Model Selection II: datasets with several explanatory variables Quantitative Methods Model Selection II: datasets with several explanatory variables

The problem of model choice Model Selection II: several explanatory variables The problem of model choice

The problem of model choice Model Selection II: several explanatory variables The problem of model choice

The problem of model choice Model Selection II: several explanatory variables The problem of model choice With 5 x-variables, there are 25=32 possible models, not including interactions. If we include two-way interactions without squared terms, there are 1x1 + 5x1 + 10x2 + 10x8 + 5x64 + 1x1024 = 1450 models If we do allow squared terms, there are 1x1 + 5x2 + 10x8 + 10x64 + 5x1024 + 1x32768 = 38619 models. With multiple models, there are many p-values and possible “right-leg/left-leg” and “poets’ dates” effects.

The problem of model choice Model Selection II: several explanatory variables The problem of model choice Economy of variables Multiplicity of p-values Marginality

The problem of model choice Model Selection II: several explanatory variables The problem of model choice

Model Selection II: several explanatory variables Economy of variables

Model Selection II: several explanatory variables Economy of variables

Economy of variables Model Selection II: several explanatory variables all variables increase R2 F<1 - adding the variable decreased R2 adj F>1 - adding the variable increased R2 adj

Economy of variables Model Selection II: several explanatory variables continuous

Model Selection II: several explanatory variables Economy of variables

Economy of variables Model Selection II: several explanatory variables (Predictions for datapoint 39)

Multiplicity of p-values Model Selection II: several explanatory variables Multiplicity of p-values

Multiplicity of p-values Model Selection II: several explanatory variables Multiplicity of p-values

Multiplicity of p-values Model Selection II: several explanatory variables Multiplicity of p-values Focus, don’t fish - reduce number of X-variables - use outside information to decide on inclusion - use outside information to decide on exclusion Stringency - reduce nominal p-value Combine model terms - for once, reverse the usual splitting

Multiplicity of p-values Model Selection II: several explanatory variables Multiplicity of p-values

Multiplicity of p-values Model Selection II: several explanatory variables Multiplicity of p-values DF SeqSS 1 366.9 1 42.7 1 14.7 3 424.3 MS=424.3/3=141.4 F = 141.4/108.9 = 1.30 on 3 and 30 DF Single p-value from Minitab using CDF: p=0.293 CDF 1.30 K1; F 3 30. LET K2=1-K1

Model Selection II: several explanatory variables Stepwise regression

Model Selection II: several explanatory variables Stepwise regression

Stepwise regression Model Selection II: several explanatory variables General Linear Model: LRGWHAL versus Source DF Seq SS Adj SS Adj MS F P VIS 1 61.166 61.166 61.166 193.35 0.000 Error 230 72.759 72.759 0.316 Total 231 133.925 Term Coef SE Coef T P Constant -4.52464 0.06116 -73.98 0.000 VIS 0.125222 0.009005 13.91 0.000

Stepwise regression Model Selection II: several explanatory variables General Linear Model: LRGWHAL versus Source DF Seq SS Adj SS Adj MS F P VIS 1 61.166 61.166 61.166 193.35 0.000 Error 230 72.759 72.759 0.316 Total 231 133.925 Term Coef SE Coef T P Constant -4.52464 0.06116 -73.98 0.000 VIS 0.125222 0.009005 13.91 0.000

Stepwise regression Model Selection II: several explanatory variables General Linear Model: LRGWHAL versus Source DF Seq SS Adj SS Adj MS F P VIS 1 61.166 61.166 61.166 193.35 0.000 Error 230 72.759 72.759 0.316 Total 231 133.925 Term Coef SE Coef T P Constant -4.52464 0.06116 -73.98 0.000 VIS 0.125222 0.009005 13.91 0.000

Stepwise regression Model Selection II: several explanatory variables General Linear Model: LRGWHAL versus Source DF Seq SS Adj SS Adj MS F P VIS 1 61.166 61.166 61.166 193.35 0.000 Error 230 72.759 72.759 0.316 Total 231 133.925 Term Coef SE Coef T P Constant -4.52464 0.06116 -73.98 0.000 VIS 0.125222 0.009005 13.91 0.000

Model Selection II: several explanatory variables Stepwise regression

Stepwise regression Model Selection II: several explanatory variables Forward ≠ Backward Forward = Backward

Model Selection II: several explanatory variables Stepwise regression

Model Selection II: several explanatory variables Stepwise regression

Model Selection II: several explanatory variables Stepwise regression

Model Selection II: several explanatory variables Stepwise regression

Model Selection II: several explanatory variables Stepwise regression

Random Effects Read Chapter 12 Last words… Model Selection II: several explanatory variables Last words… Economy of variables: prediction, adjusted R2 Multiplicity: outside information, focussing, stringency, combining model terms Stepwise regressions not usually suitable -- but are for initial sifting of a large number of potential predictors in a preliminary study Random Effects Read Chapter 12