A frequent topic on SAS discussion forums is how to check the assumptions of an ordinary least squares linear regression model. Some posts indicate misconceptions about the assumptions of linear regression. In particular, I see incorrect statements such as the following: Help! A histogram of my variables shows that they

## Tag: **Getting Started**

A SAS programmer recently asked how to interpret the "standardized regression coefficients" as computed by the STB option on the MODEL statement in PROC REG and other SAS regression procedures. The SAS documentation for the STB option states, "a standardized regression coefficient is computed by dividing a parameter estimate by

In SAS, the reserved keyword _NULL_ specifies a SAS data set that has no observations and no variables. When you specify _NULL_ as the name of an output data set, the output is not written. The _NULL_ data set is often used when you want to execute DATA step code

The SAS language provides syntax that enables you to quickly specify a list of variables. SAS statements that accept variable lists include the KEEP and DROP statements, the ARRAY statement, and the OF operator for comma-separated arguments to some functions. You can also use variable lists on the VAR statements

In a recent blog post, Chris Hemedinger used a scatter plot to show the result of 100 coin tosses. Chris arranged the 100 results in a 10 x 10 grid, where the first 10 results were shown on the first row, the second 10 were shown on the second row, and so

The sweep operator performs elementary row operations on a system of linear equations. The sweep operator enables you to build regression models by "sweeping in" or "sweeping out" particular rows of the X`X matrix. As you do so, the estimates for the regression coefficients, the error sum of squares, and

As a general rule, when SAS programmers want to manipulate data row by row, they reach for the SAS DATA step. When the computation requires column statistics, the SQL procedure is also useful. When both row and column operations are required, the SAS/IML language is a powerful addition to a

My article about the difference between CLASS variables and BY variables in SAS focused on SAS analytical procedures. However, the BY statement is also useful in the SAS DATA step where it is used to merge data sets and to analyze data at the group level. When you use the

When I first learned to program in SAS, I remember being confused about the difference between CLASS statements and BY statements. A novice SAS programmer recently asked when to use one instead of the other, so this article explains the difference between the CLASS statement and BY variables in SAS

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When someone refers to the correlation between two variables, they are probably referring to the Pearson correlation, which is the standard statistic that is taught in elementary statistics courses. Elementary courses do not usually mention that there are other measures of correlation. Why would anyone want a different estimate of

I have previously discussed how to define functions that safely evaluate their arguments and return a missing value if the argument is not in the domain of the function. The canonical example is the LOG function, which is defined only for positive arguments. For example, to evaluate the LOG function

Many intervals in statistics have the form p ± δ, where p is a point estimate and δ is the radius (or half-width) of the interval. (For example, many two-sided confidence intervals have this form, where δ is proportional to the standard error.) Many years ago I wrote an article

SAS programmers who have experience with other programming languages sometimes wonder whether the SAS language supports statements that are equivalent to the "break" and "continue" statements in other languages. The answer is yes. The LEAVE statement in the SAS DATA step is equivalent to the "break" statement. It provides a

A common question on SAS discussion forums is how to repeat an analysis multiple times. Most programmers know that the most efficient way to analyze one model across many subsets of the data (perhaps each country or each state) is to sort the data and use a BY statement to

If you're a Canvas customer who’d like to use our over 1,700 tools and lessons, you can start by simply adding Curriculum Pathways as an app. Creating Assignments using Curriculum Pathways resources Once the app is set up, teachers can get started creating new assignments with Curriculum Pathways resources. Using either the

In the beginning SAS created procedures and output. The output was formless and void. Then SAS said, "Let there be ODS," and there was ODS. Customers saw that ODS was good, and SAS separated the computation from the display and management of output. The preceding paragraph oversimplifies the SAS Output

In some applications, you need to optimize a linear objective function of many variables, subject to linear constraints. Solving this problem is called linear programming or linear optimization. This article shows two ways to solve linear programming problems in SAS: You can use the OPTMODEL procedure in SAS/OR software or

SAS formats are flexible, dynamic, and have many uses. For example, you can use formats to count missing values and to change the order of a categorical variable in a table or plot. Did you know that you can also use SAS formats to recode a variable or to bin

Last week I read an interesting paper by Bob Rodriguez: "Statistical Model Building for Large, Complex Data: Five New Directions in SAS/STAT Software." In it, Rodriguez summarizes five modern techniques for building predictive models and highlights recent SAS/STAT procedures that implement those techniques. The paper discusses the following high-performance (HP)

I'm addicted to you. You're a hard habit to break. Such a hard habit to break. — Chicago, "Hard Habit To Break" Habits are hard to break. For more than 20 years I've been putting semicolons at the end of programming statements in SAS, C/C++, and Java/Javascript. But lately I've been

Two of my favorite string-manipulation functions in the SAS DATA step are the COUNTW function and the SCAN function. The COUNTW function counts the number of words in a long string of text. Here "word" means a substring that is delimited by special characters, such as a space character, a

Every beginning SAS programmer learns the simple IF-THEN/ELSE statement for conditional processing in the SAS DATA step. The basic If-THEN statement handles two cases: if a condition is true, the program does one thing, otherwise the program does something else. Of course, you can handle more cases by using multiple

A grid is a set of evenly spaced points. You can use SAS to create a grid of points on an interval, in a rectangular region in the plane, or even in higher-dimensional regions like the parallelepiped shown at the left, which is generated by three vectors. You can use

In a previous post I showed how to download, install, and use packages in SAS/IML 14.1. SAS/IML packages incorporate source files, documentation, data sets, and sample programs into a ZIP file. The PACKAGE statement enables you to install, uninstall, and manage packages. You can load functions and data into your

Descriptive univariate statistics are the foundation of data analysis. Before you create a statistical model for new data, you should examine descriptive univariate statistics such as the mean, standard deviation, quantiles, and the number of nonmissing observations. In SAS, there is an easy way to create a data set that

A dummy variable (also known as indicator variable) is a numeric variable that indicates the presence or absence of some level of a categorical variable. The word "dummy" does not imply that these variables are not smart. Rather, dummy variables serve as a substitute or a proxy for a categorical

In the SAS/IML language, you can read data from a SAS data set into a set of vectors (each with their own name) or into a single matrix. Beginning programmers might wonder about the advantages of each approach. When should you read data into vectors? When should you read data

Novice SAS programmers quickly learn the advantages of using PROC SORT to sort data, followed by a BY-group analysis of the sorted data. A typical example is to analyze demographic data by state or by ZIP code. A BY statement enables you to produce multiple analyses from a single procedure