Common Statistical Procedures used in SAS to Analyze Data

There are several common statistical procedures used in SAS to analyze data. Some of the most widely used procedures include:

1. Descriptive Statistics: Descriptive statistics are used to summarize and describe the important characteristics of a dataset, such as mean, median, standard deviation, and range. This procedure helps researchers understand the basic patterns and distributions in their data.
2. Hypothesis Testing: Hypothesis testing is used to determine whether there is a significant difference between two or more groups or variables. This procedure helps researchers make inferences about the population based on sample data.
3. Linear Regression: Linear regression is used to model the relationship between a dependent variable and one or more independent variables. This procedure helps researchers understand how changes in the independent variables impact the dependent variable.
4. Logistic Regression: Logistic regression is used when the dependent variable is binary or categorical. This procedure helps researchers predict the probability of an event occurring based on the independent variables.
5. Cluster Analysis: Cluster analysis is used to group data points into clusters based on similarity. This procedure helps researchers identify patterns and relationships within the data.

Each of these statistical procedures in SAS serve different purposes and offer unique benefits for analyzing data in research and business contexts. By using these procedures effectively, researchers can gain valuable insights and make informed decisions based on data-driven evidence.