This article describes how to run a Generalized Linear Model. There are seven types of regression analyses to choose from. The linear regression model is the default.

## Requirements

- Familiarity with the
*Structure*and*Value Attributes*of Variable Sets, and how they are used in regression models per our Driver Analysis ebook. - An
**Outcome**variable to be predicted. **Predictors**variables will be considered as predictors of the outcome variable.

## Method

- In the
**Anything**menu select**Advanced Analysis > Regression > Generalized Linear Model**. - In the
**object inspector**go to the**Inputs**tab. - In the
**Output**menu select the variable to be predicted by the*predictor variables.* - Select the predictor variable(s) from the
**Predictor(s)**list. - OPTIONAL: Select the
**Regression type**. There are seven types of regression analysis to choose from. The linear regression model is the default. - OPTIONAL: Select the desired
**Output**type:**Summary**: The default; as shown in the example above.**Detail**: Typical R output, some additional information compared to**Summary**, but without the pretty formatting.**ANOVA**: Analysis of variance table containing the results of Chi-squared likelihood ratio tests for each predictor.**Shapley Regression**output.**Jaccard Coefficient**output.**Correlation**output.**Relative Importance Analysis**: The results of a relative importance analysis.**Effects Plot**Plots the relationship between each of the*Predictors*and the*Outcome*.

- OPTIONAL: Select the desired
**Missing Data**treatment. (See Missing Data Options). - OPTIONAL: Select
**Variable names**to display variable names in the output instead of labels. - OPTIONAL: Select
**Correction**. Choose between**None**(the default),**False Discovery Rate**,**Bonferroni**. - OPTIONAL: Select
**Robust standard errors**to compute standard errors that are robust to violations of the assumption of constant variance (eg, heteroscedasticity). - OPTIONAL: Select
**Absolute importance scores**to display the absolute value of Relative Importance Analysis scores. - OPTIONAL: Specify the
**Automated outlier removal**percentage to remove possible outliers. - OPTIONAL: Select
**Stack data**to stack the input data prior to analysis. Stacking can be desirable when each individual in the data set has multiple cases and an aggregate model is desired.

## See Also

How to Run Binary Logit Regression

How to Run a Multinomial Logit Regression

How to Run NBD Regression in Displayr

How to Run Ordered Logit Regression

How to Run Quasi-Poisson Regression

How to Run a Stepwise Regression

How to Create Regression Multicollinearity Table (VIF)

How to Create a Prediction-Accuracy Table

How to Create a Goodness-of-Fit Plot

How to Test Residual Heteroscedasticity of Regression Models

How to Save Predicted Values of Regression Models

How to Save Fitted Values of Regression Models

How to Save Probabilities of Each Response of Regression Models

How to Test Residual Normality (Shapiro-Wilk) of Regression Models

How to Test Residual Serial Correlation (Durbin-Watson) of Regression Models

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