Proc univariate histogram rename x axis series#

Proc univariate histogram rename x axis series#

*Part B*/ proc sgplot data=sp4r.sales series x=month y=revenue / legendlabel='Company A' lineattrs=(color=blue pattern=dash) series x=month y=revenue_2 / legendlabel='Company B' lineattrs=(color=red pattern=dash) *Part E*/ * regression, confidence limits and prediction limits proc sgplot data=ries_data reg x=x y=y1 / clm cli reg x=x y=y2 / clm cli run Įnhancing the plot. 5 y2 = beta02 + beta12*x + rand('Normal',0,5) output end run proc sgplot data=ries_data scatter x=x y=y1 scatter x=x y=y2 run proc sgplot data=ries_data series x=x y=y1 series x=x y=y2 run proc sgplot data=ries_data series x=x y=y1 scatter x=x y=y1 series x=x y=y2 scatter x=x y=y2 run

*Part C*/ data sp4r.sales call streaminit(123) do month=1 to 12 revenue = rand('Normal',10000,5000) output end run proc sgplot data=sp4r.sales vbar month / response=revenue run *Part B*/ data sp4r.boxplot_data (drop=rep) call streaminit(123) do group=1 to 3 do rep=1 to 100 response = rand('exponential')*10 output end end run proc sgplot data=sp4r.boxplot_data hbox response run proc sgplot data=sp4r.boxplot_data hbox response / category=group run Proc sgplot data=sp4r.hist_data histogram x / binwidth=1 density x / type=normal density x / type=kernel run *Part A*/ data sp4r.hist_data call streaminit(123) do i=1 to 1000 x = rand('exponential')*10 output end run proc sgplot data=sp4r.hist_data histogram x run Proc sgplot data=sales scatter x=month y=revenue / group=company run proc sgplot data=sales scatter x=month y=revenue by company run Proc sgplot data=sales scatter x=month y=revenue scatter x=month y=revenue_2 *series x=month y=revenue *series x=month y=revenue_2 run