This handful of cases may be the main reason for the curvilinearity we see if we ignore the existence of subgroups. Sadly, the styling for this chart is awful but we could have fixed this with a chart template if we hadn't been so damn lazy.Īnyway, note that R-square -a common effect size measure for regression- is between good and excellent for all groups except upper management. simple slopes analysis in moderation regression.inspecting homogeneity of regression slopes in ANCOVA and.BEGIN GPL SOURCE: s=userSource(id("graphdataset")) DATA: whours=col(source(s), name("whours")) DATA: salary=col(source(s), name("salary")) DATA: jtype=col(source(s), name("jtype"), unit.category()) GUIDE: axis(dim(1), label("On average, how many hours do you work per week?")) GUIDE: axis(dim(2), label("Gross monthly salary")) GUIDE: legend(aesthetic(), label("Current job type")) GUIDE: text.title(label("Scatter Plot of Gross monthly salary by On average, how many hours do ", "you work per week? by Current job type")) SCALE: cat(aesthetic(), include( "1", "2", "3", "4", "5")) ELEMENT: point(position(whours*salary), color.interior(jtype)) END GPL. GGRAPH /GRAPHDATASET NAME="graphdataset" VARIABLES=whours salary jtype MISSING=LISTWISE REPORTMISSING=NO /GRAPHSPEC SOURCE=INLINE /FITLINE TOTAL=NO SUBGROUP=YES. *SCATTERPLOT WITH LINEAR FIT LINES FOR SEPARATE GROUPS.
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