Using PROC MEANS for Descriptive Statistics in SAS

Introduction

PROC MEANS is one of the most commonly used procedures in SAS, enabling users to calculate basic descriptive statistics like mean, median, standard deviation, minimum, and maximum values. Whether you are new to SAS or an experienced data analyst, understanding how to leverage PROC MEANS can significantly improve the efficiency of your data analysis. In this article, we’ll explore how to use PROC MEANS effectively, including various options, syntax, and practical applications to derive meaningful insights from data.

What is PROC MEANS?

PROC MEANS is a statistical procedure in SAS that allows users to calculate descriptive statistics for numeric variables in a dataset. It is versatile and provides a quick summary of key statistical values, which can be beneficial for exploratory data analysis (EDA). By using PROC MEANS, data analysts can gain insights into data distribution and variability, making it easier to detect patterns and anomalies.

Why Use PROC MEANS for Descriptive Statistics?

PROC MEANS is favored by SAS professionals for several reasons:

• Efficiency: It performs calculations quickly, even with large datasets.
• Flexibility: It allows custom output options, making it suitable for diverse data analysis tasks.
• Compatibility: It integrates seamlessly with other SAS procedures, facilitating complex analyses.

Using PROC MEANS allows you to generate essential descriptive statistics with minimal code, saving time and reducing the complexity of your data analysis workflow.

Basic Syntax of PROC MEANS

To get started with PROC MEANS, let’s look at the basic syntax structure. Here’s a simple example:

PROC MEANS DATA=dataset_name;
    VAR variable1 variable2;
RUN;

• DATA=dataset_name: Specifies the dataset you want to analyze.
• VAR: Specifies the variables for which you want to calculate descriptive statistics.

Key Statistics Calculated by PROC MEANS

With PROC MEANS, you can obtain various statistics, such as:

• Mean (average)
• Median
• Standard deviation
• Minimum and maximum values
• Range

These statistics can help you understand data patterns, identify outliers, and make informed decisions about your analysis.

Example: Calculating Descriptive Statistics Using PROC MEANS

Let’s walk through a simple example using a fictional dataset named sales_data, which contains monthly sales figures for different products.

PROC MEANS DATA=sales_data;
    VAR sales_amount;
RUN;

In this example, PROC MEANS calculates the mean, median, standard deviation, minimum, and maximum values for sales_amount. This output provides a quick summary of the sales data, allowing you to assess sales trends.

Customizing PROC MEANS Output

PROC MEANS provides several options for customizing your output. Here are a few commonly used options:

1. Specifying Specific Statistics

You can specify the statistics you want by using the following syntax:

PROC MEANS DATA=sales_data N MEAN MEDIAN STD MIN MAX;
    VAR sales_amount;
RUN;

In this example, only the count (N), mean, median, standard deviation, minimum, and maximum values are displayed.

2. Grouping by Class Variables

PROC MEANS allows grouping by categorical variables using the CLASS statement, which is helpful for comparing statistics across different groups.

PROC MEANS DATA=sales_data;
    CLASS product_category;
    VAR sales_amount;
RUN;

This example provides statistics for each product category, enabling comparisons across different groups.

3. Outputting Results to a New Dataset

To save the results of PROC MEANS to a new dataset, use the OUTPUT OUT= option:

PROC MEANS DATA=sales_data N MEAN MEDIAN STD MIN MAX;
    VAR sales_amount;
    OUTPUT OUT=summary_data;
RUN;

Here, the statistics are stored in a new dataset called summary_data, which can be further analyzed or used in other procedures.

Common Options in PROC MEANS

PROC MEANS offers several options to enhance data analysis flexibility:

• MAXDEC=n: Limits the number of decimal places displayed in the output.
• CHARTYPE: Displays the CLASS variable values in character format.
• MISSING: Includes missing values in the analysis.

Here’s an example that uses some of these options:

PROC MEANS DATA=sales_data MAXDEC=2 MISSING;
    CLASS region;
    VAR sales_amount;
RUN;

Using PROC MEANS with BY Statements

The BY statement allows you to produce separate analyses for each level of a variable. However, to use the BY statement, you need to sort the dataset first.

PROC SORT DATA=sales_data;
    BY region;
RUN;

PROC MEANS DATA=sales_data;
    BY region;
    VAR sales_amount;
RUN;

This approach generates separate descriptive statistics for each region, providing a detailed view of sales across different areas.

Comparing PROC MEANS and PROC SUMMARY

PROC SUMMARY is similar to PROC MEANS and can be used interchangeably with it. However, PROC SUMMARY produces no default printed output unless a PRINT statement is included. PROC MEANS is generally more convenient for quick summaries, while PROC SUMMARY is ideal for creating custom summary datasets.

PROC SUMMARY DATA=sales_data PRINT;
    VAR sales_amount;
    OUTPUT OUT=summary_stats MEAN=average_sales;
RUN;

Practical Applications of PROC MEANS in Data Analysis

1. Quality Control in Manufacturing

PROC MEANS can help manufacturing firms monitor the consistency of production metrics, such as weight or dimensions, and identify any deviations from quality standards.

2. Sales Analysis

Sales teams often use PROC MEANS to assess revenue data, understand product performance, and make strategic adjustments based on sales trends.

3. Healthcare Data Analysis

PROC MEANS is useful for analyzing healthcare data, such as patient demographics and clinical metrics, providing insights that drive decision-making.

External Resources for PROC MEANS and SAS

For more information on PROC MEANS, check out these resources:

• SAS Documentation for PROC MEANS
• Tutorial on Descriptive Statistics in SAS

FAQs

1. What is PROC MEANS used for in SAS?
   PROC MEANS calculates descriptive statistics for numeric data in SAS, including mean, median, and standard deviation.
2. Can I use PROC MEANS for categorical data?
   PROC MEANS is designed for numeric data. For categorical data analysis, PROC FREQ is more appropriate.
3. How do I save PROC MEANS output to a new dataset?
   Use the OUTPUT OUT= option to save the results to a new dataset.
4. Is PROC MEANS faster than other procedures in SAS?
   PROC MEANS is optimized for speed, making it efficient for large datasets.
5. What’s the difference between PROC MEANS and PROC SUMMARY?
   PROC SUMMARY requires a PRINT statement for output, whereas PROC MEANS generates printed results by default.
6. Can PROC MEANS handle missing values?
   Yes, PROC MEANS can include missing values in calculations with the MISSING option.
7. How do I limit decimal places in PROC MEANS output?
   Use the MAXDEC option to specify the number of decimal places.
8. Can I calculate weighted averages with PROC MEANS?
   Yes, the WEIGHT statement allows for weighted calculations.
9. How do I run PROC MEANS by groups?
   Use the CLASS or BY statement to group data in PROC MEANS.
10. Is there a way to compare multiple variables in PROC MEANS?
    Yes, specify multiple variables in the VAR statement to get statistics for each.

Using PROC MEANS for descriptive statistics in SAS is a practical skill that can enhance your data analysis capabilities. Whether you are examining sales data, conducting quality control, or analyzing healthcare metrics, mastering PROC MEANS enables you to generate meaningful insights quickly and accurately. With its customizable options and flexibility, PROC MEANS remains a valuable tool for SAS professionals.