Multiple regression, backward elimination

Mapping displays 23 Matrix, confusion, 127 determinant, 212 dispersion, 82 identity, 206 inverse, 210 quadratic form, 212 singular, 211 square, 204 symmetric, 204 Matrix multiplication, 207 Mean centring, 17 Mean value, 2 Membership function, 117 Minkowski metrics, 99 Moving average, 36 Multiple correlation, 183 Multiple regression, backward elimination, 182... 

Forward or backward elimination (as used in multiple linear regression)... 

Table 4.1. models (using backwards elimination stepwise multiple regression) for the study lakes based on suites of parameters. The global indices based on annual and seasonal data, with and without lags (1 and 2-year) and deposition provided the best models. Local and regional-scale climate parameters (e.g., precipitation, temperature, etc.) did not provide significant models... 

Linear models were generated using multiple linear regression analysis techniques. Several methods were utilized to develop models for evaluation, including stepwise addition, backward elimination, and leaps and bounds regression techniques. The models were evaluated with respect to the multiple correlation coefficient (r), the standard error (s), and predictive ability of the model. 

Backward elimination is a variable selection algorithm for multiple linear regression it starts with all variables in the model and eliminates all nonsignificant variables see forward selection as well. 

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