Meaningful Insights from Conditional Means Analysis
Explore the significance of conditional means in data analysis using a CHD2018 dataset. Visualize the relationship between age and SBP1 through scatter plots, group data by age to calculate mean SBP1, rank and summarize the data. Finally, create a data set containing mean SBP1 by age group and plot the results for further analysis.
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Presentation Transcript
The importance of conditional means. = + + 1i s b p a g e 0 1 i i
The chd2018 data set. proc proc reg model sbp1=age; quit quit; reg data=s5238.chd2018 plots=none;
A scatter plot proc proc sgplot sgplot data=s5238.chd2018; scatter x=age y=sbp1/ markerattrs=(symbol=circlefilled); run run;
Form groups by age and calculate mean sbp1 for each age group
proc proc rank run run; proc proc means means data=toplot; class agegrp; var age sbp1; run run; rank data=s5238.chd2018(keep=age sbp1) out=toplot groups=10 10; var age; ranks agegrp;
Create a data set containing mean of sbp1 by age group proc proc means means data=toplot nway noprint; class agegrp; var age sbp1; output mean= out=means; run run; proc proc print print data=means; run run;
Plot mean sbp1 by mean age in groups proc proc sgplot sgplot data=means; series x=age y=sbp1/markers markerattrs=(symbol=circlefilled); run run;
