Modeling of Sea Surface Temperature through Fitting Linear Model with Interaction

Sea surface temperature (SST) is one of the attributes world climate system and global warming. The relationship between SST other parameters can be represented in a linearity approach. Through this approach, variability shows monthly yearly effects. Information on these two time effects important for knowing period peak effect as well statistical measures linear fitting model. models used include transformation without covariate transformation, interaction interaction, with centering addition covariates model chosen basis construction combination giving an increase magnitude multiple R2 (56.62%) adjusted (56.13%) respectively 0.31% 0.43%. This indicates that has very strong significant compared to continuous covariate. In general, significance p-value < 2.2e-16, However, because autocorrelation large AIC value, removed by means autoregressive moving average. obtained data 403.2987.