Linear Models

Example: Linear Regression with Fixed Effects
Assigning Data to Roles
Setting the Model Options
Setting the Options
Creating the Output Data Sets

Example: Linear Regression with Fixed Effects

To create this example:
  1. Create the Work.Cigar data set. For more information, see CIGAR Data Set.
  2. In the Tasks section, expand the Econometrics folder, and then double-click Cross-sectional Data Models. The user interface for the Cross-Sectional Data Models task opens.
  3. On the Data tab, select the WORK.CIGAR data set.
    Tip
    If the data set is not available from the drop-down list, click Select a table icon. In the Choose a Table window, expand the library that contains the data set that you want to use. Select the data set for the example and click OK. The selected data set should now appear in the drop-down list.
  4. Assign columns to these roles:
    Role
    Column Name
    Dependent variable
    sales
    Continuous variables
    price
    cpi
    ndi
    Categorical variables
    state
  5. On the Model tab, select Linear as the model type.
  6. To run the task, click Submit SAS Code Icon.
Here is a subset of the results:
Analysis of Variance and Parameter Estimates

Assigning Data to Roles

To perform an analysis of a linear model, you must assign an input data set. To filter the input data source, click Filter Icon.
You also must assign a variable to the Dependent variable role.
Role
Description
Roles
Dependent variable
specifies the numeric variable for the analysis.
Continuous variables
specifies the independent covariates (regressors) for the regression model. If you do not specify a continuous variable, the task fits a model that contains only an intercept.
Categorical variables
specifies the classification variables. The task generates dummy variables for each level of the categorical variable.
Additional Roles
Group analysis by
enables you to obtain separate analyses of observations for each unique group.
Note: This role is not available if you have a categorical variable.

Setting the Model Options

To create a linear model:
  1. From the Model type drop-down list, select Linear.
  2. Specify the effects for your model.
    You can display the main effects model or create a custom model. To create a custom model, select the Custom Model option, and then click Edit. The Model Effects Builder opens. All continuous variables and categorical variables are listed in the Variables pane.
    • To create a main effect, select the variable in the Variables pane, and then click Add.
    • To create a crossed effect, select the variables in the Variables pane. (You can use Ctrl to select multiple variables.) Then click Cross.
    When you finish, click OK. The effects that you specified now appear on the Model tab.
    Here is an example of model effects on the Model tab.
    price and price*productLine effects

Setting the Options

Option Name
Description
Methods
Covariance matrix estimator
specifies the method to calculate the covariance matrix of parameter estimates.
You can select the default value, or you can choose from these methods:
  • White
  • HCn specifies a heteroscedasticity-corrected covariance matrix. n is a value from 0–3.
Statistics
Select the statistics to display in the results.
Here are the additional statistics that you can include in the results:
  • correlations of the parameter estimates
  • covariances of the parameter estimates
  • heteroscedasticity-consistent standard errors
Plots
Select the plots to include in the results. By default, diagnostic, residual, and fit plots are included in the results. You can include these plots:
  • diagnostic plots, such as residuals for each explanatory variable, R-student statistic by predicted values, and normal quantile plot of the residuals
  • output plots, such as a fit plot for a single continuous variable, a plot of observed values by predicted values, and partial regression plots for each explanatory variable
You can choose to display these plots as a panel of plots or as individual plots.

Creating the Output Data Sets

You can create an output data set that contains the parameter estimates.
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