Hi   I am having a technical difficulty with creating a Teradata view. I am trying to create a view in one Teradata database as selected from a view in a different teradata database, and my only available tool to access both servers is Enterprise Guide!   I can select all the data I want from database A. I can select and write data in database B. But I want to save a view in database B, that actually points to the same view in database A, so that I can manage the changes that the administrators do to A in just my view in B, instead of in every single query against database A.   They have separate authentication domains, and schemas, but they are on the same server. I am able to write the sql.   Example SQL: PROC SQL; CONNECT TO TERADATA as con1 (SERVER=servername authdomain=AuthA); execute( REPLACE VIEW TeraB.test_view AS LOCK TeraA.source_view FOR ACCESS SELECT f1, f2, f3 FROM TeraA.source_view) by con1; execute(commit) by con1; DISCONNECT FROM con1; QUIT;   The code runs successfully, only my view does not work. I am getting a message stating I am have not been granted read access.   Help! Is this a super special case, wanting to access two different teradata databases at the same time?   Regards, Kristine ... View more

Hi   I am using SAS Enterprise Miner 13.2 with the Credit Scoring to build a prediction model for the usage of credit cards.   I suspect a problem with collinearity in my input data, as I always end up with at least one positive effect while the rest is negative. Depending on which criteria and variables I choose to include, this might be a different variable for each setting, and the same variable might be a positive effect in some settings and a negative one in other settings.   What is a good strategy to avoid this problem?   It is very difficult to explain the variables on its own, when you have a variable with opposite effect. Do I risk losing valuable information by excluding the variable?   Is it a good way to identify which of the included variables in the scorecard are related, when explaining this effect? Or just keep the opposite effects and give the answer "because the statistician said so" when asked?   I know that the data might be related, and I am not too worried about new data being from a different population, as we are looking at our own customer database, and will continue to do so.                                      Analysis of Maximum Likelihood Estimates                                                   Standard          Wald                  Standardized Parameter                     DF    Estimate       Error    Chi-Square    Pr > ChiSq        Estimate    Exp(Est)   Intercept                      1     -2.9574      0.0697       1798.25        <.0001                       0.052 WOE_1                       1     -0.7656      0.0718        113.81        <.0001         -1.1490       0.465 WOE_2                       1     -0.3554      0.1008         12.43        0.0004         -0.3569       0.701 WOE_3                       1     -0.4776      0.0592         65.10        <.0001         -0.2544       0.620 WOE_4                       1     -0.2444      0.1340          3.33        0.0682         -0.0642       0.783 WOE_5                       1      0.2427      0.1030          5.55        0.0185          0.0562       1.275   The last effect here is positive, while the rest are negative.   Fit statistics, just for fun Fit         Statistics Statistics Label Train Validation Test           _AIC_ Akaike's Information Criterion 3508.10 . . _ASE_ Average Squared Error 0.05 0.05 0.05 _AVERR_ Average Error Function 0.17 0.17 0.17 _DFE_ Degrees of Freedom for Error 10557.00 . . _DFM_ Model Degrees of Freedom 6.00 . . _DFT_ Total Degrees of Freedom 10563.00 . . _DIV_ Divisor for ASE 21126.00 15846.00 15850.00 _ERR_ Error Function 3496.10 2655.00 2670.64 _FPE_ Final Prediction Error 0.05 . . _MAX_ Maximum Absolute Error 1.00 1.00 0.99 _MSE_ Mean Square Error 0.05 0.05 0.05 _NOBS_ Sum of Frequencies 10563.00 7923.00 7925.00 _NW_ Number of Estimate Weights 6.00 . . _RASE_ Root Average Sum of Squares 0.21 0.21 0.21 _RFPE_ Root Final Prediction Error 0.21 . . _RMSE_ Root Mean Squared Error 0.21 0.21 0.21 _SBC_ Schwarz's Bayesian Criterion 3551.69 . . _SSE_ Sum of Squared Errors 963.57 724.58 731.04 _SUMW_ Sum of Case Weights Times Freq 21126.00 15846.00 15850.00 _MISC_ Misclassification Rate 0.05 0.05 0.05 _AUR_ Area Under ROC 0.83 0.82 0.81 _Gini_ Gini Coefficient 0.65 0.64 0.62 _KS_ Kolmogorov-Smirnov Statistic 0.51 0.52 0.51 _ARATIO_ Accuracy Ratio 0.65 0.64 0.62 ... View more