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Chap5_MultipleLinearRegression

# Chap5_MultipleLinearRegression - Multiple Linear Regression...

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© Galit Shmueli and Peter Bruce 2008 Multiple Linear Regression Data Mining for Business Intelligence Shmueli, Patel & Bruce

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Topics Explanatory vs. predictive modeling with regression Example: prices of Toyota Corollas Fitting a predictive model Assessing predictive accuracy Selecting a subset of predictors
Explanatory Modeling Goal: Explain relationship between predictors (explanatory variables) and target Familiar use of regression in data analysis Model Goal: Fit the data well and understand the contribution of explanatory variables to the model “goodness-of-fit”: R2, residual analysis, p-values

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Predictive Modeling Goal: predict target values in other data where we have predictor values, but not target values Classic data mining context Model Goal: Optimize predictive accuracy Train model on training data Assess performance on validation (hold-out) data Explaining role of predictors is not primary purpose (but useful)
Example: Prices of Toyota Corolla ToyotaCorolla.xls Goal: predict prices of used Toyota Corollas based on their specification Data: Prices of 1442 used Toyota Corollas, with their specification information

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Price Age KM Fuel_Type HP Metallic Automatic cc Doors Quarterly_Tax Weight 13500 23 46986 Diesel 90 1 0 2000 3 210 1165 13750 23 72937 Diesel 90 1 0 2000 3 210 1165 13950 24 41711 Diesel 90 1 0 2000 3 210 1165 14950 26 48000 Diesel 90 0 0 2000 3 210 1165 13750 30 38500 Diesel 90 0 0 2000 3 210 1170 12950 32 61000 Diesel 90 0 0 2000 3 210 1170 16900 27 94612 Diesel 90 1 0 2000 3 210 1245 18600 30 75889 Diesel 90 1 0 2000 3 210 1245 21500 27 19700 Petrol 192 0 0 1800 3 100 1185 12950 23 71138 Diesel 69 0 0 1900 3 185 1105 20950 25 31461 Petrol 192 0 0 1800 3 100 1185 Data Sample (showing only the variables to be used in analysis)
Variables Used Price in Euros

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Chap5_MultipleLinearRegression - Multiple Linear Regression...

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