| Summary: | Multiple regression has special case in a regression analysis. In multiple
regression, there is dependent variable to predict. However, when there are two or
more independent variables, the best model selected will be performed based on 3
(three) methods, i.e. Quadratic Mode Estimator (QME), Symmetrically Trimmed Least
Squares (STLS) and Left Truncated (LT) methods. Data utilized involved data on air
pollution that were produced by 7 (seven) independent variables, involving total
number of vehicles passing by, air temperature, wind speed, temperature difference,
wind, active hours. Censoring on response variable in a regression model is one of the
problems frequently identified in some applications.
Based on the discussion and simulation, several important points are able to be
concluded. In selecting semiparametric regression model, the best one is selected by
using QME method. This may be identified from the least RMSE value. In partial test,
there were 2 (two) significant variables on the dependent variables, i.e. variables in
terms of car and wind speed, and constants, in the form of intercept. In diagnostic
checking, it is concluded that normality test using Kolmogorov Smirnov Test showed
that data was not normally distributed
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