BookmarkSubscribeRSS Feed
yael
Quartz | Level 8

Hello,

 

I need your advice,

 

I have 3 groups in my research: Firms that founded on 1930 (FF - dummy variable), Firms that founded on 1970 (FNF - dummy variable), and other firms.

I also have the continous variables that describe the largest ownership in the relevant firm, FOWN for FF firm and FNFOWN for FNF firms.

If I want to check the ownership impact on performance, should I run regression as:

proc reg data=sasuser.sasfile;

model performance = FOWN FNFOWN ;

run;

 

Or should I create a new variable named, for example, OWN, that reflect that largest owners in each firm but the code will be:

proc reg data=sasuser.sasfile;

model performance = OWN*FF OWN*FNF ; (I will create a new variable for each duplicated variables)

run;

 

Thanks for your help!!!!

2 REPLIES 2
PeterClemmensen
Tourmaline | Level 20

I am assuming that your dummy variables are coded as 0/1 as PROC REG does not support a class statement.

 

I would recommed your first choice. You can also specify multiple model statements like this

 

proc reg data=sasuser.sasfile;
    model performance = FOWN;
    model performance = FNFOWN;
run;
yael
Quartz | Level 8

Thanks for your answer

But my question is what is the difference between the two options I mentioned?

sas-innovate-2024.png

Don't miss out on SAS Innovate - Register now for the FREE Livestream!

Can't make it to Vegas? No problem! Watch our general sessions LIVE or on-demand starting April 17th. Hear from SAS execs, best-selling author Adam Grant, Hot Ones host Sean Evans, top tech journalist Kara Swisher, AI expert Cassie Kozyrkov, and the mind-blowing dance crew iLuminate! Plus, get access to over 20 breakout sessions.

 

Register now!

What is ANOVA?

ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.

Find more tutorials on the SAS Users YouTube channel.

Discussion stats
  • 2 replies
  • 1228 views
  • 1 like
  • 2 in conversation