I have followed the instructions on what to do to fix the 'Not Running Genuine Windows' error msg. Using Method C.I copied and pasted the following:Method C: Add Registry Key1. Copy the below entry as is on a notepad and save the text file to Profilelist.reg2. Merge profilelist.reg3.
PROC REG;The PROC REG statement is required. If you want to fit a model to the data, you must also use a statement. If you want to use only the PROC REG options, you do not need a statement, but you must use a statement.
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If you do not use a statement, then the COVOUT and OUTEST= options are not available.lists the options you can use with the PROC REG statement. Note that any option specified in the statement applies to all statements. Table 73.1 PROC REG Statement OptionsOptionDescriptionData Set Optionsnames a data set to use for the regressionoutputs a data set that contains parameter estimates and othermodel fit summary statisticsoutputs a data set that contains sums of squares and crossproductsoutputs the covariance matrix for parameter estimates to theOUTEST= data setoutputs the number of regressors, the error degrees of freedom,and the model to the OUTEST= data setoutputs standard errors of the parameter estimates to theOUTEST= data setoutputs standardized parameter estimates to the OUTEST= dataset.
ALLrequests the display of many tables. Using the ALL option in the statement is equivalent to specifying ALL in every statement. The ALL option also implies the, and options. ALPHA= numbersets the significance level used for the construction of confidence intervals.
The value must be between 0 and 1; the default value of 0.05 results in 95% intervals. This option affects the PROC REG option TABLEOUT; the options CLB, CLI, and CLM; the statement keywords LCL, LCLM, UCL, and UCLM; the statement keywords LCL., LCLM., UCL., and UCLM.; and the statement options CONF and PRED.
ANNOTATE= SAS-data-set ANNO= SAS-data-setspecifies an input data set containing annotate variables, as described in SAS/GRAPH Software: Reference. You can use this data set to add features to the traditional graphics that you request with the statement. Features provided in this data set are applied to all plots produced in the current run of PROC REG.
To add features to individual plots, use the ANNOTATE= option in the statement. This option cannot be used if the option is specified. CORRdisplays the correlation matrix for all variables listed in the or statement. COVOUToutputs the covariance matrices for the parameter estimates to the OUTEST= data set. This option is valid only if the option is also specified. See the section. DATA= SAS-data-setnames the SAS data set to be used by PROC REG.
The data set can be an ordinary SAS data set or a TYPE=CORR, TYPE=COV, or TYPE=SSCP data set. If one of these special TYPE= data sets is used, the, and statements, ODS Graphics, and some options in the and statements are not available. SeeAppendix A,for more information about TYPE= data sets. If the DATA= option is not specified, PROC REG uses the most recently created SAS data set. EDFoutputs the number of regressors in the model excluding and including the intercept, the error degrees of freedom, and the model to the OUTEST= data set. GOUT= graphics-catalogspecifies the graphics catalog in which traditional graphics output is saved.
The default graphics-catalog is WORK.GSEG. The GOUT= option cannot be used if the option is specified.
LINEPRINTER LPcreates plots requested as line printer plots. If you do not specify this option, requested plots are created on a high-resolution graphics device. This option is required if plots are requested and you do not have SAS/GRAPH software. NOPRINTsuppresses the normal display of results.
Note that this option temporarily disables the Output Delivery System (ODS); seeChapter 20,for more information. OUTEST= SAS-data-setrequests that parameter estimates and optional model fit summary statistics be output to this data set. See the section for details. If you want to create a permanent SAS data set, you must specify a two-level name (refer to the section 'SAS Files' in SAS Language Reference: Concepts for more information about permanent SAS data sets). OUTSEBoutputs the standard errors of the parameter estimates to the OUTEST= data set. The value SEB for the variable TYPE identifies the standard errors. If the RIDGE= or PCOMIT= option is specified, additional observations are included and identified by the values RIDGESEB and IPCSEB, respectively, for the variable TYPE.
The standard errors for ridge regression estimates and IPC estimates are limited in their usefulness because these estimates are biased. This option is available for all model selection methods except RSQUARE, ADJRSQ, and CP. OUTSSCP= SAS-data-setrequests that the sums of squares and crossproducts matrix be output to this TYPE=SSCP data set.
See the section for details. If you want to create a permanent SAS data set, you must specify a two-level name (refer to the section 'SAS Files' in SAS Language Reference: Concepts for more information about permanent SAS data sets). OUTSTBoutputs the standardized parameter estimates as well as the usual estimates to the OUTEST= data set when the RIDGE= or PCOMIT= option is specified.
The values RIDGESTB and IPCSTB for the variable TYPE identify ridge regression estimates and IPC estimates, respectively. OUTVIFoutputs the variance inflation factors (VIF) to the OUTEST= data set when the RIDGE= or PCOMIT= option is specified. The factors are the diagonal elements of the inverse of the correlation matrix of regressors as adjusted by ridge regression or IPC analysis. These observations are identified in the output data set by the values RIDGEVIF and IPCVIF for the variable TYPE. PCOMIT= listrequests an incomplete principal component (IPC) analysis for each value m in the list.
The procedure computes parameter estimates by using all but the last m principal components. Each value of m produces a set of IPC estimates, which are output to the OUTEST= data set. The values of m are saved by the variable PCOMIT, and the value of the variable TYPE is set to IPC to identify the estimates.
Only nonnegative integers can be specified with the PCOMIT= option.If you specify the PCOMIT= option, statements are ignored. PLOTS PLOTS )controls the plots produced through ODS Graphics.
When you specify only one plot request, you can omit the parentheses around the plot request. Here are some examples. Ods graphics on;proc reg;model y = x1-x10;run;proc reg plots=diagnostics(stats=(default aic sbc));model y = x1-x10;run;ods graphics off;If you have enabled ODS Graphics but do not specify the PLOTS= option, then PROC REG produces a default set of plots. Lists the default set of plots produced. Table 73.2 Default ODS Graphics ProducedPlotConditional OnDiagnosticsPanelUnconditionalResidualPlotUnconditionalFitPlotModel with one regressor (excluding intercept)PartialPlotPARTIAL option specified in statementRidgePanelRIDGE= option specified in or statementFor models with multiple dependent variables, separate plots are produced for each dependent variable.
For jobs with more than one statement, plots are produced for each model statement.The global-options apply to all plots generated by the REG procedure, unless it is altered by a specific-plot-option. The following global plot options are available.
LABELspecifies that the LABEL option be applied to each plot that supports a LABEL option. See the descriptions of the specific plots for details. MAXPOINTS= NONE numberspecifies that plots with elements that require processing more than number points be suppressed. The default is MAXPOINTS=5000. This cutoff is ignored if you specify MAXPOINTS=NONE. MODELLABELrequests that the model label be displayed in the upper-left corner of all plots.
This option is useful when you use more than one statement. ONLYsuppress the default plots.
Only plots specifically requested are displayed. STATS= ALL DEFAULT NONE ( plot-statistics)requests statistics that are included on the fit plot and diagnostics panel. Lists the statistics that you can request.
STATS=ALL requests all these statistics; STATS=NONE suppresses them. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the model with the largest adjusted R-square statistic at each value of the number of parameters. LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the largest adjusted R-square statistic at each value of the number of parameters. AIC displays Akaike’s information criterion (AIC) for the models examined when you request variable selection with the SELECTION= option in the statement.The following aic-options are available for models where you request the RSQUARE, ADJRSQ, or CP selection method. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the model with the smallest AIC statistic at each value of the number of parameters. LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the smallest AIC statistic at each value of the number of parameters.
ALLproduces all appropriate plots. BIC displays Sawa’s Bayesian information criterion (BIC) for the models examined when you request variable selection with the SELECTION= option in the statement.The following bic-options are available for models where you request the RSQUARE, ADJRSQ, or CP selection method. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the model with the smallest BIC statistic at each value of the number of parameters.
LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the smallest BIC statistic at each value of the number of parameters. COOKSD plots Cook’s statistic by observation number. Observations whose Cook’s statistic lies above the horizontal reference line at value, where is the number of observations used, are deemed to be influential (Rawlings 1998). If you specify the LABEL option, then points deemed as influential are labeled. If you do not specify an ID variable, the observation number within the current BY group is used as the label.
If you specify one or more ID variables in one or more ID statements, then the first ID variable you specify is used for the labeling. CP displays Mallow’s statistic for the models examined when you request variable selection with the SELECTION= option in the statement. For models where you request the RSQUARE, ADJRSQ, or CP selection, reference lines corresponding to the equations and, where is the number of parameters in the full model (excluding the intercept) and is the number of parameters in the subset model (including the intercept), are displayed on the plot of versus. For the purpose of parameter estimation, Hocking (1976) suggests selecting a model where. For the purpose of prediction, Hocking suggests the criterion.
Mallows (1973) suggests that all subset models with small and near be considered for further study.The following cp-options are available for models where you request the RSQUARE, ADJRSQ, or CP selection method. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the model with the smallest statistic at each value of the number of parameters.
LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the smallest statistic at each value of the number of parameters. CRITERIA CRITERIONPANEL produces a panel of fit criteria for the models examined when you request variable selection with the SELECTION= option in the statement. The fit criteria displayed are R-square, adjusted R-square, Mallow’s, Akaike’s information criterion (AIC), Sawa’s Bayesian information criterion (BIC), and Schwarz’s Bayesian information criterion (SBC). For SELECTION=RSQUARE, SELECTION=ADJRSQ, or SELECTION=CP, scatter plots of these statistics versus the number of parameters (including the intercept) are displayed.
For other selection methods, line plots of these statistics as function of the selection step number are displayed.The following criteria-options are available. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the best model at each value of the number of parameters. This option applies only to the RSQUARE, ADJRSQ, and CP selection methods. LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the best model at each value of the number of parameters. Since these labels are typically long, LABELVARS is supported only when the panel is unpacked. This option applies only to the RSQUARE, ADJRSQ, and CP selection methods.
UNPACKsuppresses paneling. Separate plots are produced for each of the six fit statistics. For models where you request the RSQUARE, ADJRSQ, or CP selection, two reference lines corresponding to the equations and, where is the number of parameters in the full model (excluding the intercept) and is the number of parameters in the subset model (including the intercept), are displayed on the plot of versus. For the purpose of parameter estimation, Hocking (1976) suggests selecting a model where.
For the purpose of prediction, Hocking suggests the criterion. Mallows (1973) suggests that all subset models with small and near be considered for further study. DFBETAS produces panels of DFBETAS by observation number for the regressors in the model. Note that each panel contains at most six plots, and multiple panels are used in the case where there are more than six regressors (including the intercept) in the model. Observations whose DFBETAS’ statistics for a regressor are greater in magnitude than, where is the number of observations used, are deemed to be influential for that regressor (Rawlings 1998).The following DFBETAS-options are available. COMMONAXESspecifies that the same DFBETAS axis be used in all panels when multiple panels are needed. By default, the DFBETAS axis is chosen independently for each panel.
If you also specify the UNPACK option, then the same DFBETAS axis is used for each regressor. LABELspecifies that observations whose magnitude are greater than be labeled. If you do not specify an ID variable, the observation number within the current BY group is used as the label. If you specify one or more ID variables on one or more ID statements, then the first ID variable you specify is used for the labeling. UNPACKsuppresses paneling.
The DFBETAS statistics for each regressor are displayed on separate plots. DFFITS plots the DFFITS statistic by observation number. Observations whose DFFITS’ statistic is greater in magnitude than, where is the number of observations used and is the number of regressors, are deemed to be influential (Rawlings 1998). If you specify the LABEL option, then these influential observations are labeled.
If you do not specify an ID variable, the observation number within the current BY group is used as the label. STATS= stats-optionsdetermines which model fit statistics are included in the panel. See the global STATS= suboption for details.
The PLOTS= suboption of the DIAGNOSTICSPANEL option overrides the global PLOTS= suboption. UNPACKproduces the eight plots in the panel as individual plots. Note that you can also request individual plots in the panel by name without having to unpack the panel.
FITPLOT FIT produces a scatter plot of the data overlaid with the regression line, confidence band, and prediction band for models that depend on at most one regressor excluding the intercept.You can specify the following fit-options. NOCLIsuppresses the prediction limits. NOCLMsuppresses the confidence limits.
NOLIMITSsuppresses the confidence and prediction limits. STATS= stats-optionsdetermines which model fit statistics are included in the panel. See the global STATS= suboption for details. The PLOTS= suboption of the FITPLOT option overrides the global PLOTS= suboption. OBSERVEDBYPREDICTED plots dependent variable values by the predicted values.
If you specify the LABEL option, then points deemed as outliers or influential (see the RSTUDENTBYLEVERAGE option for details) are labeled. NONEsuppresses all plots. How to mod sse build. PARTIAL produces panels of partial regression plots for each regressor with at most six regressors per panel. If you specify the UNPACK option, then all partial plot panels are unpacked.
PREDICTIONS (X= numeric-variable )produces a panel of two plots whose horizontal axis is the variable you specify in the required X= suboption. The upper plot in the panel is a scatter plot of the residuals.
The lower plot shows the data overlaid with the regression line, confidence band, and prediction band. This plot is appropriate for models where all regressors are known to be functions of the single variable that you specify in the X= suboption.You can specify the following prediction-options. NOCLIsuppresses the prediction limits.
NOCLMsuppresses the confidence limits NOLIMITSsuppresses the confidence and prediction limits SMOOTHrequests a nonparametric smooth of the residuals as a function of the variable you specify in the X= suboption. This nonparametric fit is a loess fit that uses local linear polynomials, linear interpolation, and a smoothing parameter selected that yields a local minimum of the corrected Akaike information criterion (AICC). SeeChapter 50,for details. The SMOOTH option is not supported when a statement is used.
UNPACKsuppresses paneling. QQPLOT QQproduces a normal quantile plot of the residuals. RESIDUALBOXPLOT BOXPLOT produces a box plot consisting of the residuals.
If you specify label option, points deemed far-outliers are labeled. If you do not specify an ID variable, the observation number within the current BY group is used as the label. If you specify one or more ID variables in one or more ID statements, then the first ID variable you specify is used for the labeling. RESIDUALBYPREDICTED plots residuals by predicted values.
If you specify the LABEL option, then points deemed as outliers or influential (see the RSTUDENTBYLEVERAGE option for details) are labeled. RESIDUALS produces panels of the residuals versus the regressors in the model. Note that each panel contains at most six plots, and multiple panels are used in the case where there are more than six regressors (including the intercept) in the model.The following residual-options are available. SMOOTHrequests a nonparametric smooth of the residuals for each regressor.
Each nonparametric fit is a loess fit that uses local linear polynomials, linear interpolation, and a smoothing parameter selected that yields a local minimum of the corrected Akaike information criterion (AICC). SeeChapter 50,for details. The SMOOTH option is not supported when a statement is used. UNPACKsuppresses paneling. RESIDUALHISTOGRAMproduces a histogram of the residuals. RFPLOT RFproduces a 'Residual-Fit' (or RF) plot consisting of side-by-side quantile plots of the centered fit and the residuals.
This plot 'shows how much variation in the data is explained by the fit and how much remains in the residuals' (Cleveland 1993). RIDGE RIDGEPANEL RIDGEPLOT creates panels of VIF values and standardized ridge estimates by ridge values for each coefficient.
The VIF values for each coefficient are connected by lines and are displayed in the upper plot in each panel. The points corresponding to the standardized estimates of each coefficient are connected by lines and are displayed in the lower plot in each panel. By default, at most 10 coefficients are represented in a panel and multiple panels are produced for models with more than 10 regressors. For ridge estimates to be computed and plotted, the OUTEST= option must be specified in the statement, and the RIDGE= list must be specified in either the or the statement.
(See.)The following ridge-options are available. COMMONAXESspecifies that the same VIF axis and the same standardized estimate axis are used in all panels when multiple panels are needed. By default, these axes are chosen independently for the regressors shown in each panel. RIDGEAXIS= LINEAR LOGspecifies the axis type used to display the ridge parameters.
The default is RIDGEAXIS=LINEAR. Note that the point with the ridge parameter equal to zero is not displayed if you specify RIDGEAXIS=LOG. UNPACKsuppresses paneling.
The traces of the VIF statistics and standardized estimates are shown in separate plots. VARSPERPLOT= ALL VARSPERPLOT= numberspecifies the maximum number of regressors displayed in each panel or in each plot if you additionally specify the UNPACK option.
What Is Regrun Real
If you specify VARSPERPLOT=ALL, then the VIF values and ridge traces for all regressors are displayed in a single panel. VIFAXIS= LINEAR LOGspecifies the axis type used to display the VIF statistics. The default is VIFAXIS=LINEAR. RSQUARE displays the R-square values for the models examined when you request variable selection with the SELECTION= option in the statement.The following rsquare-options are available for models where you request the RSQUARE, ADJRSQ, or CP selection method. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the model with the largest R-square statistic at each value of the number of parameters.
LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the largest R-square statistic at each value of the number of parameters. RSTUDENTBYLEVERAGE plots studentized residuals by leverage. Observations whose studentized residuals lie outside the band between the reference lines are deemed outliers. Observations whose leverage values are greater than the vertical reference, where is the number of parameters excluding the intercept and is the number of observations used, are deemed influential (Rawlings 1998). If you specify the LABEL option, then points deemed as outliers or influential are labeled. If you do not specify an ID variable, the observation number within the current BY group is used as the label. If you specify one or more ID variables in one or more ID statements, then the first ID variable you specify is used for the labeling.
RSTUDENTBYPREDICTED plots studentized residuals by predicted values. If you specify the LABEL option, then points deemed as outliers or influential (see the RSTUDENTBYLEVERAGE option for details) are labeled.
What Is Regrounding
SBC displays Schwarz’s Bayesian information criterion (SBC) for the models examined when you request variable selection with the SELECTION= option in the statement.The following sbc-options are available for models where you request the RSQUARE, ADJRSQ, or CP selection method. LABELrequests that the model number corresponding to the one displayed in the 'Subset Selection Summary' table be used to label the model with the smallest SBC statistic at each value of the number of parameters. LABELVARSrequests that the list (excluding the intercept) of the regressors in the relevant model be used to label the model with the smallest SBC statistic at each value of the number of parameters. PRESSoutputs the PRESS statistic to the OUTEST= data set.
The values of this statistic are saved in the variable PRESS. This option is available for all model selection methods except RSQUARE, ADJRSQ, and CP. RIDGE= listrequests a ridge regression analysis and specifies the values of the ridge constant k (see the section ). Each value of k produces a set of ridge regression estimates that are placed in the OUTEST= data set.
The values of k are saved by the variable RIDGE, and the value of the variable TYPE is set to RIDGE to identify the estimates.Only nonnegative numbers can be specified with the RIDGE= option. Illustrates this option.If ODS Graphics is in effect (see the section ), then ridge regression plots are automatically produced. These plots consist of panels containing ridge traces for the regressors, with at most eight ridge traces per panel.If you specify the RIDGE= option, statements are ignored. RSQUAREhas the same effect as the option. SIMPLEdisplays the sum, mean, variance, standard deviation, and uncorrected sum of squares for each variable used in PROC REG. SINGULAR= ntunes the mechanism used to check for singularities.
The default value is machine dependent but is approximately 1E 7 on most machines. This option is rarely needed.Singularity checking is described in the section. TABLEOUToutputs the standard errors and% confidence limits for the parameter estimates, the statistics for testing if the estimates are zero, and the associated -values to the OUTEST= data set. The TYPE variable values STDERR, L B, U B, T, and PVALUE, where, identify these rows in the OUTEST= data set.
The level can be set with the ALPHA= option in the or statement. The option must be specified in the statement for this option to take effect.
USSCPdisplays the uncorrected sums-of-squares and crossproducts matrix for all variables used in the procedure.