# What do these SAS plots imply about the regression outcomes?

I have run a Tobit regression, and in the output the following charts are generated (at the end of parameter estimates). I cannot relate these charts with the parameter estimates. Could someone give some idea?

The code I have run is:

proc qlim data= MXN_Jump_News; model Abs_Jump= DW_Mon DW_Tue DW_Wed DW_Thu Sin1 Sin2 Sin3 Sin4 Cos1 Cos2 Cos3 Cos4 Abs_Surp_US4 Abs_Surp_US7
Abs_Surp_US8 Abs_Surp_US9 Abs_Surp_US11 Abs_Surp_US16 Abs_Surp_US20 Abs_Surp_US22
PS_US30 Abs_Surp_GER12 Abs_Surp_MEX5 Abs_Surp_MEX6 Abs_Surp_MEX8 Abs_Surp_MEX10
Abs_Surp_MEX14 PS_MEX16 SP4 SP16 SP21 SP23; nloptions maxiter= 500;endogenous Abs_Jump ~ censored (lb= 0);run;


DW_Mon to DW_Thu are dummies. SP4 SP16 SP21 SP23 are also dummies. Sin1-Sin4 and Cos1-Cos4 are sine and cosine values. The dependent variable has only positive values. The other independent variables take both positive and negative values.

The results are the following:

• I think you'll need to provide more info here. What regression did you estimate? What command did you run to create these plots? What is the nature of your dependent variable? What are the axis of these plots? Do they represent the full range of values of each of the predictors? Adding the relevant parts of your code to the question would probably be useful. – AlexK May 30 '19 at 6:15
• I am sorry for the confusion. I am adding details here. – Deepan Das May 30 '19 at 19:16
• I have just edited my post. I think I have been clear this time. Please let me know if there's anything else. Much thanks. – Deepan Das May 30 '19 at 19:30
• I think you should add your binary dummy variables to the CLASS statement, so they are treated as categorical variables, not as continuous. I would also recommend actually calculating predicted values for each observation and then comparing them to the actual values. These plots seem hard to interpret given the number of predictors you have. In case you don't know, in these plots every other predictor (other than the one shown in a given plot) is set equal to its mean. It'd also be helpful to look at the distribution of your dependent variable (histogram and/or percentiles/mean/min/max table). – AlexK May 30 '19 at 21:37