Statistical Modeling: Concepts and Applications

What are statistical models? If you're interested in business analytics, understanding how statistical models work and how they apply to different industries is key. At a fundamental level, statistical models examine the relationships between variables. They're used in a variety of professional settings to make predictions, forecast outcomes, uncover hidden patterns, and test theories.

Many fields, including healthcare, finance, and marketing, use statistical models to make informed decisions. This post examines key aspects of statistical modeling, including techniques and methods, and how to apply statistical models in real-world settings.

Core Components of Statistical Modeling

At their core, statistical models rely on mathematics to identify relationships between variables and generate predictions.¹

The development of an effective statistical model starts with data collection. Analysts source relevant data from primary or secondary sources, such as internal or external databases, surveys, and experiments. Once collected, analysts must examine the data for inconsistencies or flaws that could affect the model's accuracy. This essential process is often referred to as data cleansing.²

Another core component of statistical modeling is the selection of dependent and independent variables. The dependent variable represents the outcome of interest in the model. Analysts select independent variables to see how they influence the model's behavior.

Additionally, statistical models contain underlying assumptions. For example, a model may assume each data point is independent or that data points are normally distributed. Inaccurate assumptions can affect the reliability of any statistical model.

Common Types of Statistical Models

There are a number of different types of statistical models. Which type of model is best for a given initiative depends on the nature of the problem at hand and the insights you are seeking to gain.

Linear Regression

Commonly used for forecasting, linear regression is perhaps the most well-known statistical model. It assumes there is a linear relationship between the dependent variable and the independent variables.³ For example, an investment analyst could use linear regression to predict the future price of a stock based on changes to specific independent variables.

Logistic Regression

Logistic regression examines the likelihood that an event will happen. It's a binary classification model, with the outcome being either yes or no.³ Results from logistic regression include an estimated probability of the event, which analysts can use to understand how reliable the model is.

Time Series Model

In a time series model, analysts collect data over a specific period to ascertain future behavior. Time series models help predict short-term outcomes, such as sales over the next quarter. They can also help analysts identify patterns in previously collected data, leading to more accurate forecasting.⁴

Survival Analysis

Survival analysis estimates the time until a specific event will take place.⁵ It incorporates various statistical techniques, such as the Kaplan-Meier estimator and the Cox Proportional Hazard Model, to examine the factors that influence survival.⁶ Survival analysis applies to a variety of settings, from predicting how long a washing machine will last to estimating a patient's longevity following a health incident.

Applications Across Industries

Statistical modeling methods have valuable applications in many different sectors.

Finance

In finance, models for data analysis help investment managers optimize portfolios for their clients. Investment managers can use analytics to predict stock prices and make informed buying and selling decisions. Statistical models are also used in the banking and insurance sectors, where they can help organizations make key predictions, such as whether a customer will default on a loan or file a claim.⁷

Healthcare

Healthcare organizations use statistical modeling techniques to make critical predictions, such as the likelihood of a patient developing a disease. Analysts can use a patient's medical history to determine the conditions for which that person carries the greatest risk. This information is highly valuable for doctors, who can use it to customize treatment plans and improve their efficacy.⁸

Marketing

Businesses of all types use statistical models in their marketing strategies. Analytics can help marketers segment customers into groups based on demographic factors, previous brand interactions, buying history, and more.³ Using a model's insights, marketers can focus their efforts on people most likely to convert (from prospective customers into paying customers), rather than spending heavily on initiatives with a low return on investment (ROI).⁹

Science

Across scientific disciplines, statistical models are integral to research. Scientists from all fields use a broad range of models to test hypotheses and predict outcomes.³

Tools and Technologies

Analytics professionals work with a variety of tools when developing statistical models. A foundational knowledge of Python and R programming languages is particularly valuable. Both languages support large-scale data collection, preparation, and manipulation. Some professionals use software like SAS or SPSS, which offer pre-built statistical models and streamlined user interfaces.¹⁰

Cloud-based platforms such as Google Cloud and AWS are beneficial for working with large data sets that require extensive storage. Their integration capabilities make them useful for advanced statistical analysis using machine learning and artificial intelligence (AI).¹⁰

Steps in Building a Statistical Model

Developing a reliable statistical model starts with identifying an objective and establishing hypotheses. Next, you’ll need to determine how you'll source data and what kind of repository you'll use for secure storage. Once you have the data set, you’ll need to examine it for any flaws that could undermine the model's accuracy and reliability.

Next, you'll select the statistical modeling techniques that suit your objective and data set. You’ll then be able to begin testing your model and exploring outcomes.

Finally, you’ll need to interpret the results and communicate them to relevant parties. For large-scale projects, you may write a report or draft a presentation that includes an example of the statistical model and the actual results. Stakeholders can then use the information provided to make data-informed decisions.

Elevate Your Career in Business Analytics

Statistical models are an increasingly important component of data-driven decision-making across industries. Santa Clara University's Online Master of Science in Business Analytics (MSBA) will equip you with the skills you need to build statistical models from the ground up and extract critical insights to improve business outcomes.

Our rigorous curriculum is taught by industry experts and delivered in a flexible online format to support busy working professionals seeking career advancement. Throughout the program, you’ll have the opportunity to engage in hands-on analytics projects and gain experience with a range of key statistical tools, preparing you to take on real-world data challenges with confidence.

Eager to get started? Review our admissions process, schedule an appointment with an admissions outreach advisor, or contact us today to learn more.