Wk6 DQ – Advanced Statistical Concepts and Business Analytics

Chapter 16
Predictive Analytics II: Logistic Regression, Discriminate Analysis, and Neural Networks

Copyright ©2018 McGraw-Hill Education. All rights reserved.

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Chapter Outline
16.1 Logistic Regression
16.2 Linear Discriminate Analysis
16.3 Neural Networks
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16.1 Logistic Regression
The general logistic regression model relates the probability that an event will occur to k independent variables
The general model is

Y is a dummy variable that equals one if the event has occurred and zero otherwise
Odds ratio is the probability of success divided by the probability of failure
Equation is
LO16-1: Use a logistic model to estimate probabilities and odds ratios.
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LO16-1
Logistic Regression of the Price Reduction Data
Figure 16.1

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LO16-1
Logistic Regression of the Performance Data
Figure 16.3
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16.2 Linear Discriminate Analysis
Classifies an observation and estimates the probability that the observation will fall into a particular class
Calculate the squared distance between each class’s predictor variable value means and an observation’s predictor variable values
Observation put into the class with the smallest squared distance
Easiest classification analytic to use when there are more than two classes
LO16-2: Use linear discriminate analysis to classify observations and estimate probabilities.
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LO16-2
Results of a Linear Discriminate Analysis
Figure 16.11 partial
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16.3 Neural Networks
Regression techniques so far developed for n = 1,000 or less
Not uncommon for data mining projects to have millions of observations
Neural network modeling developed to handle large data sets
Idea is to represent the response variable as a nonlinear function of linear combinations of the predictor variables
Most common is the single-hidden-layer, feedforward neural network
LO16-3: Use neural network modeling to estimate probabilities and predict values of quantitative response variables.
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LO16-3
Single-Hidden-Layer, Feedforward Neural Network
An input layer consisting of predictor variables x1, x2, … xk
A single hidden layer consisting of m hidden nodes
An output layer where we form a linear combination L of the m hidden node functions
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LO16-3
The Single Layer Perceptron
Figure 16.17
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LO16-3
Neural Networks Continued
Because a neural network model employs many parameters, we say it is overparametrized
There is a danger we will overfit the model
Model finds parameter estimates that minimize a penalized least squares criterion
The penalty equals  times the sum of the squared value of the parameter estimates
The penalty weight  controls the tradeoff between overfitting and underfitting
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