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78 6 Regressionallows you to choose other levels.) So the model isLogsulphur = a + b × Windspfor dry days, andLogsulphur = a + d + b × Windspfor wet days.The model thus consists of twoparallel regression lines (Figure6.12). The estimates show thatrainfall decreases the sulphur onaverage by 25% (antilog(–0.1240) =75%), but this effect is notstatistically significant there is stilla large amount of unexplainedvariation in the sulphurmeasurements. This version of themodel is very convenient if youwant to make comparisons with thereference level (which may, forexample, represent a standard set ofconditions or treatment). However,we show later in this section howyou can obtain the alternativeversion with a parameter in themodel for each intercept.We can see whether the lineareffect of wind speed is different inthe two categories of rainfall bya d d i n g t h e i n t e r a c t i o nWindsp.Rain.Figure 6.1213 ADD [FPROBABILITY=yes; TPROBABILITY=yes] Windsp.RainRegression analysisResponse variate: LogsulphurFitted terms: Constant + Windsp + Rain + Windsp.RainSummary of analysisSource d.f. s.s. m.s. v.r. F pr.Regression 3 1.92 0.6402 4.47 0.005Residual 108 15.47 0.1432Total 111 17.39 0.1567Change -1 -0.03 0.0323 0.23 0.636Percentage variance accounted for 8.6Standard error of observations is estimated to be 0.378.Message: the following units have large standardized residuals.Unit Response Residual98 1.633 2.61Message: the following units have high leverage.Unit Response Leverage30 0.477 0.160