Creating models, running regressions, and making predictions
About models
Models describe relationships in a table and can be used to make predictions
Viewing a model’s adjusted R-squared
The adjusted R-squared is a measure of model fit
Viewing the confidence intervals of a model’s explanatory variables
Values inside a coefficient’s confidence interval are not significantly different from the estimate
Exporting a model as R code
Choose Model > Show Estimation Command to see R code for the current model
Exporting a model as a Stata, SPSS, or SAS command (Pro only)
Choose Model > Show Estimation Command to see command syntax for the current model
Copying a model’s predicted values to the host table
Choose Model > Copy Predictions to Host Table to create a numeric column out of the model’s predicted values
Copying model residuals to the host table
Choose Model > Copy Residuals to Host Table to create a numeric column out of the model residuals
About proportional hazards models
Proportional hazards models can be used to model failure times or life events
Defining the cause of failure in a proportional hazards models
Modeling multiple causes of failure produces separate coefficients for each cause
Treating records as censored in a proportional hazards models
Censoring is appropriate if some records never experience a failure
Defining default outcome values in a proportional hazards models
A default column is useful when censored observations appear in a different column than non-censored observations
Stratifying a proportional hazards model by a category column
Stratification is appropriate when different groups of observations have different underlying hazard rates
Creating a model
Create a multivariate model from the Summary view
Deleting a model
A model can be deleted from the Model menu
Duplicating a model
Duplicate a model by control-clicking it in the navigator
Adding and removing variables from a model
A model’s list of explanatory variables can be modified after creation
Treating an explanatory variable as categorical data
Categorical explanatory variables are appropriate for discrete data
Exporting a model as an interactive spreadsheet
Export a model’s coefficients and predictions as an interactive Excel spreadsheet
Exporting a model’s coefficient table
Export a model’s coefficient table as CSV, Excel, or JSON for use in other programs
Including higher-order terms of a numeric explanatory variable in a model
Higher-order terms can provide a better model fit, but should be used with caution
Creating an interaction term in a model
Interaction terms control for the interaction effects of two explanatory variables
Testing hypotheses about multiple coefficients in a model
Test model coefficients for equality, or whether they sum to 0 or 1
Testing the joint significance of multiple coefficients in a model
Perform a combined test by selecting multiple rows in the explanatory variable table
Viewing a model’s log-likelihood
The log-likelihood is a measure of model fit
Viewing the odds ratios of a model
The odds ratio is an alternative representation of a model’s coefficients
Specifying the omitted category of a categorical explanatory variable
When an explanatory variable is treated as categories, its estimated coefficients are relative to an omitted category
Setting the base outcome of a categorical outcome variable
When the outcome variable is categorical, estimated coefficients are always relative to a base outcome
Treating an outcome variable as categorical data
Categorical outcome variables are appropriate for discrete-response models
Treating an outcome variable as continuous data
Treat an outcome as continuous if it takes a range of ordered values
Setting the count exposure of an outcome variable
An exposure column defines the relative opportunity that each observation had to receive events
Viewing the p-values of a model’s explanatory variables
The p-value conveys the statistical significance of an model coefficient
Viewing a model’s unadjusted R-squared
The unadjusted R-squared is a measure of model fit
Renaming a model
A model can be renamed by double-clicking it in the navigator
Rearranging models
A model can be rearranged via drag-and-drop in the navigator
Viewing the z-scores or t-statistics of a model’s explanatory variables
The z-score or t-statistic equals the coefficient divided by the standard error, and reflects coefficient’s statistical significance