Topic Index
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A B C D E F G H I J K L M N O P
Q R S T U V W X Y Z
a
addIntercept [in 5.2.2 Intercept Option]
analysis of variance for a linear regression [in 5.4.1 Class RWLinearRegressionANOVA]
assumptions [in 1.4.2 Assumptions]
b
backward selection [in 4.2.3 Backward Selection ]
base calculation [in 2.3.1 The Base Calculation]
base class RWRegression [in 5.1 Overview]
bibliography [in Chapter 6 - References]
binary outcome data [in 3.3 Logistic Regression]
body class
c
calculation methods for logistic regression [in 5.5.2 Calculation Methods for Logistic Regression]
calculation methods
chi-squared distribution
class herarchy
class hierarchy notation [in 1.4.5 Class Relationship Notation]
class hierarchy
class relationship notations [in 1.4.5 Class Relationship Notation]
components of Analytics.h++ [in 2.1 Components]
confidence interval [in 3.2.6 Prediction Intervals]
constructing a regression object [in 5.2.2 Intercept Option]
contacting [in 1.5.2 How to Contact Technical Support]
conventions
critical value
d
data change objects [in 2.3.1 The Base Calculation]
default intercept option [in 5.2.2 Intercept Option]
deviance
dispersion matrix [in 3.2.3 Parameter Dispersion (Variance-Covariance) Matrix]
documentation [in 1.4 Documentation]
e
ellipses [in 1.4.4 Class and Function Naming]
equal mass binning [in 3.3.3.3 Hosmer-Lemeshow Statistic]
European phone numbers [in 1.5.2 How to Contact Technical Support]
example
exhaustive search [in 4.2.1 Exhaustive Search]
f
F statistic [in 5.6.1 Selection Evaluation Criteria - Function Objects]
F statistic
features [in 1.2 Product Features]
fitted regression [in 3.2.1 Parameter Calculation by Least Squares Minimization]
forward selection [in 4.2.2 Forward Selection]
function objects
g
G statistic [in 3.3.3 Significance of the Model]
G statistic [in 3.3.3.1 G Statistic]
Galton, Francis [in 3.2 Multiple Linear Regression]
goodness of fit
groups for predictor variable
h
handle class
handle functions [in 5.2.1 Updating Parameter Estimates]
Hosmer-Lemeshow statistic [in 3.3.3 Significance of the Model]
Hosmer-Lemeshow statistic [in 3.3.3.3 Hosmer-Lemeshow Statistic]
i
intercept option [in 5.2.2 Intercept Option]
intercept option
intercept parameter [in 3.2 Multiple Linear Regression]
intercept [in 5.2.2 Intercept Option]
iterative least squares [in 5.5.2.1 RWLogisticIterLSQ]
l
least squares minimization [in 3.2.1 Parameter Calculation by Least Squares Minimization]
Levenberg-Marquardt method [in 5.5 Parameter Calculation Classes]
Levenberg-Marquardt method [in 5.5.2.2 RWLogisticLevenbergMarquardt]
likelyhood ratio test [in 3.3.3.1 G Statistic]
log likelihood [in 3.3.1 Parameter Calculation]
logistic regression [in 3.3 Logistic Regression]
logistic regression
m
Mallow's statistic [in 5.6.3 Writing Your Own Function Objects]
manual organization [in 1.4 Documentation]
method of least squares [in 3.2.1 Parameter Calculation by Least Squares Minimization]
method of maximum likelihood [in 3.3.1 Parameter Calculation]
model selection classes [in 2.4 Model Selection Classes]
model selection classes
model selection tools [in 4.1 Definition]
model selection viewed as search [in 4.2 Model Selection Viewed As Search]
model selection [in 4.1 Definition]
model selection
model variance [in 3.2.2 Model Variance]
multiple linear regression parameter calculation [in 5.5.1 Calculation Methods for Linear Regression]
multiple linear regression problem [in 3.2 Multiple Linear Regression]
n
Newton-Raphson method [in 5.5.2.1 RWLogisticIterLSQ]
noIntercept [in 5.2.2 Intercept Option]
o
object-oriented technology [in 1.4.2 Assumptions]
online documentation [in 1.4.1 Online Documentation]
online documentation
optimizing computational speed [in 2.3.1 The Base Calculation]
overall F statistic [in 3.2.4 Significance of the Model (Overall F Statistic)]
p
P-value [in 5.6.1 Selection Evaluation Criteria - Function Objects]
P-value
parameter calculation classes [in 2.3 Parameter Calculation Classes]
parameter calculation classes
parameter calculation for logistic regression [in 5.5.2 Calculation Methods for Logistic Regression]
parameter calculation [in 5.5 Parameter Calculation Classes]
parameter calculation
parameter calculations
parameter dispersion matrix [in 3.2.3 Parameter Dispersion (Variance-Covariance) Matrix]
parameter estimate classes [in 5.3 Parameter Estimate Classes]
parameter estimate
parameter variances and covariances [in 3.3.2 Parameter Variances and Covariances]
Pearson statistic [in 3.3.3 Significance of the Model]
Pearson statistic [in 3.3.3.2 Pearson Statistic]
phone numbers [in 1.5.2 How to Contact Technical Support]
prediction intervals [in 3.2.6 Prediction Intervals]
predictions of a logistic regression model [in 3.3.3.3 Hosmer-Lemeshow Statistic]
predictor data matrix [in 3.2.1 Parameter Calculation by Least Squares Minimization]
predictor variable [in 3.2 Multiple Linear Regression]
predictor variable [in 3.2 Multiple Linear Regression]
predictor variable
q
QR method with pivoting [in 5.5.1.2 RWLeastSqQRPvtCalc]
QR method [in 5.5.1.1 RWLeastSqQRCalc]
r
reCalculateParameters() [in 5.2.1 Updating Parameter Estimates]
regression analysis classes [in 5.4 Regression Analysis Classes]
regression classes [in 2.2 Regression Classes]
regression classes
regression matrix [in 3.2 Multiple Linear Regression]
residual sum of squares [in 3.2.1 Parameter Calculation by Least Squares Minimization]
residuals [in 3.2.1 Parameter Calculation by Least Squares Minimization]
response variable [in 3.2 Multiple Linear Regression]
response vector [in 3.2 Multiple Linear Regression]
RWLeastSqQRCalc [in 5.5.1.1 RWLeastSqQRCalc]
RWLeastSqQRPvtCalc [in 5.5.1.2 RWLeastSqQRPvtCalc]
RWLeastSqSVDCalc [in 5.5.1.3 RWLeastSqSVDCalc]
RWLinearRegression [in 5.1 Overview]
RWLinearRegressionANOVA [in 5.1 Overview]
RWLinearRegressionANOVA [in 5.4.1 Class RWLinearRegressionANOVA]
RWLinearRegressionFTest [in 5.1 Overview]
RWLinearRegressionFTest [in 5.4.3 Class RWLinearRegressionFTest]
RWLinearRegressionParam [in 5.3 Parameter Estimate Classes]
RWLinRegModelSelector<F> [in 5.6 Using the Model Selection Classes]
RWLinRegModelSelector<F> [in 5.6.3 Writing Your Own Function Objects]
RWLogisticFitAnalysis [in 5.1 Overview]
RWLogisticFitAnalysis [in 5.4.2 Class RWLogisticFitAnalysis]
RWLogisticIterLSQ [in 5.5.2.1 RWLogisticIterLSQ]
RWLogisticLevenbergMarquardt [in 5.5.2.2 RWLogisticLevenbergMarquardt]
RWLogisticRegression [in 5.1 Overview]
RWLogisticRegressionParam [in 5.3 Parameter Estimate Classes]
RWLogRegModelSelector<F> [in 5.6 Using the Model Selection Classes]
RWRegression [in 5.1 Overview]
s
search algorithms
setCalcMethod() [in 5.5 Parameter Calculation Classes]
significance
simple linear regression [in 3.2 Multiple Linear Regression]
singular value decomposition [in 5.5.1.3 RWLeastSqSVDCalc]
software requirements [in 1.3 Software Requirements]
stepwise selection [in 4.2.4 Stepwise Selection]
t
T statistic [in 3.2.5 Significance of Predictor Variables]
T statistic [in 3.2.5.2 Critical Values]
technical support [in 1.5 Technical Support]
technical support [in 1.5.2 How to Contact Technical Support]
technical support [in 1.5.2 How to Contact Technical Support]
template parameter F [in 5.6.1 Selection Evaluation Criteria - Function Objects]
testing the null hypothesis [in 3.2.4 Significance of the Model (Overall F Statistic)]
transpose of the regression matrix [in 3.2.3 Parameter Dispersion (Variance-Covariance) Matrix]
u
unbiased estimator of variance [in 3.2.2 Model Variance]
v
variance [in 3.2.2 Model Variance]
variance-covariance [in 3.2.3 Parameter Dispersion (Variance-Covariance) Matrix]
variances and covariances [in 3.3.2 Parameter Variances and Covariances]
w
Wald chi-square statistic [in 3.3.4 Parameter Significance (Wald Test)]
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