingProcess32 <= 159
## then
## outcome = 13.809 + 0.322 ManufacturingProcess32
## - 1.67 BiologicalMaterial09 + 0.75 ManufacturingProcess34
## - 0.0032 ManufacturingProcess35 + 0.19 BiologicalMaterial08
## - 0.08 ManufacturingProcess17 - 0.09 ManufacturingProcess13
##
## Rule 8/3: [5 cases, mean 41.546, range 40.96 to 42.58, est err 3.088]
##
## if
## BiologicalMaterial03 <= 65.05
## BiologicalMaterial11 > 145.07
## ManufacturingProcess32 <= 159
## then
## outcome = 44.207 + 0.113 ManufacturingProcess32
## - 0.52 ManufacturingProcess13
##
## Rule 8/4: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.026]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = 24.215 + 0.37 BiologicalMaterial03
## - 0.255 BiologicalMaterial02 - 0.66 ManufacturingProcess17
## - 0.084 ManufacturingProcess02 + 0.0067 ManufacturingProcess20
##
## Rule 8/5: [6 cases, mean 44.105, range 42.61 to 46.34, est err 4.207]
##
## if
## ManufacturingProcess17 <= 32.5
## then
## outcome = 47.11
##
## Model 9:
##
## Rule 9/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.015]
##
## outcome = -16.239 + 0.82 ManufacturingProcess09
## + 0.12 ManufacturingProcess32
##
## Model 10:
##
## Rule 10/1: [51 cases, mean 38.676, range 36.12 to 40.41, est err 1.026]
##
## if
## ManufacturingProcess17 > 33.5
## ManufacturingProcess32 <= 158
## then
## outcome = 33.852 + 0.35 BiologicalMaterial04
##
## Rule 10/2: [126 cases, mean 40.025, range 35.25 to 43.88, est err 1.232]
##
## if
## ManufacturingProcess17 > 32.5
## then
## outcome = -17.349 + 0.00266 ManufacturingProcess26
## - 0.00347 ManufacturingProcess27 + 0.402 BiologicalMaterial02
## + 0.163 ManufacturingProcess32 - 1.12 BiologicalMaterial01
## - 0.23 ManufacturingProcess17 - 0.078 ManufacturingProcess22
## + 0.0053 ManufacturingProcess20 - 0.03 ManufacturingProcess43
##
## Rule 10/3: [12 cases, mean 41.410, range 38.81 to 43.88, est err 3.147]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -12.457 + 1.987 BiologicalMaterial02
## - 9.67 BiologicalMaterial08 + 5.91 BiologicalMaterial12
## - 0.05 ManufacturingProcess43
##
## Rule 10/4: [32 cases, mean 41.477, range 37.89 to 46.34, est err 2.067]
##
## if
## ManufacturingProcess17 <= 33.5
## then
## outcome = 14.033 + 0.456 BiologicalMaterial11
## - 0.257 ManufacturingProcess32
##
## Rule 10/5: [6 cases, mean 44.105, range 42.61 to 46.34, est err 4.925]
##
## if
## ManufacturingProcess17 <= 32.5
## then
## outcome = -9.268 + 0.00721 ManufacturingProcess26
## - 0.00937 ManufacturingProcess27 - 0.67 ManufacturingProcess17
## + 0.0142 ManufacturingProcess20 + 0.154 BiologicalMaterial02
## + 0.021 ManufacturingProcess32
##
## Model 11:
##
## Rule 11/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.172]
##
## outcome = -36.976 - 0.18 ManufacturingProcess33
## + 1.01 ManufacturingProcess42 + 0.94 ManufacturingProcess09
## + 0.217 ManufacturingProcess32
##
## Model 12:
##
## Rule 12/1: [5 cases, mean 38.346, range 35.25 to 39.38, est err 4.313]
##
## if
## BiologicalMaterial03 <= 64.07
## ManufacturingProcess32 > 158
## then
## outcome = 16.387 + 0.323 BiologicalMaterial03
##
## Rule 12/2: [38 cases, mean 38.537, range 36.12 to 40.66, est err 1.102]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 79.06 - 6.91 ManufacturingProcess39
## - 3.02 BiologicalMaterial12 + 0.421 BiologicalMaterial11
## - 0.1 ManufacturingProcess17 + 0.022 ManufacturingProcess32
## + 0.0027 ManufacturingProcess19
## - 0.0021 ManufacturingProcess20 + 0.05 BiologicalMaterial05
##
## Rule 12/3: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.276]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 103.825 - 1.69 BiologicalMaterial12
## - 0.294 BiologicalMaterial03 - 0.079 ManufacturingProcess02
## - 0.12 ManufacturingProcess17 - 0.07 ManufacturingProcess09
##
## Rule 12/4: [12 cases, mean 39.917, range 38.2 to 41.45, est err 2.520]
##
## if
## BiologicalMaterial03 <= 66.95
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 222.006 - 1.598 BiologicalMaterial03
## - 2.42 BiologicalMaterial12 - 0.6 ManufacturingProcess09
##
## Rule 12/5: [71 cases, mean 41.069, range 38.37 to 46.34, est err 1.777]
##
## if
## BiologicalMaterial03 > 64.07
## BiologicalMaterial04 > 11.95
## then
## outcome = 99.567 + 0.677 BiologicalMaterial03
## - 0.55 BiologicalMaterial02 - 0.244 ManufacturingProcess02
## - 0.185 BiologicalMaterial11 - 0.0078 ManufacturingProcess20
## - 0.18 ManufacturingProcess17
##
## Rule 12/6: [13 cases, mean 41.197, range 36.83 to 44.35, est err 1.581]
##
## if
## BiologicalMaterial04 <= 11.95
## ManufacturingProcess32 > 158
## then
## outcome = 116.473 - 8.8 BiologicalMaterial04
## + 0.0412 ManufacturingProcess35 + 0.08 BiologicalMaterial03
## - 0.07 BiologicalMaterial02 - 0.16 ManufacturingProcess17
## - 0.017 ManufacturingProcess02 + 0.0025 ManufacturingProcess20
##
## Rule 12/7: [29 cases, mean 41.656, range 39.14 to 46.34, est err 1.667]
##
## if
## BiologicalMaterial04 > 11.95
## ManufacturingProcess13 <= 33.9
## then
## outcome = 126.24 - 3.51 ManufacturingProcess13
## + 0.263 BiologicalMaterial11 + 0.42 BiologicalMaterial04
## - 0.51 ManufacturingProcess17 + 0.029 BiologicalMaterial03
## - 0.021 BiologicalMaterial02 - 0.005 ManufacturingProcess02
## + 0.0007 ManufacturingProcess20
##
## Model 13:
##
## Rule 13/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.138]
##
## outcome = -9.678 + 0.267 ManufacturingProcess32
## + 0.71 ManufacturingProcess42 - 0.116 ManufacturingProcess33
## + 0.65 ManufacturingProcess09 - 0.41 ManufacturingProcess17
## - 0.13 BiologicalMaterial03
##
## Model 14:
##
## Rule 14/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.383]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 87.217 - 6.95 ManufacturingProcess39
##
## Rule 14/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.216]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = 43.019 + 0.314 ManufacturingProcess08
## - 0.313 ManufacturingProcess07 + 0.289 BiologicalMaterial03
## - 0.73 ManufacturingProcess17 - 0.1 ManufacturingProcess02
## - 0.141 BiologicalMaterial06 + 0.072 ManufacturingProcess32
##
## Model 15:
##
## Rule 15/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.235]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -28.327 + 0.275 ManufacturingProcess32
## + 0.9 ManufacturingProcess09 - 0.315 BiologicalMaterial06
## - 0.06 BiologicalMaterial09
##
## Rule 15/2: [43 cases, mean 40.823, range 38.13 to 44.35, est err 1.322]
##
## if
## BiologicalMaterial04 <= 14.2
## ManufacturingProcess13 <= 34.2
## then
## outcome = 179.432 - 4.15 ManufacturingProcess13
## - 0.84 BiologicalMaterial04 - 0.151 ManufacturingProcess28
## + 0.27 BiologicalMaterial08 - 0.32 BiologicalMaterial09
## + 0.002 ManufacturingProcess18 + 7e-05 ManufacturingProcess26
## - 0.07 ManufacturingProcess21 + 0.02 ManufacturingProcess09
##
## Rule 15/3: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.947]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 142.38 - 3.76 ManufacturingProcess13
## - 0.104 ManufacturingProcess28 + 0.63 BiologicalMaterial08
## - 0.76 BiologicalMaterial09 + 0.0047 ManufacturingProcess18
## + 0.00016 ManufacturingProcess26 - 0.16 ManufacturingProcess21
## + 0.05 ManufacturingProcess09
##
## Model 16:
##
## Rule 16/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.462]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 37.062
##
## Rule 16/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.260]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = -12.379 + 0.0554 ManufacturingProcess04
## - 0.291 ManufacturingProcess07 + 0.424 BiologicalMaterial03
## + 0.21 ManufacturingProcess32 - 0.286 BiologicalMaterial02
## + 0.0142 ManufacturingProcess35 - 0.105 ManufacturingProcess33
## - 0.096 ManufacturingProcess02 - 0.75 ManufacturingProcess21
## + 0.56 BiologicalMaterial12 + 0.22 ManufacturingProcess30
## - 0.89 BiologicalMaterial09 - 0.029 ManufacturingProcess31
## + 0.17 ManufacturingProcess09
##
## Model 17:
##
## Rule 17/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.297]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -24.252 + 0.28 ManufacturingProcess32
## + 0.82 ManufacturingProcess09 - 0.356 BiologicalMaterial06
##
## Rule 17/2: [43 cases, mean 40.823, range 38.13 to 44.35, est err 1.310]
##
## if
## BiologicalMaterial04 <= 14.2
## ManufacturingProcess13 <= 34.2
## then
## outcome = 182.075 - 3.88 ManufacturingProcess13
## - 1.21 BiologicalMaterial04 + 0.18 BiologicalMaterial10
## + 0.015 ManufacturingProcess32 + 0.03 ManufacturingProcess09
##
## Rule 17/3: [5 cases, mean 43.342, range 41.49 to 46.34, est err 3.364]
##
## if
## BiologicalMaterial04 > 14.2
## ManufacturingProcess13 <= 34.2
## then
## outcome = 192.892 - 4.51 ManufacturingProcess13
## - 0.04 BiologicalMaterial04 + 0.1 BiologicalMaterial10
## + 0.009 ManufacturingProcess32 + 0.02 ManufacturingProcess09
##
## Model 18:
##
## Rule 18/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.708]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 90.16 - 7.4 ManufacturingProcess39
##
## Rule 18/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.334]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = -91.513 + 0.0848 ManufacturingProcess04
## + 0.623 BiologicalMaterial06 - 0.355 BiologicalMaterial02
## + 0.175 ManufacturingProcess32 - 0.93 ManufacturingProcess21
## + 0.7 BiologicalMaterial08 + 0.04 ManufacturingProcess09
##
## Model 19:
##
## Rule 19/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.341]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -17.131 + 0.255 ManufacturingProcess32
## + 0.83 ManufacturingProcess09 - 0.374 BiologicalMaterial06
## - 0.11 ManufacturingProcess13 + 0.02 BiologicalMaterial02
##
## Rule 19/2: [43 cases, mean 40.823, range 38.13 to 44.35, est err 1.580]
##
## if
## BiologicalMaterial04 <= 14.2
## ManufacturingProcess13 <= 34.2
## then
## outcome = 197.047 - 4.31 ManufacturingProcess13
## - 1.05 BiologicalMaterial04 + 0.00062 ManufacturingProcess27
## - 0.23 ManufacturingProcess39 + 0.1 BiologicalMaterial01
## - 0.015 BiologicalMaterial06 + 0.02 BiologicalMaterial05
## - 0.006 ManufacturingProcess32 + 0.02 ManufacturingProcess09
## + 0.007 BiologicalMaterial02
##
## Rule 19/3: [5 cases, mean 43.342, range 41.49 to 46.34, est err 3.012]
##
## if
## BiologicalMaterial04 > 14.2
## ManufacturingProcess13 <= 34.2
## then
## outcome = 163.005 - 3.6 ManufacturingProcess13
## + 0.00215 ManufacturingProcess27 - 1.17 ManufacturingProcess39
## + 0.52 BiologicalMaterial01 - 0.043 ManufacturingProcess32
## + 0.11 BiologicalMaterial05 - 0.046 BiologicalMaterial06
## + 0.022 BiologicalMaterial02 + 0.05 ManufacturingProcess09
##
## Model 20:
##
## Rule 20/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.851]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 92.263 - 7.71 ManufacturingProcess39
##
## Rule 20/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.258]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = 8.088 - 0.01371 ManufacturingProcess25
## + 0.00676 ManufacturingProcess26
## + 1.238 ManufacturingProcess29 - 0.357 BiologicalMaterial02
## + 0.263 BiologicalMaterial03 + 0.284 BiologicalMaterial06
## - 1 ManufacturingProcess21 + 0.132 ManufacturingProcess32
##
## Model 21:
##
## Rule 21/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.283]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -11.563 + 0.234 ManufacturingProcess32
## + 0.76 ManufacturingProcess09 - 0.338 BiologicalMaterial06
## - 0.15 ManufacturingProcess13 + 0.034 BiologicalMaterial02
##
## Rule 21/2: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.589]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 171.637 - 4.04 ManufacturingProcess13
## + 1.85 ManufacturingProcess42 - 0.295 ManufacturingProcess33
## + 0.486 BiologicalMaterial06 - 0.277 BiologicalMaterial03
## - 0.25 ManufacturingProcess10
##
## Model 22:
##
## Rule 22/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 2.084]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 90.171 - 7.45 ManufacturingProcess39
##
## Rule 22/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.301]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = 6.136 + 0.01907 ManufacturingProcess26
## - 0.02371 ManufacturingProcess25 + 0.432 BiologicalMaterial06
## - 1.14 ManufacturingProcess21 - 0.225 BiologicalMaterial02
## + 0.159 ManufacturingProcess32
##
## Model 23:
##
## Rule 23/1: [47 cases, mean 38.822, range 36.12 to 41.43, est err 1.125]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 <= 158
## then
## outcome = 88.343 - 0.32 ManufacturingProcess33
## + 0.00251 ManufacturingProcess26
## - 0.212 ManufacturingProcess31 - 0.183 BiologicalMaterial02
## + 0.118 BiologicalMaterial03 + 0.073 ManufacturingProcess32
## - 0.0077 ManufacturingProcess18 - 0.03 ManufacturingProcess13
##
## Rule 23/2: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.220]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 75.577 + 0.1091 ManufacturingProcess04
## - 4.05 ManufacturingProcess13
##
## Rule 23/3: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.335]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = -47.766 + 0.0714 ManufacturingProcess04
## + 0.00183 ManufacturingProcess26
## - 0.155 ManufacturingProcess31 + 0.243 ManufacturingProcess32
## + 0.74 ManufacturingProcess09 - 1.08 BiologicalMaterial08
## - 0.134 BiologicalMaterial02 + 0.086 BiologicalMaterial03
## - 0.0056 ManufacturingProcess18 - 0.07 ManufacturingProcess13
##
## Model 24:
##
## Rule 24/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.724]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 36.799
##
## Rule 24/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.229]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = -15.116 - 0.59 ManufacturingProcess17
## + 0.92 BiologicalMaterial08 + 0.0082 ManufacturingProcess15
## - 0.33 ManufacturingProcess13 + 0.0071 ManufacturingProcess19
## - 0.0053 ManufacturingProcess20 + 0.065 BiologicalMaterial06
## - 0.017 ManufacturingProcess02
##
## Model 25:
##
## Rule 25/1: [68 cases, mean 39.124, range 36.12 to 42.31, est err 1.270]
##
## if
## ManufacturingProcess32 <= 158
## then
## outcome = 47.487 - 0.282 ManufacturingProcess07
## + 0.053 ManufacturingProcess04 + 8.51 ManufacturingProcess34
## - 0.259 ManufacturingProcess33 + 0.145 ManufacturingProcess32
## - 0.0079 ManufacturingProcess35 - 0.71 BiologicalMaterial12
## - 0.49 ManufacturingProcess13
##
## Rule 25/2: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.432]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = -97.56 + 0.1871 ManufacturingProcess04
## - 0.217 ManufacturingProcess07 + 0.291 ManufacturingProcess32
## - 1.14 ManufacturingProcess13 + 2.36 ManufacturingProcess34
## - 0.083 ManufacturingProcess33 - 0.23 BiologicalMaterial12
##
## Model 26:
##
## Rule 26/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.929]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 49.491 - 0.203 BiologicalMaterial03
##
## Rule 26/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.176]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = 26.208 + 0.42 ManufacturingProcess09
## + 0.187 BiologicalMaterial06 - 0.42 ManufacturingProcess17
##
## Model 27:
##
## Rule 27/1: [47 cases, mean 38.771, range 36.12 to 41.45, est err 1.760]
##
## if
## BiologicalMaterial03 <= 67.28
## ManufacturingProcess32 <= 158
## then
## outcome = -110.173 - 1117 ManufacturingProcess36
## + 8.96 ManufacturingProcess34 - 0.655 BiologicalMaterial03
## + 1.2 ManufacturingProcess10 - 2.49 BiologicalMaterial01
## + 0.0229 ManufacturingProcess15 + 1.89 BiologicalMaterial08
## + 0.17 ManufacturingProcess32 - 0.02 ManufacturingProcess17
## + 0.01 BiologicalMaterial04
##
## Rule 27/2: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.808]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -22.222 + 0.307 ManufacturingProcess32
## + 0.83 ManufacturingProcess09 + 0.283 BiologicalMaterial03
## - 0.268 BiologicalMaterial02 - 0.184 BiologicalMaterial11
##
## Rule 27/3: [21 cases, mean 39.914, range 38.37 to 42.31, est err 1.308]
##
## if
## BiologicalMaterial03 > 67.28
## ManufacturingProcess32 <= 158
## then
## outcome = 77.341 - 0.521 BiologicalMaterial03
## + 1.32 ManufacturingProcess34 - 143 ManufacturingProcess36
## + 0.09 ManufacturingProcess10 - 0.1 ManufacturingProcess17
## + 0.07 BiologicalMaterial04
##
## Rule 27/4: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.257]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 92.438 - 2.88 ManufacturingProcess13
## + 0.279 BiologicalMaterial11 + 0.012 ManufacturingProcess32
## + 0.04 ManufacturingProcess09
##
## Model 28:
##
## Rule 28/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 2.070]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 109.524 - 6.28 ManufacturingProcess39
## - 0.18 ManufacturingProcess32
##
## Rule 28/2: [25 cases, mean 40.126, range 35.25 to 43.44, est err 1.419]
##
## if
## BiologicalMaterial06 > 45.48
## ManufacturingProcess17 > 35
## then
## outcome = -40.083 + 0.181 ManufacturingProcess04
## - 0.01595 ManufacturingProcess27
## - 0.589 ManufacturingProcess31 + 0.333 BiologicalMaterial06
## + 0.0104 ManufacturingProcess05 + 0.226 ManufacturingProcess29
## - 0.12 ManufacturingProcess17 - 0.018 BiologicalMaterial03
##
## Rule 28/3: [101 cases, mean 40.335, range 36.77 to 46.34, est err 1.153]
##
## if
## ManufacturingProcess17 <= 35
## then
## outcome = 34.525 - 0.00414 ManufacturingProcess27
## + 0.793 ManufacturingProcess29 + 0.0338 ManufacturingProcess04
## - 1.07 ManufacturingProcess17 - 0.206 BiologicalMaterial03
## + 0.185 BiologicalMaterial06 + 0.044 ManufacturingProcess31
## + 0.1 BiologicalMaterial02 + 0.15 ManufacturingProcess09
## + 0.018 ManufacturingProcess32
##
## Rule 28/4: [23 cases, mean 40.522, range 37.86 to 43.88, est err 2.144]
##
## if
## BiologicalMaterial02 <= 53.8
## BiologicalMaterial06 > 45.48
## ManufacturingProcess17 <= 35
## then
## outcome = 487.435 - 4.057 BiologicalMaterial02
## + 2.038 ManufacturingProcess29 - 2.75 ManufacturingProcess17
## - 0.0394 ManufacturingProcess20
##
## Model 29:
##
## Rule 29/1: [68 cases, mean 39.124, range 36.12 to 42.31, est err 1.016]
##
## if
## ManufacturingProcess32 <= 158
## then
## outcome = 0.257 + 0.01063 ManufacturingProcess26
## - 0.01315 ManufacturingProcess25 + 4.16 ManufacturingProcess34
## - 0.147 ManufacturingProcess33 + 0.231 ManufacturingProcess32
## + 0.137 BiologicalMaterial03 - 0.135 BiologicalMaterial02
## - 0.025 ManufacturingProcess28
##
## Rule 29/2: [37 cases, mean 40.838, range 35.25 to 43.88, est err 2.680]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 > 158
## then
## outcome = -93.057 + 0.558 BiologicalMaterial03
## + 1.2 ManufacturingProcess09 + 0.323 ManufacturingProcess32
## - 0.397 BiologicalMaterial02 + 0.99 ManufacturingProcess13
## - 0.86 BiologicalMaterial12 - 0.18 BiologicalMaterial04
## - 0.2 ManufacturingProcess11 - 0.032 ManufacturingProcess28
##
## Rule 29/3: [27 cases, mean 42.086, range 39.38 to 46.34, est err 1.104]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 85.973 - 2.78 ManufacturingProcess13
## + 0.288 BiologicalMaterial11 + 0.027 BiologicalMaterial03
## + 0.017 ManufacturingProcess32 - 0.018 BiologicalMaterial02
## + 0.04 ManufacturingProcess09 - 0.011 ManufacturingProcess28
##
## Model 30:
##
## Rule 30/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.998]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 23.626 + 1.25 ManufacturingProcess42
## - 0.0014 ManufacturingProcess04
##
## Rule 30/2: [92 cases, mean 39.614, range 35.25 to 43.88, est err 1.145]
##
## if
## ManufacturingProcess09 <= 46.43
## then
## outcome = 26.263 - 0.00368 ManufacturingProcess27
## + 0.82 ManufacturingProcess29 + 0.00434 ManufacturingProcess14
## - 0.00445 ManufacturingProcess16 + 0.48 ManufacturingProcess09
## - 0.42 ManufacturingProcess17 + 0.23 BiologicalMaterial05
## + 0.07 BiologicalMaterial02 - 0.037 BiologicalMaterial03
##
## Rule 30/3: [9 cases, mean 39.964, range 38.6 to 42.23, est err 1.655]
##
## if
## BiologicalMaterial01 <= 5.6
## ManufacturingProcess09 <= 46.43
## then
## outcome = 212.665 - 3.32 ManufacturingProcess17
## - 1.3 ManufacturingProcess09
##
## Rule 30/4: [40 cases, mean 41.582, range 37.86 to 46.34, est err 1.248]
##
## if
## ManufacturingProcess09 > 46.43
## then
## outcome = -47.503 + 0.3111 ManufacturingProcess06
## - 0.00195 ManufacturingProcess27
## + 0.435 ManufacturingProcess29 + 0.0023 ManufacturingProcess14
## - 0.00236 ManufacturingProcess16 - 0.67 ManufacturingProcess17
## + 0.32 BiologicalMaterial04 + 0.0075 ManufacturingProcess20
## + 0.15 ManufacturingProcess09 + 0.08 BiologicalMaterial05
##
## Model 31:
##
## Rule 31/1: [3 cases, mean 39.413, range 38.9 to 39.68, est err 3.213]
##
## if
## ManufacturingProcess32 <= 158
## ManufacturingProcess39 <= 0
## then
## outcome = 37.807
##
## Rule 31/2: [98 cases, mean 39.814, range 35.25 to 43.88, est err 1.251]
##
## if
## ManufacturingProcess17 > 33.5
## ManufacturingProcess39 > 0
## then
## outcome = 48.024 - 3.72 ManufacturingProcess39
## + 0.124 ManufacturingProcess32 - 0.05 ManufacturingProcess17
##
## Rule 31/3: [15 cases, mean 40.572, range 37.89 to 42.31, est err 1.157]
##
## if
## ManufacturingProcess17 <= 33.5
## ManufacturingProcess32 <= 158
## ManufacturingProcess39 > 0
## then
## outcome = 94.863 - 7.51 ManufacturingProcess39
##
## Rule 31/4: [37 cases, mean 40.838, range 35.25 to 43.88, est err 2.412]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 21.3 - 0.04211 ManufacturingProcess16
## - 1.219 ManufacturingProcess31 - 0.886 BiologicalMaterial02
## + 0.755 BiologicalMaterial03 + 0.0431 ManufacturingProcess15
## + 0.237 ManufacturingProcess32
##
## Rule 31/5: [27 cases, mean 42.086, range 39.38 to 46.34, est err 1.376]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 102.431 - 3.37 ManufacturingProcess13
## + 0.199 BiologicalMaterial11 + 0.134 ManufacturingProcess32
## - 0.031 ManufacturingProcess02
##
## Model 32:
##
## Rule 32/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.160]
##
## outcome = -31.853 + 1.01 ManufacturingProcess09
## + 0.169 ManufacturingProcess32 - 0.044 ManufacturingProcess02
##
## Model 33:
##
## Rule 33/1: [53 cases, mean 39.723, range 35.25 to 43.44, est err 1.047]
##
## if
## ManufacturingProcess17 > 34.6
## then
## outcome = 23.078 - 0.457 ManufacturingProcess33
## + 0.389 ManufacturingProcess32 - 0.49 ManufacturingProcess17
## + 0.189 ManufacturingProcess01 - 0.114 ManufacturingProcess22
##
## Rule 33/2: [15 cases, mean 40.087, range 37.14 to 43.88, est err 2.039]
##
## if
## BiologicalMaterial02 <= 52.67
## BiologicalMaterial12 > 19.1
## ManufacturingProcess17 <= 34.6
## then
## outcome = 142.81 - 5.47 ManufacturingProcess17
## + 1.292 BiologicalMaterial03
##
## Rule 33/3: [34 cases, mean 40.200, range 37.51 to 44.35, est err 1.859]
##
## if
## BiologicalMaterial02 > 52.67
## BiologicalMaterial06 <= 50.06
## ManufacturingProcess17 <= 34.6
## then
## outcome = -31.301 + 0.02282 ManufacturingProcess27
## + 1.624 BiologicalMaterial06 - 0.957 BiologicalMaterial02
## - 2.45 ManufacturingProcess17 + 0.353 BiologicalMaterial03
## + 0.01 ManufacturingProcess29
##
## Rule 33/4: [6 cases, mean 40.580, range 38.63 to 42.73, est err 3.898]
##
## if
## BiologicalMaterial12 <= 19.1
## ManufacturingProcess17 <= 34.6
## then
## outcome = -179.375 + 1.122 ManufacturingProcess02
## + 10.52 BiologicalMaterial12 - 0.04 ManufacturingProcess17
##
## Rule 33/5: [28 cases, mean 41.476, range 38.37 to 46.34, est err 1.869]
##
## if
## BiologicalMaterial06 > 50.06
## ManufacturingProcess17 <= 34.6
## then
## outcome = 36.831 - 2.77 ManufacturingProcess17
## + 0.551 BiologicalMaterial02 + 0.0136 ManufacturingProcess20
## + 0.007 ManufacturingProcess32
##
## Model 34:
##
## Rule 34/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.102]
##
## outcome = -40.282 - 0.337 ManufacturingProcess07
## + 0.061 ManufacturingProcess04 + 0.97 ManufacturingProcess09
## + 0.248 ManufacturingProcess32
##
## Model 35:
##
## Rule 35/1: [10 cases, mean 39.238, range 35.25 to 42.73, est err 1.946]
##
## if
## BiologicalMaterial12 <= 19.1
## then
## outcome = -105.639 + 9.54 BiologicalMaterial12
## - 0.98 ManufacturingProcess17
##
## Rule 35/2: [53 cases, mean 39.723, range 35.25 to 43.44, est err 0.924]
##
## if
## ManufacturingProcess17 > 34.6
## then
## outcome = 16.462 + 0.189 ManufacturingProcess32
## - 0.24 ManufacturingProcess13 + 0.08 BiologicalMaterial05
##
## Rule 35/3: [68 cases, mean 40.496, range 37.14 to 46.34, est err 1.183]
##
## if
## BiologicalMaterial12 > 19.1
## ManufacturingProcess17 <= 34.6
## ManufacturingProcess39 > 6.7
## then
## outcome = 143.756 - 6.77 ManufacturingProcess39
## - 2.12 ManufacturingProcess17 - 0.091 ManufacturingProcess02
## + 0.192 BiologicalMaterial03 - 0.32 ManufacturingProcess13
## + 0.003 ManufacturingProcess15 - 0.038 BiologicalMaterial02
##
## Rule 35/4: [79 cases, mean 40.537, range 37.14 to 46.34, est err 1.381]
##
## if
## ManufacturingProcess17 <= 34.6
## then
## outcome = 37.836 - 0.99 ManufacturingProcess13
## - 0.68 ManufacturingProcess17 + 0.125 BiologicalMaterial03
## + 0.0085 ManufacturingProcess15 - 0.107 BiologicalMaterial02
## + 0.01 ManufacturingProcess31 + 0.018 ManufacturingProcess32
## + 0.04 BiologicalMaterial05
##
## Model 36:
##
## Rule 36/1: [55 cases, mean 39.152, range 35.25 to 43.88, est err 1.433]
##
## if
## BiologicalMaterial12 <= 19.93
## then
## outcome = -29.301 + 11.69 ManufacturingProcess34
## - 0.092 ManufacturingProcess07 + 0.65 ManufacturingProcess09
## + 0.0116 ManufacturingProcess04 + 0.029 ManufacturingProcess08
## + 0.04 ManufacturingProcess32 + 0.18 BiologicalMaterial12
## + 0.007 ManufacturingProcess02
##
## Rule 36/2: [34 cases, mean 39.471, range 37.51 to 42.31, est err 0.909]
##
## if
## BiologicalMaterial10 > 2.32
## ManufacturingProcess09 > 44.92
## ManufacturingProcess32 <= 158
## then
## outcome = 55.161 - 0.139 ManufacturingProcess07
## - 1.21 ManufacturingProcess17 + 0.86 ManufacturingProcess10
## + 1.87 BiologicalMaterial10 + 0.191 ManufacturingProcess32
## + 0.0052 ManufacturingProcess04 + 0.06 ManufacturingProcess09
##
## Rule 36/3: [15 cases, mean 39.547, range 37.86 to 41.45, est err 1.606]
##
## if
## BiologicalMaterial10 <= 2.32
## ManufacturingProcess32 <= 158
## then
## outcome = 61.111 + 15.32 BiologicalMaterial10
## - 0.268 ManufacturingProcess32 - 0.06 ManufacturingProcess07
## - 0.05 ManufacturingProcess17
##
## Rule 36/4: [77 cases, mean 40.966, range 38.2 to 46.34, est err 1.029]
##
## if
## BiologicalMaterial12 > 19.93
## then
## outcome = 13.067 + 0.03 ManufacturingProcess04
## - 0.157 ManufacturingProcess07 + 0.3 ManufacturingProcess09
## + 0.087 ManufacturingProcess32
##
## Rule 36/5: [51 cases, mean 41.848, range 38.81 to 46.34, est err 1.621]
##
## if
## ManufacturingProcess09 > 44.92
## ManufacturingProcess32 > 158
## then
## outcome = -67.841 + 0.00505 ManufacturingProcess25
## - 0.0397 ManufacturingProcess35 + 1.31 ManufacturingProcess09
## - 0.368 ManufacturingProcess28 + 0.297 ManufacturingProcess32
## + 0.011 ManufacturingProcess04 - 0.058 ManufacturingProcess07
##
## Model 37:
##
## Rule 37/1: [51 cases, mean 38.676, range 36.12 to 40.41, est err 0.773]
##
## if
## ManufacturingProcess17 > 33.5
## ManufacturingProcess32 <= 158
## then
## outcome = 40.97 - 0.15 ManufacturingProcess17
## + 0.017 ManufacturingProcess32
##
## Rule 37/2: [47 cases, mean 40.116, range 35.25 to 43.44, est err 0.858]
##
## if
## BiologicalMaterial02 > 53.18
## ManufacturingProcess13 > 34.2
## then
## outcome = -33.891 + 0.207 ManufacturingProcess32
## + 0.54 ManufacturingProcess09 + 0.45 ManufacturingProcess13
## + 0.064 ManufacturingProcess24
##
## Rule 37/3: [13 cases, mean 41.308, range 38.81 to 43.88, est err 2.186]
##
## if
## BiologicalMaterial02 <= 53.18
## ManufacturingProcess32 > 158
## then
## outcome = -263.967 + 3.712 BiologicalMaterial02
## + 0.701 ManufacturingProcess32 - 0.01 ManufacturingProcess24
## - 0.05 ManufacturingProcess13
##
## Rule 37/4: [32 cases, mean 41.477, range 37.89 to 46.34, est err 1.671]
##
## if
## ManufacturingProcess17 <= 33.5
## then
## outcome = 38.723 - 0.131 ManufacturingProcess24
## + 0.0061 ManufacturingProcess35
##
## Rule 37/5: [27 cases, mean 42.086, range 39.38 to 46.34, est err 1.410]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 124.117 - 2.55 ManufacturingProcess13
## - 0.269 ManufacturingProcess24 + 0.036 ManufacturingProcess32
## - 0.04 ManufacturingProcess17
##
## Model 38:
##
## Rule 38/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.104]
##
## outcome = -36.191 - 0.01003 ManufacturingProcess25
## + 0.00804 ManufacturingProcess26
## - 0.01098 ManufacturingProcess16
## + 0.0104 ManufacturingProcess14 + 0.76 ManufacturingProcess09
## + 0.144 ManufacturingProcess32 + 0.43 BiologicalMaterial05
## + 0.86 BiologicalMaterial09
##
## Model 39:
##
## Rule 39/1: [76 cases, mean 39.221, range 35.25 to 43.12, est err 0.955]
##
## if
## ManufacturingProcess32 <= 159
## then
## outcome = 64.959 - 0.41 ManufacturingProcess17
## - 0.34 ManufacturingProcess13
##
## Rule 39/2: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.169]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = 59.819 - 0.53 ManufacturingProcess13
##
## Model 40:
##
## Rule 40/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.018]
##
## outcome = -32.452 + 0.89 ManufacturingProcess09
## + 0.202 ManufacturingProcess32
##
## Model 41:
##
## Rule 41/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.039]
##
## outcome = 37.257 - 0.68 ManufacturingProcess17
## + 0.13 ManufacturingProcess32 + 0.119 BiologicalMaterial06
##
## Model 42:
##
## Rule 42/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.001]
##
## outcome = -31.109 + 0.86 ManufacturingProcess09
## + 0.202 ManufacturingProcess32
##
## Model 43:
##
## Rule 43/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.042]
##
## outcome = -18.698 - 0.9 ManufacturingProcess13
## + 0.0116 ManufacturingProcess15 + 0.093 ManufacturingProcess32
## + 0.079 BiologicalMaterial03
##
## Model 44:
##
## Rule 44/1: [35 cases, mean 38.439, range 35.25 to 42.58, est err 1.463]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 <= 159
## then
## outcome = 144.459 - 5.09 ManufacturingProcess39
## - 0.00484 ManufacturingProcess16
## - 0.0194 ManufacturingProcess18 + 0.131 ManufacturingProcess32
## - 0.56 ManufacturingProcess13 + 0.0075 ManufacturingProcess19
##
## Rule 44/2: [76 cases, mean 39.221, range 35.25 to 43.12, est err 2.107]
##
## if
## ManufacturingProcess32 <= 159
## then
## outcome = 93.689 - 1.59 BiologicalMaterial12
## - 0.00205 ManufacturingProcess16
## - 0.0082 ManufacturingProcess18 + 0.056 ManufacturingProcess32
## + 0.0032 ManufacturingProcess19
##
## Rule 44/3: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.204]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 153.955 - 3.39 ManufacturingProcess13
##
## Rule 44/4: [31 cases, mean 41.137, range 36.83 to 43.88, est err 1.220]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 > 159
## then
## outcome = 212.27 - 0.0401 ManufacturingProcess18
## - 0.00287 ManufacturingProcess16 - 0.82 ManufacturingProcess38
## + 0.078 ManufacturingProcess32 + 0.0044 ManufacturingProcess19
##
## Model 45:
##
## Rule 45/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.693]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = -11.453 + 13.52 ManufacturingProcess34
## + 1.29 ManufacturingProcess42 - 9e-05 ManufacturingProcess25
## + 4e-05 ManufacturingProcess26 + 4e-05 ManufacturingProcess27
##
## Rule 45/2: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.367]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = -111.177 - 0.00991 ManufacturingProcess25
## + 0.0976 ManufacturingProcess04
## + 0.00744 ManufacturingProcess26
## + 0.0246 ManufacturingProcess35 - 578 ManufacturingProcess36
## - 0.99 ManufacturingProcess17 - 0.313 BiologicalMaterial02
## - 0.12 ManufacturingProcess02 + 0.61 ManufacturingProcess09
## + 0.164 BiologicalMaterial03 + 0.00073 ManufacturingProcess27
## + 0.092 ManufacturingProcess32 + 0.64 BiologicalMaterial01
## + 0.0093 ManufacturingProcess18 + 0.23 BiologicalMaterial05
## + 0.002 ManufacturingProcess19 - 0.0015 ManufacturingProcess20
## + 0.02 BiologicalMaterial06
##
## Model 46:
##
## Rule 46/1: [38 cases, mean 38.537, range 36.12 to 40.66, est err 1.527]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 70.994 - 0.0358 ManufacturingProcess05
## + 0.194 ManufacturingProcess07 - 0.203 ManufacturingProcess32
## + 0.02 ManufacturingProcess09 - 0.007 BiologicalMaterial06
##
## Rule 46/2: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.169]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 55.433 - 0.41 BiologicalMaterial06
## + 0.018 ManufacturingProcess32 + 0.04 ManufacturingProcess09
##
## Rule 46/3: [37 cases, mean 40.838, range 35.25 to 43.88, est err 1.608]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 120.427 - 0.01739 ManufacturingProcess27
## + 1.57 ManufacturingProcess30 + 0.293 BiologicalMaterial03
## - 0.229 BiologicalMaterial11 - 0.27 ManufacturingProcess11
## + 0.013 ManufacturingProcess32 + 0.03 ManufacturingProcess09
## - 0.009 BiologicalMaterial06
##
## Rule 46/4: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.385]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 105.29 - 2.72 ManufacturingProcess13
## + 0.201 BiologicalMaterial11 - 0.26 ManufacturingProcess23
## - 0.0006 ManufacturingProcess18
##
## Model 47:
##
## Rule 47/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.243]
##
## outcome = -28.442 + 0.02632 ManufacturingProcess26
## - 0.03149 ManufacturingProcess25 + 0.438 BiologicalMaterial06
## + 0.79 ManufacturingProcess09 - 0.257 BiologicalMaterial02
## - 0.57 ManufacturingProcess30 + 0.154 ManufacturingProcess32
## - 0.54 ManufacturingProcess21
##
## Model 48:
##
## Rule 48/1: [59 cases, mean 39.270, range 35.25 to 43.88, est err 2.075]
##
## if
## BiologicalMaterial12 <= 19.96
## then
## outcome = 107.736 - 0.877 ManufacturingProcess33
## + 0.463 ManufacturingProcess32 + 0.549 BiologicalMaterial06
## - 0.458 BiologicalMaterial03 - 1.56 ManufacturingProcess13
## - 0.6 ManufacturingProcess09
##
## Rule 48/2: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.194]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 56.378 - 0.516 BiologicalMaterial06
## + 0.727 ManufacturingProcess01 + 0.004 ManufacturingProcess32
##
## Rule 48/3: [89 cases, mean 40.582, range 35.25 to 46.34, est err 0.987]
##
## if
## BiologicalMaterial02 > 52.95
## then
## outcome = -63.918 + 0.1043 ManufacturingProcess04
## + 0.476 BiologicalMaterial02 - 0.377 BiologicalMaterial06
## + 0.164 ManufacturingProcess32 - 0.67 ManufacturingProcess13
## - 0.3 BiologicalMaterial04
##
## Rule 48/4: [12 cases, mean 41.249, range 36.83 to 44.35, est err 2.789]
##
## if
## BiologicalMaterial04 <= 11.89
## ManufacturingProcess32 > 158
## then
## outcome = 292.253 - 0.152 ManufacturingProcess04
## - 6.42 BiologicalMaterial04 - 2.43 ManufacturingProcess42
## - 0.108 BiologicalMaterial02
##
## Rule 48/5: [12 cases, mean 41.410, range 38.81 to 43.88, est err 2.599]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -390.299 + 7.424 BiologicalMaterial02
## + 1.792 ManufacturingProcess02 + 0.004 ManufacturingProcess04
## + 0.005 ManufacturingProcess32
##
## Rule 48/6: [23 cases, mean 42.199, range 39.4 to 46.34, est err 1.722]
##
## if
## BiologicalMaterial02 > 52.95
## ManufacturingProcess13 <= 33.9
## ManufacturingProcess32 > 158
## then
## outcome = 111.445 - 2.99 ManufacturingProcess13
## + 0.423 BiologicalMaterial06 + 0.0043 ManufacturingProcess04
## + 0.025 BiologicalMaterial02 + 0.008 ManufacturingProcess32
## - 0.01 BiologicalMaterial04
##
## Model 49:
##
## Rule 49/1: [27 cases, mean 38.440, range 35.25 to 40.77, est err 1.332]
##
## if
## ManufacturingProcess09 <= 44.56
## then
## outcome = -54.935 + 1.19 ManufacturingProcess09
## + 0.261 ManufacturingProcess32
##
## Rule 49/2: [76 cases, mean 39.221, range 35.25 to 43.12, est err 0.964]
##
## if
## ManufacturingProcess32 <= 159
## then
## outcome = 4.335 + 1.77 ManufacturingProcess11
## - 0.482 ManufacturingProcess01 + 0.185 ManufacturingProcess32
## - 0.68 ManufacturingProcess21 - 0.13 ManufacturingProcess13
## - 0.02 ManufacturingProcess17
##
## Rule 49/3: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.124]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = 45.667 + 0.129 BiologicalMaterial06
## - 0.3 ManufacturingProcess17
##
## Rule 49/4: [6 cases, mean 44.105, range 42.61 to 46.34, est err 1.704]
##
## if
## ManufacturingProcess17 <= 32.5
## then
## outcome = 66.133 - 0.58 ManufacturingProcess13
## + 0.086 BiologicalMaterial06 - 0.2 ManufacturingProcess17
##
## Model 50:
##
## Rule 50/1: [20 cases, mean 38.595, range 36.77 to 40.81, est err 0.965]
##
## if
## ManufacturingProcess28 <= 10.5
## ManufacturingProcess39 > 7.2
## then
## outcome = 53.414 - 0.00378 ManufacturingProcess16
## - 0.8 BiologicalMaterial10 + 0.05 ManufacturingProcess09
## + 0.012 ManufacturingProcess32
##
## Rule 50/2: [43 cases, mean 39.938, range 38.13 to 42.23, est err 0.734]
##
## if
## BiologicalMaterial02 > 52.95
## ManufacturingProcess28 <= 10.5
## then
## outcome = 37.56 - 0.00338 ManufacturingProcess16
## - 0.133 BiologicalMaterial06 + 0.111 BiologicalMaterial03
## - 0.048 ManufacturingProcess02 + 0.2 ManufacturingProcess09
## + 0.058 ManufacturingProcess32 - 0.055 ManufacturingProcess28
##
## Rule 50/3: [32 cases, mean 39.944, range 37.3 to 43.88, est err 1.266]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess39 <= 7.2
## then
## outcome = 137.682 + 0.791 BiologicalMaterial02
## - 0.0407 ManufacturingProcess18 - 0.231 ManufacturingProcess28
## + 0.169 BiologicalMaterial03 + 0.0081 ManufacturingProcess19
##
## Rule 50/4: [44 cases, mean 40.471, range 35.25 to 44.35, est err 0.892]
##
## if
## ManufacturingProcess13 > 32.9
## ManufacturingProcess28 > 10.5
## then
## outcome = 6.943 + 3.15 ManufacturingProcess28
## - 0.54 ManufacturingProcess13 - 0.124 BiologicalMaterial06
## + 0.1 BiologicalMaterial03 - 0.045 ManufacturingProcess02
## + 0.19 ManufacturingProcess09 + 0.054 ManufacturingProcess32
##
## Rule 50/5: [10 cases, mean 43.009, range 40.7 to 46.34, est err 1.275]
##
## if
## ManufacturingProcess13 <= 32.9
## then
## outcome = 331.143 - 8.83 ManufacturingProcess13
##
## Model 51:
##
## Rule 51/1: [45 cases, mean 38.728, range 36.12 to 41.45, est err 0.889]
##
## if
## BiologicalMaterial03 <= 67.17
## ManufacturingProcess32 <= 158
## then
## outcome = 0.983 - 0.204 ManufacturingProcess33
## + 5.11 ManufacturingProcess34 + 0.17 ManufacturingProcess32
## + 0.34 ManufacturingProcess09 - 0.37 BiologicalMaterial12
## + 0.41 BiologicalMaterial10 + 0.11 BiologicalMaterial05
##
## Rule 51/2: [23 cases, mean 39.897, range 38.37 to 42.31, est err 0.836]
##
## if
## BiologicalMaterial03 > 67.17
## ManufacturingProcess32 <= 158
## then
## outcome = 30.557 - 0.243 BiologicalMaterial03
## + 0.43 ManufacturingProcess09 - 0.135 ManufacturingProcess22
## + 0.056 ManufacturingProcess32 + 0.78 ManufacturingProcess34
## - 0.027 ManufacturingProcess33 - 0.15 BiologicalMaterial12
## + 0.17 BiologicalMaterial10 + 0.05 BiologicalMaterial05
##
## Rule 51/3: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.080]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = -58.46 + 0.0709 ManufacturingProcess04
## + 0.24 ManufacturingProcess32 - 0.74 ManufacturingProcess17
## + 0.72 BiologicalMaterial12 - 0.065 ManufacturingProcess24
## + 0.11 ManufacturingProcess09 + 0.05 BiologicalMaterial05
## - 0.02 ManufacturingProcess22 + 0.18 ManufacturingProcess34
##
## Model 52:
##
## Rule 52/1: [37 cases, mean 38.699, range 35.25 to 41.42, est err 0.921]
##
## if
## ManufacturingProcess09 <= 44.92
## then
## outcome = 14.67 + 0.104 BiologicalMaterial03
## - 0.098 BiologicalMaterial06 + 0.18 ManufacturingProcess09
## + 0.49 BiologicalMaterial09 + 0.19 BiologicalMaterial12
## + 0.024 ManufacturingProcess32
##
## Rule 52/2: [43 cases, mean 39.440, range 36.77 to 43.88, est err 2.391]
##
## if
## BiologicalMaterial02 <= 52.95
## then
## outcome = 52.285 - 0.321 BiologicalMaterial02
## + 0.61 ManufacturingProcess23
##
## Rule 52/3: [84 cases, mean 39.710, range 35.25 to 43.88, est err 0.881]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -16.175 + 0.77 ManufacturingProcess09
## + 0.145 ManufacturingProcess32 + 0.1 BiologicalMaterial03
## - 0.094 BiologicalMaterial06 - 0.58 BiologicalMaterial09
## + 0.18 BiologicalMaterial12 - 0.013 ManufacturingProcess28
##
## Rule 52/4: [45 cases, mean 41.236, range 38.2 to 46.34, est err 1.153]
##
## if
## ManufacturingProcess09 > 44.92
## ManufacturingProcess13 <= 34.2
## then
## outcome = 129.776 - 2.78 ManufacturingProcess13
## + 0.37 BiologicalMaterial04 - 0.113 ManufacturingProcess22
##
## Rule 52/5: [13 cases, mean 41.921, range 39.38 to 43.88, est err 2.903]
##
## if
## BiologicalMaterial02 <= 52.95
## BiologicalMaterial06 > 45.48
## ManufacturingProcess09 > 44.92
## then
## outcome = 248.477 + 0.597 BiologicalMaterial06
## - 0.0387 ManufacturingProcess19
##
## Model 53:
##
## Rule 53/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.014]
##
## outcome = -36.322 + 0.192 ManufacturingProcess32
## - 0.78 ManufacturingProcess13 + 0.0103 ManufacturingProcess15
## + 0.24 ManufacturingProcess09
##
## Model 54:
##
## Rule 54/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.036]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 89.868 - 7.27 ManufacturingProcess39
##
## Rule 54/2: [26 cases, mean 39.362, range 36.12 to 41.43, est err 1.297]
##
## if
## BiologicalMaterial06 > 45.48
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 <= 158
## then
## outcome = 200.214 - 0.02278 ManufacturingProcess14
## - 0.542 ManufacturingProcess32
## + 0.00562 ManufacturingProcess16 + 1.94 BiologicalMaterial08
## - 0.339 BiologicalMaterial06 - 0.16 ManufacturingProcess42
## - 0.041 BiologicalMaterial02 - 0.07 ManufacturingProcess17
## + 0.05 ManufacturingProcess10 - 0.1 BiologicalMaterial12
##
## Rule 54/3: [43 cases, mean 40.823, range 38.13 to 44.35, est err 1.315]
##
## if
## BiologicalMaterial04 <= 14.2
## ManufacturingProcess13 <= 34.2
## then
## outcome = 140.168 - 3.01 ManufacturingProcess13
## - 0.86 BiologicalMaterial04 + 0.451 ManufacturingProcess01
## + 0.00217 ManufacturingProcess14
## - 0.00227 ManufacturingProcess16 + 0.1 ManufacturingProcess09
## + 0.07 BiologicalMaterial05
##
## Rule 54/4: [34 cases, mean 40.997, range 35.25 to 43.88, est err 1.365]
##
## if
## BiologicalMaterial06 > 45.48
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 14.178 + 1.16 ManufacturingProcess09
## + 0.388 BiologicalMaterial06 - 0.32 BiologicalMaterial02
## - 1.44 BiologicalMaterial12 + 0.00107 ManufacturingProcess14
## - 0.00112 ManufacturingProcess16
## + 0.019 ManufacturingProcess32 - 0.05 ManufacturingProcess42
## + 0.03 BiologicalMaterial05 + 0.02 ManufacturingProcess10
## + 0.04 BiologicalMaterial08 - 0.02 ManufacturingProcess17
##
## Rule 54/5: [48 cases, mean 41.085, range 38.13 to 46.34, est err 2.073]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 157.594 - 3.5 ManufacturingProcess13
## + 0.00066 ManufacturingProcess14
## - 0.00069 ManufacturingProcess16 + 0.06 BiologicalMaterial05
## + 0.03 ManufacturingProcess09
##
## Model 55:
##
## Rule 55/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.061]
##
## outcome = 47.403 - 0.9 ManufacturingProcess17
## + 0.156 ManufacturingProcess32 - 0.073 ManufacturingProcess02
##
## Model 56:
##
## Rule 56/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.297]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = 3.338 + 0.81 ManufacturingProcess09
##
## Rule 56/2: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.030]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 142.966 - 3.16 ManufacturingProcess13
## + 0.08 ManufacturingProcess09
##
## Model 57:
##
## Rule 57/1: [42 cases, mean 38.580, range 35.25 to 42.58, est err 0.889]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 12.015 - 0.442 ManufacturingProcess33
## + 0.331 ManufacturingProcess32 + 0.73 ManufacturingProcess42
## - 0.37 ManufacturingProcess17 - 0.12 ManufacturingProcess13
## + 0.0018 ManufacturingProcess15
##
## Rule 57/2: [73 cases, mean 40.970, range 38.2 to 46.34, est err 1.677]
##
## if
## BiologicalMaterial12 > 19.96
## then
## outcome = 100.452 - 3.15 BiologicalMaterial12
## + 0.273 BiologicalMaterial11 - 0.82 ManufacturingProcess17
## - 0.206 BiologicalMaterial03 - 0.077 ManufacturingProcess02
## - 0.018 ManufacturingProcess33 + 0.1 ManufacturingProcess42
## + 0.026 ManufacturingProcess32 - 0.05 ManufacturingProcess13
## + 0.0008 ManufacturingProcess15
##
## Rule 57/3: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.290]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = -61.585 + 0.65 ManufacturingProcess42
## - 0.113 ManufacturingProcess33 + 0.183 ManufacturingProcess32
## + 0.0152 ManufacturingProcess15 - 0.53 ManufacturingProcess13
##
## Model 58:
##
## Rule 58/1: [47 cases, mean 38.883, range 35.25 to 42.58, est err 1.117]
##
## if
## BiologicalMaterial03 <= 66.25
## then
## outcome = 50.338 - 0.434 BiologicalMaterial03
## + 0.19 BiologicalMaterial11 - 0.58 ManufacturingProcess13
## - 0.98 BiologicalMaterial10 - 0.0014 ManufacturingProcess16
## + 0.26 ManufacturingProcess09 + 0.069 BiologicalMaterial06
## + 0.022 ManufacturingProcess32 - 0.27 ManufacturingProcess37
##
## Rule 58/2: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.053]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = 8.495 - 0.00456 ManufacturingProcess16
## + 0.99 ManufacturingProcess09 + 0.224 BiologicalMaterial06
## + 0.091 ManufacturingProcess32 - 0.111 BiologicalMaterial11
## - 0.87 ManufacturingProcess37
##
## Rule 58/3: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.737]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 160.271 - 3.74 ManufacturingProcess13
## + 0.019 ManufacturingProcess31 + 0.06 ManufacturingProcess09
## + 0.011 ManufacturingProcess32 + 0.011 BiologicalMaterial11
##
## Rule 58/4: [30 cases, mean 41.253, range 38.2 to 46.34, est err 1.879]
##
## if
## ManufacturingProcess02 <= 20.4
## ManufacturingProcess13 <= 34.2
## then
## outcome = -80.61 + 0.4103 ManufacturingProcess06
## - 2.8 ManufacturingProcess13 + 0.203 BiologicalMaterial11
## + 0.56 ManufacturingProcess09 + 0.231 BiologicalMaterial03
## + 0.0126 ManufacturingProcess20
##
## Model 59:
##
## Rule 59/1: [38 cases, mean 38.537, range 36.12 to 40.66, est err 1.100]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 117.406 - 8.44 ManufacturingProcess39
## - 2.33 BiologicalMaterial12 - 0.00194 ManufacturingProcess25
## + 0.00155 ManufacturingProcess26 + 0.386 BiologicalMaterial03
## + 0.014 ManufacturingProcess32
##
## Rule 59/2: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.265]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 53.79 - 0.226 BiologicalMaterial02
## + 0.0094 ManufacturingProcess05 + 0.286 ManufacturingProcess01
## - 0.65 BiologicalMaterial12 + 0.0014 ManufacturingProcess35
##
## Rule 59/3: [89 cases, mean 40.582, range 35.25 to 46.34, est err 0.996]
##
## if
## BiologicalMaterial02 > 52.95
## then
## outcome = -8.438 + 0.00511 ManufacturingProcess16
## - 0.00419 ManufacturingProcess14
## + 0.00102 ManufacturingProcess26
## - 0.00127 ManufacturingProcess25 + 0.18 ManufacturingProcess32
## + 0.37 ManufacturingProcess09
##
## Rule 59/4: [12 cases, mean 41.410, range 38.81 to 43.88, est err 1.406]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -169.784 + 3.37 BiologicalMaterial02
## + 0.527 BiologicalMaterial03 + 0.00013 ManufacturingProcess26
## - 0.00016 ManufacturingProcess25
##
## Model 60:
##
## Rule 60/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 0.893]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = -16.684 + 0.57 ManufacturingProcess09
## + 0.165 ManufacturingProcess32 - 0.38 ManufacturingProcess13
## + 0.003 ManufacturingProcess15
##
## Rule 60/2: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.625]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 28.902 - 4.49 ManufacturingProcess13
## + 0.0545 ManufacturingProcess04
## + 0.0178 ManufacturingProcess15 + 1.13 BiologicalMaterial10
## - 0.094 ManufacturingProcess28 + 0.028 ManufacturingProcess31
## + 0.005 ManufacturingProcess32
##
## Model 61:
##
## Rule 61/1: [59 cases, mean 39.270, range 35.25 to 43.88, est err 1.302]
##
## if
## BiologicalMaterial12 <= 19.96
## then
## outcome = 14.516 - 0.0024 ManufacturingProcess16
## - 0.093 ManufacturingProcess33 + 3.07 ManufacturingProcess44
## + 0.161 ManufacturingProcess32 + 0.189 BiologicalMaterial06
## + 0.28 ManufacturingProcess09 - 0.084 BiologicalMaterial03
## - 0.076 BiologicalMaterial02 - 0.22 ManufacturingProcess17
## + 0.037 BiologicalMaterial11
##
## Rule 61/2: [34 cases, mean 40.012, range 38.2 to 43.12, est err 1.204]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 57.691 + 0.661 ManufacturingProcess01
## + 0.66 ManufacturingProcess10 - 0.265 BiologicalMaterial06
## - 0.83 BiologicalMaterial12 - 0.00016 ManufacturingProcess16
## + 0.006 BiologicalMaterial02 - 0.006 BiologicalMaterial03
##
## Rule 61/3: [47 cases, mean 41.505, range 36.83 to 46.34, est err 1.210]
##
## if
## BiologicalMaterial02 > 52.95
## ManufacturingProcess32 > 159
## then
## outcome = -96.825 - 0.00144 ManufacturingProcess25
## + 3.34 ManufacturingProcess44 + 0.0195 ManufacturingProcess18
## + 0.53 ManufacturingProcess09 + 0.151 BiologicalMaterial02
## + 0.00089 ManufacturingProcess14 - 0.02 ManufacturingProcess33
## + 0.035 ManufacturingProcess32 + 0.041 BiologicalMaterial06
##
## Rule 61/4: [9 cases, mean 41.803, range 38.99 to 43.88, est err 3.809]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 159
## then
## outcome = -437.356 + 6.83 BiologicalMaterial02
## + 8.92 ManufacturingProcess42 + 1.518 ManufacturingProcess01
##
## Model 62:
##
## Rule 62/1: [98 cases, mean 39.589, range 35.25 to 44.16, est err 1.159]
##
## if
## ManufacturingProcess32 <= 161
## then
## outcome = 47.009 - 0.52 ManufacturingProcess17
## - 0.33 ManufacturingProcess13 + 0.0037 ManufacturingProcess15
##
## Rule 62/2: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.304]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = 46.623 - 4.04 ManufacturingProcess13
## + 0.0216 ManufacturingProcess15
##
## Rule 62/3: [34 cases, mean 41.999, range 38.95 to 46.34, est err 1.130]
##
## if
## ManufacturingProcess32 > 161
## then
## outcome = 30.458 - 0.34 ManufacturingProcess13
## + 0.0039 ManufacturingProcess15
##
## Model 63:
##
## Rule 63/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.062]
##
## outcome = -0.92 + 0.0483 ManufacturingProcess35
## - 1173 ManufacturingProcess36 + 0.72 ManufacturingProcess09
## + 0.142 BiologicalMaterial06
##
## Model 64:
##
## Rule 64/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.156]
##
## outcome = 34.651 + 0.0855 ManufacturingProcess04
## - 0.449 ManufacturingProcess07 + 0.233 ManufacturingProcess32
## - 0.9 ManufacturingProcess17
##
## Model 65:
##
## Rule 65/1: [27 cases, mean 38.398, range 36.77 to 40.66, est err 1.907]
##
## if
## BiologicalMaterial06 <= 45.48
## then
## outcome = 116.72 - 8.11 ManufacturingProcess39
## - 0.34 BiologicalMaterial03
##
## Rule 65/2: [47 cases, mean 38.822, range 36.12 to 41.43, est err 2.489]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess32 <= 158
## then
## outcome = 104.67 - 0.515 ManufacturingProcess32
## - 0.224 ManufacturingProcess33 + 0.81 ManufacturingProcess10
## + 1.74 BiologicalMaterial08 - 0.248 BiologicalMaterial06
## + 1.84 ManufacturingProcess45 - 0.03 ManufacturingProcess42
## + 0.02 ManufacturingProcess30 - 4e-05 ManufacturingProcess25
## - 0.006 BiologicalMaterial02
##
## Rule 65/3: [105 cases, mean 40.676, range 35.25 to 46.34, est err 1.537]
##
## if
## BiologicalMaterial06 > 45.48
## then
## outcome = 0.277 + 0.39 BiologicalMaterial06
## - 0.00164 ManufacturingProcess25 + 0.85 ManufacturingProcess09
## - 0.308 BiologicalMaterial02 + 0.00143 ManufacturingProcess27
## + 0.178 ManufacturingProcess32 - 0.188 BiologicalMaterial11
## - 0.048 ManufacturingProcess33 - 0.22 ManufacturingProcess42
## + 0.18 ManufacturingProcess30 + 0.25 BiologicalMaterial08
## + 0.02 BiologicalMaterial05
##
## Rule 65/4: [45 cases, mean 41.193, range 38.13 to 46.34, est err 1.767]
##
## if
## BiologicalMaterial06 > 45.48
## ManufacturingProcess13 <= 34.2
## then
## outcome = -28.374 - 2.64 ManufacturingProcess13
## + 0.00288 ManufacturingProcess27
## + 0.0228 ManufacturingProcess05 - 0.172 ManufacturingProcess33
## + 0.316 BiologicalMaterial11 + 0.82 ManufacturingProcess09
## + 0.0105 ManufacturingProcess20
## - 0.00025 ManufacturingProcess25
## + 0.005 ManufacturingProcess32
##
## Model 66:
##
## Rule 66/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.124]
##
## outcome = 17.714 + 0.0136 ManufacturingProcess26
## - 0.0163 ManufacturingProcess25 - 0.499 ManufacturingProcess07
## + 0.0927 ManufacturingProcess04 + 0.242 ManufacturingProcess32
## - 0.67 ManufacturingProcess17 + 0.09 BiologicalMaterial03
##
## Model 67:
##
## Rule 67/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.052]
##
## outcome = -16.563 + 0.74 ManufacturingProcess09
## + 0.335 BiologicalMaterial06 - 0.237 BiologicalMaterial02
## + 0.127 ManufacturingProcess32
##
## Model 68:
##
## Rule 68/1: [33 cases, mean 38.372, range 36.12 to 40.54, est err 0.940]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess11 <= 9.9
## ManufacturingProcess32 <= 158
## then
## outcome = 85.281 - 6.81 ManufacturingProcess39
## + 0.0175 ManufacturingProcess04 - 0.092 ManufacturingProcess07
## + 0.00063 ManufacturingProcess26 - 0.05 ManufacturingProcess31
## + 0.04 ManufacturingProcess32 - 0.14 ManufacturingProcess13
## - 0.11 ManufacturingProcess21
##
## Rule 68/2: [78 cases, mean 39.704, range 36.77 to 43.88, est err 1.628]
##
## if
## ManufacturingProcess28 <= 10.5
## then
## outcome = 29.674 + 0.0266 ManufacturingProcess04
## - 2.46 ManufacturingProcess21 + 0.287 ManufacturingProcess32
## - 1.38 ManufacturingProcess13 - 0.068 ManufacturingProcess07
## + 0.00047 ManufacturingProcess26
## - 0.037 ManufacturingProcess31 - 0.023 ManufacturingProcess28
## + 0.27 ManufacturingProcess45 - 0.016 ManufacturingProcess24
##
## Rule 68/3: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.495]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 54.617 + 1.48 ManufacturingProcess11
## - 1.32 ManufacturingProcess10 - 0.378 BiologicalMaterial06
## + 0.0148 ManufacturingProcess04 - 0.078 ManufacturingProcess07
## + 0.00054 ManufacturingProcess26
## - 0.042 ManufacturingProcess31 + 0.034 ManufacturingProcess32
## - 0.12 ManufacturingProcess13 - 0.09 ManufacturingProcess21
##
## Rule 68/4: [14 cases, mean 40.424, range 38.63 to 42.31, est err 1.465]
##
## if
## ManufacturingProcess11 > 9.9
## ManufacturingProcess32 <= 158
## then
## outcome = -39.753 + 0.01422 ManufacturingProcess26
## - 2.14 ManufacturingProcess11 + 0.0186 ManufacturingProcess04
## - 0.097 ManufacturingProcess07 + 0.273 ManufacturingProcess22
## + 0.156 ManufacturingProcess32 - 0.053 ManufacturingProcess31
## - 0.15 ManufacturingProcess13 - 0.11 ManufacturingProcess21
##
## Rule 68/5: [54 cases, mean 40.941, range 35.25 to 46.34, est err 1.164]
##
## if
## ManufacturingProcess28 > 10.5
## then
## outcome = -13.674 + 4.557 ManufacturingProcess28
## + 0.188 BiologicalMaterial03 - 0.71 ManufacturingProcess13
## + 0.095 ManufacturingProcess32 + 0.0023 ManufacturingProcess04
## - 0.01 ManufacturingProcess07 - 0.006 ManufacturingProcess31
## + 7e-05 ManufacturingProcess26
##
## Rule 68/6: [27 cases, mean 42.086, range 39.38 to 46.34, est err 1.340]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 62.315 - 3.13 ManufacturingProcess13
## + 0.0292 ManufacturingProcess04 + 0.283 BiologicalMaterial11
## + 0.068 ManufacturingProcess32 + 0.57 ManufacturingProcess45
## + 0.00052 ManufacturingProcess16
## - 0.034 ManufacturingProcess24 - 0.03 ManufacturingProcess28
## - 0.11 ManufacturingProcess21
##
## Model 69:
##
## Rule 69/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 0.977]
##
## outcome = -27.664 + 0.8 ManufacturingProcess09
## + 0.197 ManufacturingProcess32
##
## Model 70:
##
## Rule 70/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.345]
##
## outcome = 75.251 - 1.01 ManufacturingProcess13
##
## Model 71:
##
## Rule 71/1: [35 cases, mean 38.389, range 36.12 to 40.66, est err 2.168]
##
## if
## BiologicalMaterial11 <= 145.37
## ManufacturingProcess32 <= 158
## then
## outcome = 24.873 - 5.65 ManufacturingProcess39
## + 11.79 ManufacturingProcess34 + 0.0203 ManufacturingProcess35
## - 0.00092 ManufacturingProcess25
## + 0.00097 ManufacturingProcess27
## + 0.052 ManufacturingProcess32 + 0.11 ManufacturingProcess09
## + 0.02 ManufacturingProcess30
##
## Rule 71/2: [31 cases, mean 39.804, range 35.25 to 43.44, est err 1.355]
##
## if
## ManufacturingProcess17 > 35
## then
## outcome = 0.741 + 0.369 ManufacturingProcess32
## - 1.2 ManufacturingProcess17 + 0.309 BiologicalMaterial02
## + 0.03 ManufacturingProcess09 - 0.011 BiologicalMaterial06
## + 0.0009 ManufacturingProcess19 - 0.01 ManufacturingProcess23
##
## Rule 71/3: [11 cases, mean 39.847, range 38.2 to 41.45, est err 1.859]
##
## if
## BiologicalMaterial03 <= 67.17
## BiologicalMaterial11 > 145.37
## ManufacturingProcess32 <= 158
## then
## outcome = 126.081 + 0.857 ManufacturingProcess02
## - 1.592 BiologicalMaterial03 + 0.03 ManufacturingProcess30
## + 0.13 ManufacturingProcess34 - 6e-05 ManufacturingProcess27
## - 0.0006 ManufacturingProcess35
##
## Rule 71/4: [101 cases, mean 40.335, range 36.77 to 46.34, est err 1.229]
##
## if
## ManufacturingProcess17 <= 35
## then
## outcome = -35.418 + 0.384 BiologicalMaterial02
## - 0.71 ManufacturingProcess17 - 0.174 BiologicalMaterial06
## + 0.31 ManufacturingProcess09 + 0.086 ManufacturingProcess32
## + 0.0099 ManufacturingProcess19 - 0.07 ManufacturingProcess23
##
## Rule 71/5: [78 cases, mean 40.866, range 38.2 to 46.34, est err 1.440]
##
## if
## BiologicalMaterial11 > 145.37
## then
## outcome = 39.842 + 0.0015 ManufacturingProcess27
## - 0.00135 ManufacturingProcess25 - 0.284 BiologicalMaterial03
## + 0.076 ManufacturingProcess32 + 0.16 ManufacturingProcess09
##
## Rule 71/6: [12 cases, mean 41.410, range 38.81 to 43.88, est err 1.492]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -244.5 + 5.534 BiologicalMaterial02
## - 0.22 ManufacturingProcess17 + 0.076 BiologicalMaterial06
## - 0.12 ManufacturingProcess23
##
## Model 72:
##
## Rule 72/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.064]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = 27.948 - 0.226 ManufacturingProcess08
## + 0.0421 ManufacturingProcess04 + 0.163 ManufacturingProcess32
## - 0.65 ManufacturingProcess13 + 0.22 ManufacturingProcess09
##
## Rule 72/2: [48 cases, mean 41.085, range 38.13 to 46.34, est err 1.093]
##
## if
## ManufacturingProcess13 <= 34.2
## then
## outcome = -12.947 + 0.1336 ManufacturingProcess04
## - 3.22 ManufacturingProcess13 + 0.258 BiologicalMaterial11
##
## Model 73:
##
## Rule 73/1: [42 cases, mean 38.580, range 35.25 to 42.58, est err 1.475]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 33.898 - 0.477 ManufacturingProcess33
## + 0.84 BiologicalMaterial04 + 0.211 ManufacturingProcess32
## - 1.41 BiologicalMaterial01 + 0.00118 ManufacturingProcess27
## - 0.00108 ManufacturingProcess25 - 0.17 ManufacturingProcess17
## + 0.12 ManufacturingProcess09 - 0.015 ManufacturingProcess28
##
## Rule 73/2: [34 cases, mean 40.012, range 38.2 to 43.12, est err 1.012]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 25.82 + 0.0829 ManufacturingProcess04
## - 0.95 ManufacturingProcess17 - 0.141 ManufacturingProcess02
## - 0.29 BiologicalMaterial03 - 1.52 BiologicalMaterial12
## + 1.58 BiologicalMaterial09 + 0.0002 ManufacturingProcess27
## - 0.00018 ManufacturingProcess25
## + 0.008 ManufacturingProcess32 + 0.02 BiologicalMaterial04
## + 0.02 ManufacturingProcess09
##
## Rule 73/3: [89 cases, mean 40.582, range 35.25 to 46.34, est err 0.872]
##
## if
## BiologicalMaterial02 > 52.95
## then
## outcome = -36.698 + 0.00581 ManufacturingProcess27
## - 0.00533 ManufacturingProcess25
## + 0.276 ManufacturingProcess32 + 0.72 ManufacturingProcess09
## - 0.078 ManufacturingProcess28
##
## Rule 73/4: [9 cases, mean 41.803, range 38.99 to 43.88, est err 3.168]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 159
## then
## outcome = -279.894 + 5.366 BiologicalMaterial02
## + 0.735 BiologicalMaterial06 + 0.02 ManufacturingProcess32
## + 0.06 ManufacturingProcess09 - 0.017 ManufacturingProcess28
##
## Model 74:
##
## Rule 74/1: [84 cases, mean 39.710, range 35.25 to 43.88, est err 1.100]
##
## if
## ManufacturingProcess13 > 34.2
## then
## outcome = 9.602 - 0.275 ManufacturingProcess07
## + 0.0512 ManufacturingProcess04 + 0.234 ManufacturingProcess29
## - 0.00095 ManufacturingProcess25
## + 0.131 ManufacturingProcess32 + 0.31 ManufacturingProcess09
## + 0.041 BiologicalMaterial11 - 0.24 BiologicalMaterial12
## - 0.17 ManufacturingProcess13 + 0.036 BiologicalMaterial03
## - 0.07 BiologicalMaterial04
##
## Rule 74/2: [40 cases, mean 40.589, range 38.13 to 43.84, est err 0.973]
##
## if
## BiologicalMaterial11 <= 151.25
## ManufacturingProcess13 <= 34.2
## then
## outcome = 59.257 + 0.95 ManufacturingProcess01
## - 2.06 ManufacturingProcess13 + 0.00176 ManufacturingProcess27
## - 0.218 ManufacturingProcess29 + 0.39 ManufacturingProcess09
## + 0.79 BiologicalMaterial12 - 0.025 ManufacturingProcess07
## + 0.0046 ManufacturingProcess04
## - 0.00027 ManufacturingProcess25 + 0.013 BiologicalMaterial11
## + 0.01 BiologicalMaterial03 - 0.02 BiologicalMaterial04
## + 0.006 ManufacturingProcess32
##
## Rule 74/3: [8 cases, mean 43.569, range 41.49 to 46.34, est err 2.195]
##
## if
## BiologicalMaterial11 > 151.25
## ManufacturingProcess13 <= 34.2
## then
## outcome = 173.589 - 3.89 ManufacturingProcess13
##
## Model 75:
##
## Rule 75/1: [32 cases, mean 38.219, range 35.25 to 39.98, est err 1.398]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess25 > 4832
## ManufacturingProcess32 <= 159
## then
## outcome = 125.063 - 0.02232 ManufacturingProcess25
## - 0.487 ManufacturingProcess33 + 0.0542 ManufacturingProcess06
## + 0.241 ManufacturingProcess32 - 0.0129 ManufacturingProcess05
## + 0.291 BiologicalMaterial06 + 0.101 ManufacturingProcess29
## + 0.026 BiologicalMaterial11 - 0.11 ManufacturingProcess13
## - 0.017 BiologicalMaterial02 - 0.09 BiologicalMaterial12
## + 0.04 ManufacturingProcess09 + 0.014 BiologicalMaterial03
##
## Rule 75/2: [42 cases, mean 38.580, range 35.25 to 42.58, est err 2.906]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = -386.046 - 0.00775 ManufacturingProcess25
## + 0.0756 ManufacturingProcess19 - 0.168 ManufacturingProcess33
## + 0.089 ManufacturingProcess32 + 0.0187 ManufacturingProcess06
## - 0.0044 ManufacturingProcess05 + 0.1 BiologicalMaterial06
## + 0.052 ManufacturingProcess29 - 0.013 BiologicalMaterial02
## + 0.03 ManufacturingProcess09 + 0.011 BiologicalMaterial03
##
## Rule 75/3: [34 cases, mean 40.012, range 38.2 to 43.12, est err 0.911]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 139.709 + 0.681 ManufacturingProcess29
## - 0.185 ManufacturingProcess31 - 1.77 BiologicalMaterial12
## - 0.0132 ManufacturingProcess18 - 6e-05 ManufacturingProcess25
## + 0.009 BiologicalMaterial11 - 0.04 ManufacturingProcess13
##
## Rule 75/4: [89 cases, mean 40.582, range 35.25 to 46.34, est err 0.896]
##
## if
## BiologicalMaterial02 > 52.95
## then
## outcome = -34.146 + 0.349 BiologicalMaterial02
## + 0.243 ManufacturingProcess32 + 0.7 ManufacturingProcess09
## - 0.284 BiologicalMaterial06 - 0.23 BiologicalMaterial04
## - 0.00031 ManufacturingProcess25
## + 0.073 ManufacturingProcess29 + 0.016 BiologicalMaterial03
## - 0.011 ManufacturingProcess28
##
## Rule 75/5: [9 cases, mean 41.803, range 38.99 to 43.88, est err 2.929]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 159
## then
## outcome = -271.989 + 5.213 BiologicalMaterial02
## + 0.739 BiologicalMaterial06 + 0.019 ManufacturingProcess32
## + 0.06 ManufacturingProcess09 - 0.015 ManufacturingProcess28
##
## Model 76:
##
## Rule 76/1: [43 cases, mean 38.870, range 35.25 to 43.88, est err 1.345]
##
## if
## BiologicalMaterial12 <= 19.93
## ManufacturingProcess13 > 34.2
## then
## outcome = 183.261 - 0.656 ManufacturingProcess31
## - 0.0268 ManufacturingProcess20 + 0.31 ManufacturingProcess09
## + 0.069 ManufacturingProcess32
##
## Rule 76/2: [40 cases, mean 40.589, range 38.13 to 43.84, est err 0.909]
##
## if
## BiologicalMaterial11 <= 151.25
## ManufacturingProcess13 <= 34.2
## then
## outcome = 72.981 + 0.0403 ManufacturingProcess04
## - 2.53 ManufacturingProcess13 + 0.503 ManufacturingProcess01
## - 0.044 ManufacturingProcess07 + 0.072 BiologicalMaterial11
## + 0.011 ManufacturingProcess32 + 0.03 ManufacturingProcess09
## - 0.07 BiologicalMaterial08 + 0.0007 ManufacturingProcess15
## - 0.08 ManufacturingProcess37
##
## Rule 76/3: [77 cases, mean 40.966, range 38.2 to 46.34, est err 1.008]
##
## if
## BiologicalMaterial12 > 19.93
## then
## outcome = -6.885 + 0.023 ManufacturingProcess04
## - 0.12 ManufacturingProcess07 + 0.52 ManufacturingProcess09
## - 0.95 ManufacturingProcess37 - 0.54 BiologicalMaterial12
## + 0.045 ManufacturingProcess32 + 0.0044 ManufacturingProcess20
## + 0.048 BiologicalMaterial11 - 0.2 ManufacturingProcess13
## - 0.18 BiologicalMaterial08 + 0.0019 ManufacturingProcess15
##
## Rule 76/4: [8 cases, mean 43.569, range 41.49 to 46.34, est err 2.127]
##
## if
## BiologicalMaterial11 > 151.25
## ManufacturingProcess13 <= 34.2
## then
## outcome = 168.013 - 3.72 ManufacturingProcess13
##
## Model 77:
##
## Rule 77/1: [68 cases, mean 39.124, range 36.12 to 42.31, est err 1.020]
##
## if
## ManufacturingProcess32 <= 158
## then
## outcome = 37.669 - 0.00067 ManufacturingProcess27
## + 0.137 ManufacturingProcess29 - 0.025 BiologicalMaterial02
## + 0.018 ManufacturingProcess32 + 0.04 ManufacturingProcess30
## + 0.016 BiologicalMaterial03 - 0.04 ManufacturingProcess17
##
## Rule 77/2: [17 cases, mean 40.466, range 37.89 to 42.31, est err 1.403]
##
## if
## ManufacturingProcess17 <= 33.5
## ManufacturingProcess32 <= 158
## then
## outcome = 82.325 - 0.8554 ManufacturingProcess06
## + 3.98 ManufacturingProcess17 + 0.00078 ManufacturingProcess27
## + 0.012 ManufacturingProcess29
##
## Rule 77/3: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.159]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = 17.896 + 0.472 ManufacturingProcess29
## - 0.0189 ManufacturingProcess35 + 0.141 ManufacturingProcess32
## - 0.41 ManufacturingProcess17 + 0.099 BiologicalMaterial02
## + 0.19 ManufacturingProcess09
##
## Rule 77/4: [12 cases, mean 41.410, range 38.81 to 43.88, est err 2.119]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -195.722 + 4.486 BiologicalMaterial02
## - 0.00062 ManufacturingProcess27
## + 0.127 ManufacturingProcess29 + 0.017 ManufacturingProcess32
## + 0.04 ManufacturingProcess30 + 0.015 BiologicalMaterial03
## - 0.03 ManufacturingProcess17
##
## Model 78:
##
## Rule 78/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.035]
##
## outcome = -21.18 + 0.86 ManufacturingProcess09
## + 0.138 ManufacturingProcess32
##
## Model 79:
##
## Rule 79/1: [68 cases, mean 39.124, range 36.12 to 42.31, est err 1.123]
##
## if
## ManufacturingProcess32 <= 158
## then
## outcome = 36.39 + 0.3 BiologicalMaterial04 - 0.06 ManufacturingProcess17
## + 0.03 BiologicalMaterial05
##
## Rule 79/2: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.211]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = 46.659 - 0.69 ManufacturingProcess17
## + 0.136 BiologicalMaterial02 + 0.083 BiologicalMaterial03
## - 0.023 ManufacturingProcess02 + 0.033 ManufacturingProcess32
## + 5e-05 ManufacturingProcess27
##
## Rule 79/3: [12 cases, mean 41.410, range 38.81 to 43.88, est err 3.213]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -7.094 + 1.958 BiologicalMaterial02
## - 9.99 BiologicalMaterial08 + 6 BiologicalMaterial12
##
## Rule 79/4: [32 cases, mean 41.477, range 37.89 to 46.34, est err 2.070]
##
## if
## ManufacturingProcess17 <= 33.5
## then
## outcome = 17.586 + 0.455 BiologicalMaterial11
## - 0.277 ManufacturingProcess32
##
## Model 80:
##
## Rule 80/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.074]
##
## outcome = -22.738 + 0.97 ManufacturingProcess09
## + 0.173 ManufacturingProcess32 - 0.49 BiologicalMaterial08
##
## Model 81:
##
## Rule 81/1: [68 cases, mean 39.124, range 36.12 to 42.31, est err 1.210]
##
## if
## ManufacturingProcess32 <= 158
## then
## outcome = 28.502 + 1.05 BiologicalMaterial10
## + 0.078 ManufacturingProcess32 - 0.26 ManufacturingProcess17
## + 0.12 BiologicalMaterial05 + 0.09 ManufacturingProcess42
## + 0.04 BiologicalMaterial08
##
## Rule 81/2: [16 cases, mean 39.669, range 37.86 to 41.5, est err 1.923]
##
## if
## BiologicalMaterial10 <= 2.3
## then
## outcome = 66.35 + 20.32 BiologicalMaterial10
## - 0.466 ManufacturingProcess32 + 0.48 ManufacturingProcess23
## - 0.03 ManufacturingProcess17 + 0.01 BiologicalMaterial05
##
## Rule 81/3: [89 cases, mean 40.582, range 35.25 to 46.34, est err 1.064]
##
## if
## BiologicalMaterial02 > 52.95
## then
## outcome = -34.669 - 0.67 ManufacturingProcess17
## + 0.215 BiologicalMaterial02 + 0.0133 ManufacturingProcess19
## + 0.033 BiologicalMaterial03 + 0.019 ManufacturingProcess32
## + 0.03 BiologicalMaterial05 + 0.02 ManufacturingProcess42
##
## Rule 81/4: [12 cases, mean 41.410, range 38.81 to 43.88, est err 2.471]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -248.607 + 4.5 BiologicalMaterial02
## + 0.816 BiologicalMaterial03
##
## Model 82:
##
## Rule 82/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.333]
##
## outcome = -2.968 + 0.95 ManufacturingProcess09
##
## Model 83:
##
## Rule 83/1: [68 cases, mean 39.124, range 36.12 to 42.31, est err 1.540]
##
## if
## ManufacturingProcess32 <= 158
## then
## outcome = 12.841 + 0.0579 ManufacturingProcess04
## - 0.303 ManufacturingProcess07 + 0.229 ManufacturingProcess32
## - 0.4 ManufacturingProcess17 + 0.2 BiologicalMaterial05
## - 0.64 ManufacturingProcess37
##
## Rule 83/2: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.390]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = -128.103 + 0.149 ManufacturingProcess04
## - 0.167 ManufacturingProcess07 + 0.432 ManufacturingProcess32
## + 1.3 BiologicalMaterial12 - 0.63 ManufacturingProcess17
## - 0.89 BiologicalMaterial08 + 0.11 BiologicalMaterial05
## - 0.35 ManufacturingProcess37
##
## Model 84:
##
## Rule 84/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.359]
##
## outcome = 0.23 + 0.00962 ManufacturingProcess26
## - 0.01179 ManufacturingProcess25 + 0.86 ManufacturingProcess09
##
## Model 85:
##
## Rule 85/1: [42 cases, mean 38.580, range 35.25 to 42.58, est err 1.591]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 49.736 - 6.8 ManufacturingProcess39
## + 0.32 BiologicalMaterial06 + 0.079 ManufacturingProcess32
## - 0.17 ManufacturingProcess13 - 0.024 ManufacturingProcess28
## + 0.0023 ManufacturingProcess15 + 0.03 BiologicalMaterial03
## - 0.27 ManufacturingProcess37 + 0.03 BiologicalMaterial08
##
## Rule 85/2: [76 cases, mean 39.221, range 35.25 to 43.12, est err 2.421]
##
## if
## ManufacturingProcess32 <= 159
## then
## outcome = 44.762 + 0.815 ManufacturingProcess01
## - 1.7 BiologicalMaterial12 + 0.143 ManufacturingProcess32
## - 1.09 ManufacturingProcess37 - 0.02 ManufacturingProcess13
##
## Rule 85/3: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.450]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = -41.128 + 0.295 ManufacturingProcess32
## - 0.59 ManufacturingProcess13 + 0.15 BiologicalMaterial03
## - 0.082 ManufacturingProcess28 + 0.0077 ManufacturingProcess15
## - 0.9 ManufacturingProcess37
##
## Model 86:
##
## Rule 86/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.423]
##
## outcome = -15.186 + 1.17 ManufacturingProcess09
## + 0.00027 ManufacturingProcess26
##
## Model 87:
##
## Rule 87/1: [59 cases, mean 39.270, range 35.25 to 43.88, est err 1.165]
##
## if
## BiologicalMaterial12 <= 19.96
## then
## outcome = 6.791 + 0.02201 ManufacturingProcess26
## - 0.02637 ManufacturingProcess25
## + 0.211 ManufacturingProcess32 - 0.55 ManufacturingProcess30
## - 0.066 ManufacturingProcess28 - 0.71 ManufacturingProcess37
##
## Rule 87/2: [73 cases, mean 40.970, range 38.2 to 46.34, est err 1.913]
##
## if
## BiologicalMaterial12 > 19.96
## then
## outcome = 45.51 + 0.00648 ManufacturingProcess26
## - 0.00788 ManufacturingProcess25
## + 0.733 ManufacturingProcess01 - 1.21 BiologicalMaterial12
## - 1.24 ManufacturingProcess37 + 0.081 ManufacturingProcess32
## - 0.11 ManufacturingProcess30 - 0.02 ManufacturingProcess28
##
## Rule 87/3: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.859]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = -6.967 + 0.02384 ManufacturingProcess26
## - 0.02857 ManufacturingProcess25
## + 0.312 ManufacturingProcess32 - 0.59 ManufacturingProcess30
## - 0.071 ManufacturingProcess28 - 0.77 ManufacturingProcess37
##
## Rule 87/4: [25 cases, mean 42.069, range 39.38 to 46.34, est err 2.266]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 159
## then
## outcome = 102.048 - 3.85 ManufacturingProcess13
## + 0.264 ManufacturingProcess32 - 0.244 ManufacturingProcess24
## + 1.54 BiologicalMaterial08 + 0.00025 ManufacturingProcess26
## - 0.00029 ManufacturingProcess25
##
## Model 88:
##
## Rule 88/1: [35 cases, mean 38.614, range 35.25 to 40.91, est err 1.287]
##
## if
## ManufacturingProcess09 <= 44.86
## then
## outcome = -8.438 + 1.04 ManufacturingProcess09
## + 0.46 ManufacturingProcess42 - 223 ManufacturingProcess36
##
## Rule 88/2: [9 cases, mean 40.449, range 37.86 to 43.88, est err 2.696]
##
## if
## BiologicalMaterial08 <= 16.8
## ManufacturingProcess09 > 44.86
## then
## outcome = -48.003 + 5.6 BiologicalMaterial08
## + 0.00267 ManufacturingProcess14
## - 0.0028 ManufacturingProcess16 - 0.34 BiologicalMaterial04
## + 0.86 BiologicalMaterial10 - 1.04 BiologicalMaterial09
## + 0.092 BiologicalMaterial03 + 0.13 ManufacturingProcess09
## + 0.08 ManufacturingProcess42 - 0.017 ManufacturingProcess28
## - 0.015 ManufacturingProcess24
##
## Rule 88/3: [97 cases, mean 40.786, range 37.51 to 46.34, est err 1.478]
##
## if
## ManufacturingProcess09 > 44.86
## then
## outcome = 2.615 + 0.92 ManufacturingProcess09
## + 0.317 BiologicalMaterial02 + 0.0021 ManufacturingProcess14
## - 0.00221 ManufacturingProcess16 - 0.148 BiologicalMaterial03
## - 0.27 BiologicalMaterial04 - 0.087 ManufacturingProcess28
## - 0.07 ManufacturingProcess24 + 0.68 BiologicalMaterial10
## - 0.171 ManufacturingProcess01 - 0.82 BiologicalMaterial09
## + 0.19 ManufacturingProcess42 + 0.00022 ManufacturingProcess26
## - 0.13 ManufacturingProcess17 + 0.08 ManufacturingProcess30
## + 0.21 BiologicalMaterial08 - 31 ManufacturingProcess36
##
## Rule 88/4: [6 cases, mean 44.105, range 42.61 to 46.34, est err 2.108]
##
## if
## ManufacturingProcess17 <= 32.5
## then
## outcome = 28.843 + 0.41 BiologicalMaterial02 - 0.35 BiologicalMaterial03
## + 0.72 ManufacturingProcess09 - 0.63 ManufacturingProcess30
## + 0.00101 ManufacturingProcess26 - 0.6 ManufacturingProcess17
## + 0.3 ManufacturingProcess42 - 0.072 ManufacturingProcess24
## - 10 ManufacturingProcess36
##
## Model 89:
##
## Rule 89/1: [38 cases, mean 38.537, range 36.12 to 40.66, est err 0.968]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 86.176 - 6.37 ManufacturingProcess39
## + 0.547 BiologicalMaterial02 - 1.11 BiologicalMaterial12
## - 0.0087 ManufacturingProcess05
## + 0.0095 ManufacturingProcess04 - 0.109 BiologicalMaterial03
## - 0.18 ManufacturingProcess17 + 0.018 ManufacturingProcess32
## + 0.02 BiologicalMaterial05 + 0.02 ManufacturingProcess09
## + 0.013 ManufacturingProcess01 - 0.06 ManufacturingProcess37
##
## Rule 89/2: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.246]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 57.577 + 0.858 ManufacturingProcess01
## - 1.76 BiologicalMaterial12 + 0.04 ManufacturingProcess32
## - 0.03 BiologicalMaterial02 + 0.024 BiologicalMaterial03
## + 0.04 BiologicalMaterial05 + 0.04 ManufacturingProcess09
## - 0.13 ManufacturingProcess37
##
## Rule 89/3: [64 cases, mean 41.364, range 35.25 to 46.34, est err 1.668]
##
## if
## ManufacturingProcess32 > 158
## then
## outcome = -20.851 + 0.267 ManufacturingProcess32
## + 0.318 BiologicalMaterial03 - 0.136 BiologicalMaterial02
## - 0.099 BiologicalMaterial06 + 0.19 BiologicalMaterial05
## + 0.133 ManufacturingProcess01 + 0.17 ManufacturingProcess09
## - 0.15 BiologicalMaterial04 - 0.59 ManufacturingProcess37
##
## Model 90:
##
## Rule 90/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.291]
##
## outcome = -8.213 + 0.87 ManufacturingProcess29
## - 0.00341 ManufacturingProcess27 + 1.02 ManufacturingProcess09
##
## Model 91:
##
## Rule 91/1: [59 cases, mean 39.270, range 35.25 to 43.88, est err 1.250]
##
## if
## BiologicalMaterial12 <= 19.96
## then
## outcome = 5.617 - 0.00877 ManufacturingProcess25
## + 0.00915 ManufacturingProcess27 + 0.18 ManufacturingProcess32
## + 0.216 BiologicalMaterial03 - 0.169 BiologicalMaterial02
##
## Rule 91/2: [34 cases, mean 40.012, range 38.2 to 43.12, est err 1.513]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 36.902 + 1.158 ManufacturingProcess01
## - 2.06 BiologicalMaterial12 + 0.192 ManufacturingProcess32
## + 0.0102 ManufacturingProcess35
## - 0.00059 ManufacturingProcess27 + 0.03 ManufacturingProcess30
## - 6e-05 ManufacturingProcess25
##
## Rule 91/3: [20 cases, mean 41.171, range 39.4 to 43.88, est err 3.293]
##
## if
## ManufacturingProcess28 <= 10.5
## ManufacturingProcess32 > 159
## then
## outcome = -0.419 - 0.717 BiologicalMaterial02
## + 0.423 ManufacturingProcess32 + 2.15 BiologicalMaterial01
## - 0.00134 ManufacturingProcess25
## + 0.0014 ManufacturingProcess27 - 0.208 ManufacturingProcess28
## + 0.033 BiologicalMaterial03
##
## Rule 91/4: [36 cases, mean 41.765, range 36.83 to 46.34, est err 1.914]
##
## if
## ManufacturingProcess28 > 10.5
## ManufacturingProcess32 > 159
## then
## outcome = -39.687 + 3.595 ManufacturingProcess28
## + 0.249 ManufacturingProcess32 - 3e-05 ManufacturingProcess25
## + 3e-05 ManufacturingProcess27
##
## Rule 91/5: [25 cases, mean 42.069, range 39.38 to 46.34, est err 1.985]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 159
## then
## outcome = 89.293 - 3.52 ManufacturingProcess13
## - 0.229 ManufacturingProcess24 + 0.228 ManufacturingProcess32
## + 0.23 BiologicalMaterial11 - 0.00017 ManufacturingProcess25
## + 0.00017 ManufacturingProcess27
##
## Model 92:
##
## Rule 92/1: [37 cases, mean 38.699, range 35.25 to 41.42, est err 1.145]
##
## if
## ManufacturingProcess09 <= 44.92
## then
## outcome = -7.057 + 0.75 ManufacturingProcess09
## + 0.71 BiologicalMaterial08
##
## Rule 92/2: [95 cases, mean 40.799, range 37.51 to 46.34, est err 1.401]
##
## if
## ManufacturingProcess09 > 44.92
## then
## outcome = 17.661 + 0.82 ManufacturingProcess09
## - 1.92 BiologicalMaterial09 - 0.076 ManufacturingProcess24
## + 0.00048 ManufacturingProcess25 + 0.085 BiologicalMaterial03
## - 0.24 ManufacturingProcess13 + 0.025 ManufacturingProcess32
## + 0.008 ManufacturingProcess31 + 0.05 ManufacturingProcess42
## + 0.001 ManufacturingProcess15
##
## Rule 92/3: [6 cases, mean 44.105, range 42.61 to 46.34, est err 4.325]
##
## if
## ManufacturingProcess17 <= 32.5
## then
## outcome = 19.77 + 2.38 ManufacturingProcess42
##
## Model 93:
##
## Rule 93/1: [42 cases, mean 38.580, range 35.25 to 42.58, est err 1.170]
##
## if
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = 15.842 - 0.00197 ManufacturingProcess27
## + 0.419 ManufacturingProcess29 + 0.127 ManufacturingProcess32
## + 0.049 BiologicalMaterial03
##
## Rule 93/2: [34 cases, mean 40.012, range 38.2 to 43.12, est err 1.280]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 159
## then
## outcome = -10.912 + 0.0968 ManufacturingProcess04
## - 2.16 BiologicalMaterial12 - 0.00069 ManufacturingProcess27
## + 0.146 ManufacturingProcess29 + 0.28 BiologicalMaterial05
##
## Rule 93/3: [56 cases, mean 41.553, range 36.83 to 46.34, est err 1.460]
##
## if
## ManufacturingProcess32 > 159
## then
## outcome = -5.517 + 0.202 ManufacturingProcess32
## - 0.194 ManufacturingProcess28 + 0.233 BiologicalMaterial03
##
## Model 94:
##
## Rule 94/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.480]
##
## outcome = 16.713 - 0.03652 ManufacturingProcess25
## + 0.02653 ManufacturingProcess27
## + 0.00958 ManufacturingProcess26
## + 0.01639 ManufacturingProcess14
## - 0.01706 ManufacturingProcess16 + 0.66 ManufacturingProcess09
## - 0.71 ManufacturingProcess13 + 0.091 ManufacturingProcess32
##
## Model 95:
##
## Rule 95/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.315]
##
## outcome = -7.748 + 0.245 ManufacturingProcess32
## + 0.162 BiologicalMaterial03 - 0.03 ManufacturingProcess33
##
## Model 96:
##
## Rule 96/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.422]
##
## outcome = 42.06 + 1.386 ManufacturingProcess29
## - 0.0052 ManufacturingProcess25 - 0.9 ManufacturingProcess17
## + 0.58 ManufacturingProcess09
##
## Model 97:
##
## Rule 97/1: [19 cases, mean 38.526, range 36.12 to 40.64, est err 2.181]
##
## if
## BiologicalMaterial06 > 45.06
## BiologicalMaterial12 <= 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = -52.724 + 1.844 BiologicalMaterial06
## - 0.442 BiologicalMaterial03 + 0.71 ManufacturingProcess09
## + 0.008 ManufacturingProcess32
##
## Rule 97/2: [4 cases, mean 38.998, range 38.2 to 40.15, est err 3.244]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess01 <= 9.7
## ManufacturingProcess32 <= 158
## then
## outcome = 19.608 + 0.299 ManufacturingProcess32
## + 0.562 ManufacturingProcess01 - 1.17 BiologicalMaterial12
## - 0.133 BiologicalMaterial03 - 0.17 BiologicalMaterial04
## + 76 ManufacturingProcess36
##
## Rule 97/3: [59 cases, mean 39.270, range 35.25 to 43.88, est err 1.223]
##
## if
## BiologicalMaterial12 <= 19.96
## then
## outcome = -13.681 + 0.29 ManufacturingProcess32
## - 0.098 ManufacturingProcess28 + 0.127 BiologicalMaterial03
##
## Rule 97/4: [30 cases, mean 39.867, range 38.2 to 42.31, est err 0.907]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 41.845 + 0.21 ManufacturingProcess32
## - 1.35 BiologicalMaterial12 + 0.361 ManufacturingProcess01
## - 0.151 BiologicalMaterial03 - 0.11 BiologicalMaterial04
## + 49 ManufacturingProcess36 - 0.007 ManufacturingProcess28
##
## Rule 97/5: [6 cases, mean 40.422, range 39.38 to 43.12, est err 2.814]
##
## if
## BiologicalMaterial04 > 12.12
## BiologicalMaterial05 <= 18.44
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 45.325 + 1.955 ManufacturingProcess28
## - 2.77 ManufacturingProcess13 + 0.326 ManufacturingProcess32
## - 0.34 ManufacturingProcess43 + 0.18 BiologicalMaterial05
## + 0.41 BiologicalMaterial08 + 0.046 BiologicalMaterial03
## - 0.027 BiologicalMaterial02 - 0.007 ManufacturingProcess02
##
## Rule 97/6: [16 cases, mean 40.426, range 35.25 to 43.44, est err 2.659]
##
## if
## ManufacturingProcess13 > 34.2
## ManufacturingProcess28 > 10.5
## ManufacturingProcess32 > 158
## then
## outcome = -22.614 + 2.575 ManufacturingProcess28
## + 0.411 BiologicalMaterial03 + 0.214 ManufacturingProcess32
## + 0.69 ManufacturingProcess13 - 0.0085 ManufacturingProcess15
## - 0.02 BiologicalMaterial04
##
## Rule 97/7: [23 cases, mean 41.123, range 39.4 to 43.88, est err 2.518]
##
## if
## ManufacturingProcess28 <= 10.5
## ManufacturingProcess32 > 158
## then
## outcome = 26.218 + 0.244 ManufacturingProcess32
## - 1.38 BiologicalMaterial12 + 0.205 BiologicalMaterial03
## - 0.14 BiologicalMaterial02 - 0.097 ManufacturingProcess28
## - 0.036 ManufacturingProcess02
##
## Rule 97/8: [31 cases, mean 41.751, range 38.2 to 46.34, est err 1.293]
##
## if
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess28 > 10.5
## then
## outcome = -23.388 + 3.315 ManufacturingProcess28
## + 0.23 ManufacturingProcess32 + 0.292 BiologicalMaterial03
## - 0.69 ManufacturingProcess13 - 0.37 BiologicalMaterial04
##
## Rule 97/9: [20 cases, mean 41.929, range 39.38 to 46.34, est err 1.142]
##
## if
## BiologicalMaterial04 > 12.12
## ManufacturingProcess13 <= 34.2
## ManufacturingProcess32 > 158
## then
## outcome = 32.699 - 2.36 ManufacturingProcess13
## + 0.355 ManufacturingProcess28 + 0.326 ManufacturingProcess32
## - 0.9 ManufacturingProcess43 + 0.47 BiologicalMaterial05
## + 1.1 BiologicalMaterial08 + 0.057 BiologicalMaterial03
## - 0.02 BiologicalMaterial02 - 0.03 BiologicalMaterial04
## - 0.005 ManufacturingProcess02
##
## Model 98:
##
## Rule 98/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.353]
##
## outcome = -32.932 + 0.02066 ManufacturingProcess27
## - 0.01924 ManufacturingProcess25 + 1.05 ManufacturingProcess09
## - 0.207 BiologicalMaterial02 + 0.207 BiologicalMaterial03
## + 0.133 ManufacturingProcess32
##
## Model 99:
##
## Rule 99/1: [59 cases, mean 39.270, range 35.25 to 43.88, est err 1.803]
##
## if
## BiologicalMaterial12 <= 19.96
## then
## outcome = 6.873 - 0.778 ManufacturingProcess33
## + 0.496 ManufacturingProcess32 + 0.69 BiologicalMaterial06
## - 0.437 BiologicalMaterial03
##
## Rule 99/2: [30 cases, mean 39.867, range 38.2 to 42.31, est err 1.692]
##
## if
## BiologicalMaterial12 > 19.96
## ManufacturingProcess32 <= 158
## then
## outcome = 39.518 + 0.0475 ManufacturingProcess04
## - 2.63 BiologicalMaterial12 + 0.776 ManufacturingProcess01
## + 0.165 ManufacturingProcess33 - 3.59 ManufacturingProcess44
## - 0.00029 ManufacturingProcess26
##
## Rule 99/3: [89 cases, mean 40.582, range 35.25 to 46.34, est err 1.368]
##
## if
## BiologicalMaterial02 > 52.95
## then
## outcome = -116.066 + 0.1095 ManufacturingProcess04
## + 0.263 ManufacturingProcess32 + 0.135 BiologicalMaterial02
## + 0.104 BiologicalMaterial03 - 0.13 BiologicalMaterial04
##
## Rule 99/4: [12 cases, mean 41.410, range 38.81 to 43.88, est err 2.932]
##
## if
## BiologicalMaterial02 <= 52.95
## ManufacturingProcess32 > 158
## then
## outcome = -312.797 + 5.651 BiologicalMaterial02
## + 0.808 BiologicalMaterial03 + 0.0032 ManufacturingProcess04
## + 0.01 ManufacturingProcess32 - 0.01 BiologicalMaterial04
##
## Rule 99/5: [6 cases, mean 44.105, range 42.61 to 46.34, est err 2.700]
##
## if
## ManufacturingProcess17 <= 32.5
## then
## outcome = 45.88
##
## Model 100:
##
## Rule 100/1: [132 cases, mean 40.210, range 35.25 to 46.34, est err 1.240]
##
## outcome = 0.092 + 0.81 ManufacturingProcess09
## - 0.55 ManufacturingProcess17 + 0.48 ManufacturingProcess39
## + 0.091 ManufacturingProcess32 + 0.22 BiologicalMaterial05
##
##
## Evaluation on training data (132 cases):
##
## Average |error| 0.438
## Relative |error| 0.28
## Correlation coefficient 0.94
##
##
## Attribute usage:
## Conds Model
##
## 26% 74% ManufacturingProcess32
## 18% 30% ManufacturingProcess13
## 12% 13% BiologicalMaterial12
## 10% 23% BiologicalMaterial06
## 9% 31% ManufacturingProcess17
## 7% 26% BiologicalMaterial02
## 5% 53% ManufacturingProcess09
## 3% 31% BiologicalMaterial03
## 2% 11% ManufacturingProcess28
## 2% 9% BiologicalMaterial11
## 2% 8% BiologicalMaterial04
## 2% 6% ManufacturingProcess39
## 3% BiologicalMaterial10
## 1% ManufacturingProcess11
## 19% ManufacturingProcess25
## 9% ManufacturingProcess02
## 9% BiologicalMaterial08
## 2% BiologicalMaterial01
## 12% BiologicalMaterial05
## 6% ManufacturingProcess01
## 18% ManufacturingProcess04
## 16% ManufacturingProcess26
## 12% ManufacturingProcess27
## 11% ManufacturingProcess33
## 10% ManufacturingProcess07
## 10% ManufacturingProcess15
## 9% ManufacturingProcess16
## 8% ManufacturingProcess29
## 8% ManufacturingProcess42
## 7% ManufacturingProcess20
## 6% ManufacturingProcess31
## 6% ManufacturingProcess30
## 6% BiologicalMaterial09
## 6% ManufacturingProcess21
## 6% ManufacturingProcess37
## 5% ManufacturingProcess19
## 5% ManufacturingProcess35
## 5% ManufacturingProcess14
## 5% ManufacturingProcess24
## 4% ManufacturingProcess34
## 4% ManufacturingProcess18
## 3% ManufacturingProcess36
## 3% ManufacturingProcess10
## 2% ManufacturingProcess22
## 2% ManufacturingProcess23
## 2% ManufacturingProcess05
## 2% ManufacturingProcess08
## 1% ManufacturingProcess43
## 1% ManufacturingProcess06
##
##
## Time: 0.4 secsplot(varImp(tree2), top=10)
varimpCube <- varImp(tree2)$importance
varimpCube## Overall
## ManufacturingProcess32 100
## ManufacturingProcess13 48
## BiologicalMaterial12 25
## BiologicalMaterial06 33
## ManufacturingProcess17 40
## BiologicalMaterial02 33
## ManufacturingProcess09 58
## BiologicalMaterial03 34
## ManufacturingProcess28 13
## BiologicalMaterial11 11
## BiologicalMaterial04 10
## ManufacturingProcess39 8
## BiologicalMaterial10 3
## ManufacturingProcess11 1
## ManufacturingProcess25 19
## ManufacturingProcess02 9
## BiologicalMaterial08 9
## BiologicalMaterial01 2
## BiologicalMaterial05 12
## ManufacturingProcess01 6
## ManufacturingProcess04 18
## ManufacturingProcess26 16
## ManufacturingProcess27 12
## ManufacturingProcess33 11
## ManufacturingProcess07 10
## ManufacturingProcess15 10
## ManufacturingProcess16 9
## ManufacturingProcess29 8
## ManufacturingProcess42 8
## ManufacturingProcess20 7
## ManufacturingProcess31 6
## ManufacturingProcess30 6
## BiologicalMaterial09 6
## ManufacturingProcess21 6
## ManufacturingProcess37 6
## ManufacturingProcess19 5
## ManufacturingProcess35 5
## ManufacturingProcess14 5
## ManufacturingProcess24 5
## ManufacturingProcess34 4
## ManufacturingProcess18 4
## ManufacturingProcess36 3
## ManufacturingProcess10 3
## ManufacturingProcess22 2
## ManufacturingProcess23 2
## ManufacturingProcess05 2
## ManufacturingProcess08 2
## ManufacturingProcess43 1
## ManufacturingProcess06 1
## BiologicalMaterial07 0
## ManufacturingProcess03 0
## ManufacturingProcess12 0
## ManufacturingProcess38 0
## ManufacturingProcess40 0
## ManufacturingProcess41 0
## ManufacturingProcess44 0
## ManufacturingProcess45 0