Logistic Regression Using SAS - Indepth Predictive Modeling

Many students are taken aback by the seemingly endless options and voluminous output when they first encounter regression in SAS. Though the LOGISTIC procedure is complex, most problems and confusion can be avoided by approaching your analyses in a systematic manner. Misuse is the most common issue we as SAS instructors face when teaching regression. Unfortunately, many students use regression when simpler analytic methods would suffice, or, more egregiously, rush straight to regression without first performing the necessary preliminary analytics. Though the LOGISTIC procedure is the primary focus of this paper, we want to emphasise that regression should be used as part of a deliberate and well-thought-out analytic plan. Remember that no software or series of equations can think for you.
We generally advise students to create an a priori conceptual model of how the variables in their dataset might interact and affect one another before beginning a logistic regression analysis. After that, descriptive frequencies for each variable should be examined (using the UNIVARIATE, MEANS, and/or FREQ procedures) to determine distribution, coding, and the presence of any potentially influential outliers. What you do with this information will be heavily influenced by the results. Even if you do not make any changes to the dataset before proceeding with your analyses, it is critical that you understand each variable and how it behaves. Higher level analyses should be performed only after you have gathered and interpreted basic descriptive information for your variables.

KEY FEATURE

Predictive modelling can be used to estimate a number of different outcomes, such as the quality of a sales lead, or the likelihood that someone will click on a link or buy a product.

It plays a significant role in predictive analytics, a subfield of data science that looks to the past and present for clues about what the future will bring in terms of behaviour and trends.

The ability to do this is increasingly integrated into many business applications, so it’s important to learn the fundamentals of predictive modelling so you can troubleshoot and optimise results.

Predictive modelling is a form of data mining that analyses historical data with the goal of identifying trends or patterns and then using those insights to predict future outcomes.

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Key Topics

    What Exactly Is Regression?

    Regression is a statistical procedure that attempts to predict the values of a given variable (referred to as the dependent, outcome, or response variable) based on the values of one or more other variables (called independent variables, predictors, or covariates). A regression result is typically an equation (or model) that summarises the relationship between the dependent and independent variables (s). Typically, the model is accompanied by summary statistics that describe how well the model fits the data, the amount of variation in the outcome that the model accounts for, and a basis for comparing the existing model to other similar models.
    By comparing these statistics across multiple models, the user can determine the combination and order of independent variables that best predict the outcome values. To predict the values of a wide range of outcome measures, various types of regression have been developed. Because regression modelling focuses on the response variable, the type of regression you use will be determined by the type of response variable you are analysing as well as your ultimate analytic goal. Due to space constraints, we will confine our discussion to the types of regression most commonly used by our students: linear regression and logistic regression.

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