Topic Index

Business Analysis Module User's Guide
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Topic Index

Click on one of the letters below to jump immediately to that section of the index.

A | B | C | D | E | F | G | H | I | L | M | N | O | P | Q | R | S | T | U | V | W

A

addIntercept [in 5.2.2 Intercept Option]
analysis of variance for a linear regression [in 5.4.1 Class RWLinearRegressionANOVA]

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]
binary outcome data [in 3.3 Logistic Regression]
body class
RWNeighborIterator [in 2.4 Model Selection Classes]

C

calculation methods for logistic regression [in 5.5.2 Calculation Methods for Logistic Regression]
calculation methods
   switching at runtime [in 5.5 Parameter Calculation Classes]
chi-squared distribution
   default value for G (Lemeshow) [in 3.3.3.3 Hosmer-Lemeshow Statistic]
   G statistic [in 3.3.3.1 G Statistic]
   Lemeshow statistics [in 3.3.3.3 Hosmer-Lemeshow Statistic]
class hierarchy
   model selection classes [in 2.4 Model Selection Classes]
   parameter calculation classes [in 2.3 Parameter Calculation Classes]
   regression classes [in 2.2 Regression Classes]
components of the Business Analysis Module [in 2.1 Components]
confidence interval [in 3.2.6 Prediction Intervals]
constructing a regression object [in 5.2.2 Intercept Option]
critical value
   for parameter estimates [in 3.3.4.2 Critical Values]
   of the F statistic [in 3.2.4.2 Critical Value]
   of the parameter T statistic [in 3.2.5.2 Critical Values]

D

data change objects [in 2.3.1 The Base Calculation]
default intercept option [in 5.2.2 Intercept Option]
deviance
logistical regression [in 3.3.3.1 G Statistic]
dispersion matrix [in 3.2.3 Parameter Dispersion (Variance-Covariance) Matrix]

E

equal mass binning [in 3.3.3.3 Hosmer-Lemeshow Statistic]
example
   analysis of variance [in 5.4.1 Class RWLinearRegressionANOVA]
   constructing parameter estimate classes [in 5.3 Parameter Estimate Classes]
   custom linear regression calculation class [in 5.5.3 Writing Your Own Parameter Calculation Class]
   forward selection [in 5.6.2 A Detailed Example]
   implementing a function object to produce Mallow&rsquo [in 5.6.3 Writing Your Own Function Objects]
   intercept options [in 5.2.2 Intercept Option]
   Levenberg-Marquardt calculation method [in 5.5 Parameter Calculation Classes]
   logistic regression with RWLogisticRegression [in 5.2 Regression Classes]
   model selection [in 5.6.2 A Detailed Example]
   model selection technique [in 4.2 Model Selection Viewed As Search]
   multiple linear regression with RWLinearRegression [in 5.2 Regression Classes]
   parameter calculation [in 5.2.1 Updating Parameter Estimates]
   stepwise selection [in 5.6.2 A Detailed Example]
   switching calculation methods [in 5.5 Parameter Calculation Classes]
   testing regression hypotheses [in 5.4.3 Class RWLinearRegressionFTest]
   using class RWLogisticFitAnalysis [in 5.4.2 Class RWLogisticFitAnalysis]
exhaustive search [in 4.2.1 Exhaustive Search]

F

F statistic [in 5.6.1 Selection Evaluation Criteria: Function Objects]
critical value [in 3.2.4.2 Critical Value]
fitted regression [in 3.2.1 Parameter Calculation by Least Squares Minimization]
forward selection [in 4.2.2 Forward Selection]
function objects
writing your own [in 5.6.3 Writing Your Own Function Objects]

G

G statistic [in 3.3.3.1 G Statistic]
   [in 3.3.3 Significance of the Model]
Galton, Francis [in 3.2 Multiple Linear Regression]
goodness of fit
   of a logistic regression model [in 3.3.3 Significance of the Model]
groups for predictor variable
   default number [in 3.3.3.2 Pearson Statistic]

M

Mallow&rsquo [in 5.6.3 Writing Your Own Function Objects]
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 [in 4.1 Definition]
model selection classes [in 2.4 Model Selection Classes]
   class hierarchy [in 2.4 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
   backward selection [in 4.2.3 Backward Selection]
   choosing a technique [in 4.2.3 Backward Selection]
   exhaustive search [in 4.2.1 Exhaustive Search]
   forward selection [in 4.2.2 Forward Selection]
   preferences [in 4.2.3 Backward Selection]
   stepwise selection [in 4.2.4 Stepwise 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

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]
   for parameter estimate [in 3.3.4.1 p-Values]
   for parameter estimates [in 3.2.5.1 p-Values]
   of the F statistic [in 3.2.4.1 p-Value]
parameter calculation [in 5.5 Parameter Calculation Classes]
parameter calculation classes [in 2.3 Parameter Calculation Classes]
   class hierarchy [in 2.3 Parameter Calculation Classes]
parameter calculation for logistic regression [in 5.5.2 Calculation Methods for Logistic Regression]
parameter calculation
   by least squares minimization [in 3.2.1 Parameter Calculation by Least Squares Minimization]
   maximum likelihood [in 3.3.1 Parameter Calculation]
   writing your own class [in 5.5.3 Writing Your Own Parameter Calculation Class]
parameter calculations
   automatic update [in 5.2.1 Updating Parameter Estimates]
   manual update [in 5.2.1 Updating Parameter Estimates]
parameter dispersion matrix [in 3.2.3 Parameter Dispersion (Variance-Covariance) Matrix]
parameter estimate classes [in 5.3 Parameter Estimate Classes]
parameter estimate
   P-value [in 3.3.4.1 p-Values]
parameter variances and covariances [in 3.3.2 Parameter Variances and Covariances]
Pearson statistic [in 3.3.3 Significance of the Model]
   [in 3.3.3.2 Pearson Statistic]
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]
   [in 3.2 Multiple Linear Regression]
   significance [in 3.2.5 Significance of Predictor Variables]

Q

QR method [in 5.5.1.1 RWLeastSqQRCalc]
with pivoting [in 5.5.1.2 RWLeastSqQRPvtCalc]

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]
   class hierarchy [in 2.2 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]
   [in 5.4.1 Class RWLinearRegressionANOVA]
RWLinearRegressionFTest [in 5.1 Overview]
   [in 5.4.3 Class RWLinearRegressionFTest]
RWLinearRegressionParam [in 5.3 Parameter Estimate Classes]
RWLinRegModelSelector<F> [in 5.6.3 Writing Your Own Function Objects]
   [in 5.6 Using the Model Selection Classes]
RWLogisticFitAnalysis [in 5.4.2 Class RWLogisticFitAnalysis]
   [in 5.1 Overview]
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]
RWRegression [in 5.1 Overview]

S

s statistic [in 5.6.3 Writing Your Own Function Objects]
   [in 5.6.3 Writing Your Own Function Objects]
search algorithms
   class derivations [in 2.4 Model Selection Classes]
setCalcMethod() [in 5.5 Parameter Calculation Classes]
significance
   G statistic [in 3.3.3.1 G Statistic]
   of a logistic regression model [in 3.3.3 Significance of the Model]
   of predictor variables [in 3.2.5 Significance of Predictor Variables]
   Pearson statistic [in 3.3.3.2 Pearson Statistic]
simple linear regression [in 3.2 Multiple Linear Regression]
singular value decomposition [in 5.5.1.3 RWLeastSqSVDCalc]
stepwise selection [in 4.2.4 Stepwise Selection]

T

T statistic [in 3.2.5.2 Critical Values]
[in 3.2.5 Significance of Predictor Variables]
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-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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