Regression Analysis in SAS Tutorial

Introduction

Regression analysis is a statistical technique used to understand the relationship between a dependent variable and one or more independent variables. SAS (Statistical Analysis System) provides comprehensive tools for performing regression analysis, enabling researchers and analysts to model and analyze data effectively. This tutorial will guide you through the process of regression analysis in SAS, including examples of commands or code and detailed steps.

Performing Regression Analysis in SAS

To perform regression analysis in SAS, follow these steps:

Step 1: Prepare the Data

Ensure that your dataset is properly prepared for regression analysis. Cleanse the data, handle missing values, and select the appropriate variables for analysis.

Step 2: Choose the Regression Procedure

Select the appropriate regression procedure in SAS based on your requirements. SAS provides several procedures for regression analysis, such as PROC REG, PROC GLM, and PROC LOGISTIC, among others. Choose the procedure that best suits your data and research question.

Step 3: Specify the Model

Specify the regression model you want to build. Define the dependent variable and the independent variables, and specify any transformations or interactions needed. You can use SAS syntax to define the model in the selected procedure.

Step 4: Run the Regression Analysis

Run the SAS program to perform the regression analysis. SAS will estimate the model parameters, calculate statistical measures, and generate output containing the results, such as coefficients, p-values, and goodness-of-fit measures.

Step 5: Interpret and Validate the Results

Interpret the results obtained from the regression analysis. Examine the coefficients to understand the direction and significance of the relationships between variables. Validate the model using diagnostic measures and assess its goodness-of-fit. Make inferences and draw conclusions based on the results.

Here's an example of SAS code to perform a linear regression analysis:

Common Mistakes in Regression Analysis with SAS

• Including irrelevant or highly correlated variables in the model, leading to multicollinearity issues.
• Not checking the assumptions of regression analysis, such as linearity, independence, and homoscedasticity.
• Ignoring outliers or influential observations that can affect the regression model's results.
• Interpreting association as causation without considering potential confounding variables.
• Using a single model without considering alternative models or performing model selection techniques.

FAQs about Regression Analysis in SAS

1. Can I perform regression analysis with categorical variables?

   Yes, SAS allows for regression analysis with categorical variables. You can use techniques such as dummy coding, effect coding, or the CLASS statement in SAS procedures to include categorical variables in regression models.

2. How can I handle missing values in regression analysis?

   SAS provides various methods to handle missing values in regression analysis. You can use techniques such as listwise deletion, mean imputation, or multiple imputation to address missing data before performing the regression analysis.

3. What are the measures of model fit in regression analysis?

   Common measures of model fit in regression analysis include the coefficient of determination (R-squared), adjusted R-squared, Akaike Information Criterion (AIC), and Bayesian Information Criterion (BIC). These measures assess how well the model fits the data and the trade-off between model complexity and goodness-of-fit.

4. Can I perform regression analysis with non-linear relationships?

   Yes, SAS allows for regression analysis with non-linear relationships by using non-linear regression models or by transforming variables to achieve linearity. SAS provides procedures such as PROC NLIN or options like the TRANSREG statement in the PROC REG procedure for non-linear regression analysis.

5. Can I perform regression analysis with time series data?

   Yes, SAS provides specialized procedures like PROC ARIMA or PROC UCM to perform regression analysis on time series data. These procedures account for the autocorrelation and time dependencies present in the data.

Summary

Regression analysis is a powerful statistical technique used to model and analyze relationships between variables. SAS provides a wide range of procedures and tools to perform regression analysis effectively. This tutorial covered the steps involved in performing regression analysis in SAS, including model specification, interpretation of results, and validation. By following these steps and considering the common mistakes to avoid, you can conduct regression analysis in SAS with confidence, gaining valuable insights from your data.