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SAS Institute - JMP 14 Predictive and Specialized Modeling

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    JMP 14 Predictive and Specialized Modeling
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JMP 14 Predictive and Specialized Modeling: summary, description and annotation

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JMP 14 Predictive and Specialized Modeling provides details about modeling techniques such as partitioning, neural networks, nonlinear regression, and time series analysis. Topics include the Gaussian platform, which is useful in analyzing computer simulation experiments. The book also covers the Response Screening platform, which is useful in testing the effect of a predictor when you have many responses.

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Index
Specialized and Predictive Modeling
Symbols
^, redundant leaf labels
A
AAE
AAE, model comparison
Add Highest Nines to Missing Value Codes option
ADF tests
antecedent, Association Analysis
ApproxStdErr
ARIMA
ARIMA lag
Association Analysis
antecedent
association rules
condition
Frequent Item Sets report
lift ratio
Maximum Antecedents
Maximum Rule Size
Minimum Confidence
Minimum Lift
Minimum Support
Rules report
SVD
Topic Words Matrix
association rules
Association Analysis
AUC Comparison
Augmented Dickey-Fuller test
Autocorrelation
B
Bartletts Kolmogorov-Smirnov
Boston Housing.jmp
Brown smoothing
By variable
C
Cauchy option
Change Highest Nines to Missing option
Close All Below (Partition Platform)
Color Points
Column Contributions
Compare Parameter Estimates
comparing models
condition, Association Analysis
Confidence Limits
consequent, Association Analysis
Association Analysis
consequent
Contour Profiler
Corr option
Correlation Type
Cross Correlation
crossvalidation
Cubic
custom loss function
D
damped-trend linear exponential smoothing
decision trees
derivative
double exponential smoothing
Downshift Position
E
Entropy RSquare
Equivalence Test
equivalence tests
Estimate
Estimate Nugget Parameter
Expand Intermediate Formulas
Expand Into Categories, selecting column
Explore Missing Values utility
Missing Value Clustering
Missing Value Snapshot
Multivariate Normal Imputation utility
Explore Outliers utility
examples
Multivariate k-Nearest Neighbor Outliers utility
Multivariate Robust Outliers utility
Quantile Range Outliers utility
Robust Fit Outliers utility
F
False Discovery Rate
FDR
First
Fishers Kappa
Fit Curve
Force X Categorical option
Force X Continuous option
Force Y Categorical option
Force Y Continuous option
forecast
Forecast plot
Frequent Item Sets report
Association Analysis
G
Gaussian
Gaussian Process
Gaussian Process
Gauss-Newton method
Generalized RSquare
goal SSE
Grouping
H
Hessian
Holt smoothing
I
Informative Missing
Ingots2.jmp
K
K Fold Crossvalidation
Kappa option
L
lag,ARIMA
Largest Downshift
Largest Upshift
Leaf Report
Lift Curve
lift ratio, Association Analysis
likelihood confidence intervals
linear exponential smoothing
Lock
Lock Columns
Partition platform
Logistic platform
example
Logistic w Loss.jmp
Loss
loss function
custom
Lower CL and Upper CL
M
Mahalanobis Distance
Make SAS DATA Step
Make Validation Column utility
market basket analysis
Matched Pairs platform
examples
launching
multiple Y columns
options
report window
statistical details
Tukey mean-difference plot
Maximum Antecedents
Association Analysis
Maximum Rule Size
Association Analysis
MaxLogWorth setting
Mean Abs Dev
Mean -Log p
Measures of Fit report
Minimum Confidence
Association Analysis
Minimum Lift
Association Analysis
Minimum Size Split
Minimum Support
Association Analysis
Minimum Theta Value
Misclassification Rate
Missing is category option
Missing Value Clustering
Missing Value Snapshot
missing values
codes in Explore Outliers utility
codes in Quantile Range Outliers utility
Explore Missing Values utility
Model Averaging
Model Comparison platform
example
launch
options
report
Model Library
Multivariate k-Nearest Neighbor Outliers utility
Multivariate Normal Imputation utility
Multivariate Robust Outliers utility
Multivariate SVD Imputation utility
N
Negative Exponential.jmp
Neural
Boosting options
Confusion Matrix, Confusion Rates report
example
Fitting options
Hidden Layer Structure, Hidden Nodes options
Informative Missing option
launch window
Model Launch control panel
overview of networks
Random Seed option
red triangle options
Training report
Validation
Excluded Rows Holdback
Holdback
KFold
launch option
method
report
Newton-Raphson method
nonlinear fit options
nonlinear fit, setting parameter limits
Nonlinear Model Library
Customizing
Nonlinear platform
derivatives
Nonlinear platform, builtin models
Nonlinear platform, custom models
nonstationary time series
Nugget parameters
Number of Forecast Periods
Number of Points
NumDeriv
Numeric Derivatives Only
O
Optimal Value
Outlier Threshold
Output Split Table
P
p, d, q parameters
paired t-test
Paired X and Y option
Parameter
Parameter Contour Profiler
parameter limits, nonlinear fit
Parameter Profiler
Parameter Surface Profiler
Partial Autocorrelation
Partition
Informative Missing
Partition platform
periodicity
periodogram
Plot Actual by Predicted
Plot Actual by Predicted (Partition)
Plot Dif by Mean option
Plot Dif by Row option
Plot Residual by Row
Poisson loss function
Poisson Y option
Practical Difference Portion setting
Practical Significance
Predictor role
Predictor Screening
options
report
Predictors report
probit example
Process Performance Graph
Process Screening
By variable
Grouping variable
profile confidence limits
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