{"id":17830,"date":"2022-06-07T06:32:00","date_gmt":"2022-06-07T10:32:00","guid":{"rendered":"https:\/\/poole.ncsu.edu\/thought-leadership\/article\/beating-analysis-paralysis-with-regression-analysis\/"},"modified":"2026-05-17T14:09:04","modified_gmt":"2026-05-17T18:09:04","slug":"beating-analysis-paralysis-with-regression-analysis","status":"publish","type":"post","link":"https:\/\/poole.ncsu.edu\/thought-leadership\/article\/beating-analysis-paralysis-with-regression-analysis\/","title":{"rendered":"Beating Analysis Paralysis With Regression Analysis"},"content":{"rendered":"\n

By Samantha Beavers<\/em><\/p>\n\n\n\n

From real estate firms to fast food restaurants, organizations worldwide are leveraging predictive analytics to inform their strategic decisions \u2013 and smaller businesses, seeing its promise, also want in. With a variety of techniques and models to choose from, how can businesses get their analytics efforts off the ground?<\/p>\n\n\n\n

According to Basiru Usman<\/a>, teaching assistant professor for Poole College of Management\u2019s department of business management, regression analysis is a good technique to start with.<\/p>\n\n\n\n

A statistical method that determines the relationship between variables, regression analysis is one of the most commonly used predictive modeling techniques. With various types, it has a wide variety of uses \u2013 which is one of the reasons Usman enjoys teaching it to Poole College\u2019s Master of Management, Marketing Analytics<\/a> students.<\/p>\n\n\n\n

Applications in the real world<\/h3>\n\n\n\n

Whether they realize it or not, most people have encountered regression analysis before, Usman explains. Anyone who\u2019s applied for a credit card online has run up against it.<\/p>\n\n\n\n

\u201cA credit card application asks the applicant a series of questions \u2013 like whether they own a house or rent one, what kind of payments they have and so on. Using that information, the credit card company will classify the applicant and determine whether they\u2019re qualified or not. What\u2019s happening behind the screen is a process called logistic regression,\u201d Usman says.<\/p>\n\n\n\n

Often used for application approvals that require binary classification, logistic regression models \u2013 which have an S-shape \u2013 set a threshold around parameters. A value above that threshold will determine one outcome while a value below it will determine another.<\/p>\n\n\n\n

\u201cThis same process applies to mortgage applications, as well. What I always tell students is that this analytics technique isn\u2019t just about the classroom or the office \u2013 it\u2019s about their life. Understanding how it works will, in some ways, give them the upper hand,\u201d Usman says. \u201cArmed with this information, they can know when they\u2019re in a good spot to apply for a mortgage, for example.\u201d<\/p>\n\n\n\n

Data-driven car valuations<\/h3>\n\n\n\n

Regression models are also behind the used car valuations issued by companies like Kelley Blue Book. Using predictor variables such as the car\u2019s age, Usman says, these companies can determine the car\u2019s price \u2013 or response variable.<\/p>\n\n\n\n

In a simple linear regression, he explains, the car\u2019s value would depreciate by the same amount each year \u2013 with the constant relationship between the variables giving the model a straight line. In real life, however, the relationship between these variables isn\u2019t so obvious. Typically the relationship is nonlinear \u2013 with cars seeing the sharpest depreciation in value after the first year and a smaller depreciation after that. <\/p>\n\n\n\n

Leveraging linear regression<\/h3>\n\n\n\n

In addition to teaching students linear regression with real-world examples, Usman is passionate about helping businesses leverage the technique to make smarter, more strategic decisions. Whether it\u2019s understanding how much inventory to carry or minimizing their marketing dollars, he says, linear regression models can help.<\/p>\n\n\n\n

With simple regression analysis, Usman explains, businesses can model the demand for their product based on price fluctuations in order to supply the right amount.<\/p>\n\n\n\n

\u201cWhat you need to do first is collect your data. Note how much product you sold when the price was $10 and the number of sales when it was $12.50 and $15. Then, once you\u2019ve collected your data, just fit a simple line,\u201d he says. \u201cUsing that historical data, you can determine that your supply should be in a particular range when it\u2019s marked at a certain price.\u201d<\/p>\n\n\n\n

And with a logistic regression model, businesses can target the right customers for marketing campaigns.<\/p>\n\n\n\n

\u201cLet\u2019s say you have a contact list and you\u2019re trying to determine which of them to include in your campaign. You want to minimize your marketing costs by not targeting people who are unlikely to purchase, but you also want to maximize your profits by reaching as many potential customers as possible,\u201d Usman explains.<\/p>\n\n\n\n

\u201cWhat you can do is make use of your historical data \u2013 like the age of the customers, their locations and what they normally buy. Setting a threshold, you can classify which customers are likely to respond and which ones aren\u2019t \u2013 and then plan your campaign accordingly,\u201d he continues.<\/p>\n\n\n\n

Getting started <\/h3>\n\n\n\n

For businesses hesitant about getting started with regression analysis, Usman offers an important reminder: it doesn\u2019t have to be complicated.<\/p>\n\n\n\n

\u201cRegression analysis can be as simple as you want it to be \u2013 and you don\u2019t have to have sophisticated knowledge before you can use it. So if you\u2019re a business wanting to use data to make improvements, there\u2019s good news \u2013 this isn\u2019t far from you,\u201d he says.<\/p>\n\n\n\n

All they need, Usman explains, is some clean, historical data, a business problem they\u2019re trying to solve and access to Excel. And for businesses getting started with collecting data, he says, less is more.<\/p>\n\n\n\n

\u201cKeep your data records as clean and consistent as possible. Make sure that you\u2019re tracking the same variables consistently over time and don\u2019t get distracted by trying to do too much. If some of your inputs have customer arrival time and some don\u2019t, but include extra information like customer reviews, your data won\u2019t be usable,\u201d he explains.<\/p>\n\n\n\n

\u201cStart small and make it clean. Put your customer arrival times in one place and track them consistently, and then you can track your customer reviews in another place. Don\u2019t try to mix them together or you\u2019ll have to do extra work,\u201d he continues.<\/p>\n\n\n\n

As long as they do that, Usman explains, businesses can have a regression model up and running in no time.<\/p>\n\n\n\n

\u201cIt\u2019s as simple as four clicks in Excel,\u201d he says. \u201cThat\u2019s all it takes to take your business to the next level.\u201d<\/p>\n

This post was originally published<\/a> in Master of Management Marketing Analytics.<\/em><\/p>","protected":false,"raw":"\n

By Samantha Beavers<\/em><\/p>\n\n\n\n

From real estate firms to fast food restaurants, organizations worldwide are leveraging predictive analytics to inform their strategic decisions \u2013 and smaller businesses, seeing its promise, also want in. With a variety of techniques and models to choose from, how can businesses get their analytics efforts off the ground?<\/p>\n\n\n\n

According to Basiru Usman<\/a>, teaching assistant professor for Poole College of Management\u2019s department of business management, regression analysis is a good technique to start with.<\/p>\n\n\n\n

A statistical method that determines the relationship between variables, regression analysis is one of the most commonly used predictive modeling techniques. With various types, it has a wide variety of uses \u2013 which is one of the reasons Usman enjoys teaching it to Poole College\u2019s Master of Management, Marketing Analytics<\/a> students.<\/p>\n\n\n\n

Applications in the real world<\/h3>\n\n\n\n

Whether they realize it or not, most people have encountered regression analysis before, Usman explains. Anyone who\u2019s applied for a credit card online has run up against it.<\/p>\n\n\n\n

\u201cA credit card application asks the applicant a series of questions \u2013 like whether they own a house or rent one, what kind of payments they have and so on. Using that information, the credit card company will classify the applicant and determine whether they\u2019re qualified or not. What\u2019s happening behind the screen is a process called logistic regression,\u201d Usman says.<\/p>\n\n\n\n

Often used for application approvals that require binary classification, logistic regression models \u2013 which have an S-shape \u2013 set a threshold around parameters. A value above that threshold will determine one outcome while a value below it will determine another.<\/p>\n\n\n\n

\u201cThis same process applies to mortgage applications, as well. What I always tell students is that this analytics technique isn\u2019t just about the classroom or the office \u2013 it\u2019s about their life. Understanding how it works will, in some ways, give them the upper hand,\u201d Usman says. \u201cArmed with this information, they can know when they\u2019re in a good spot to apply for a mortgage, for example.\u201d<\/p>\n\n\n\n

Data-driven car valuations<\/h3>\n\n\n\n

Regression models are also behind the used car valuations issued by companies like Kelley Blue Book. Using predictor variables such as the car\u2019s age, Usman says, these companies can determine the car\u2019s price \u2013 or response variable.<\/p>\n\n\n\n

In a simple linear regression, he explains, the car\u2019s value would depreciate by the same amount each year \u2013 with the constant relationship between the variables giving the model a straight line. In real life, however, the relationship between these variables isn\u2019t so obvious. Typically the relationship is nonlinear \u2013 with cars seeing the sharpest depreciation in value after the first year and a smaller depreciation after that. <\/p>\n\n\n\n

Leveraging linear regression<\/h3>\n\n\n\n

In addition to teaching students linear regression with real-world examples, Usman is passionate about helping businesses leverage the technique to make smarter, more strategic decisions. Whether it\u2019s understanding how much inventory to carry or minimizing their marketing dollars, he says, linear regression models can help.<\/p>\n\n\n\n

With simple regression analysis, Usman explains, businesses can model the demand for their product based on price fluctuations in order to supply the right amount.<\/p>\n\n\n\n

\u201cWhat you need to do first is collect your data. Note how much product you sold when the price was $10 and the number of sales when it was $12.50 and $15. Then, once you\u2019ve collected your data, just fit a simple line,\u201d he says. \u201cUsing that historical data, you can determine that your supply should be in a particular range when it\u2019s marked at a certain price.\u201d<\/p>\n\n\n\n

And with a logistic regression model, businesses can target the right customers for marketing campaigns.<\/p>\n\n\n\n

\u201cLet\u2019s say you have a contact list and you\u2019re trying to determine which of them to include in your campaign. You want to minimize your marketing costs by not targeting people who are unlikely to purchase, but you also want to maximize your profits by reaching as many potential customers as possible,\u201d Usman explains.<\/p>\n\n\n\n

\u201cWhat you can do is make use of your historical data \u2013 like the age of the customers, their locations and what they normally buy. Setting a threshold, you can classify which customers are likely to respond and which ones aren\u2019t \u2013 and then plan your campaign accordingly,\u201d he continues.<\/p>\n\n\n\n

Getting started <\/h3>\n\n\n\n

For businesses hesitant about getting started with regression analysis, Usman offers an important reminder: it doesn\u2019t have to be complicated.<\/p>\n\n\n\n

\u201cRegression analysis can be as simple as you want it to be \u2013 and you don\u2019t have to have sophisticated knowledge before you can use it. So if you\u2019re a business wanting to use data to make improvements, there\u2019s good news \u2013 this isn\u2019t far from you,\u201d he says.<\/p>\n\n\n\n

All they need, Usman explains, is some clean, historical data, a business problem they\u2019re trying to solve and access to Excel. And for businesses getting started with collecting data, he says, less is more.<\/p>\n\n\n\n

\u201cKeep your data records as clean and consistent as possible. Make sure that you\u2019re tracking the same variables consistently over time and don\u2019t get distracted by trying to do too much. If some of your inputs have customer arrival time and some don\u2019t, but include extra information like customer reviews, your data won\u2019t be usable,\u201d he explains.<\/p>\n\n\n\n

\u201cStart small and make it clean. Put your customer arrival times in one place and track them consistently, and then you can track your customer reviews in another place. Don\u2019t try to mix them together or you\u2019ll have to do extra work,\u201d he continues.<\/p>\n\n\n\n

As long as they do that, Usman explains, businesses can have a regression model up and running in no time.<\/p>\n\n\n\n

\u201cIt\u2019s as simple as four clicks in Excel,\u201d he says. \u201cThat\u2019s all it takes to take your business to the next level.\u201d<\/p>\n"},"excerpt":{"rendered":"

In addition to helping businesses make critical decisions with things like inventory management and customer acquisition, simple regression analysis techniques can help inform everyday decisions, like whether or not your mortgage application is approved, says assistant professor Basiru Usman.<\/p>\n","protected":false},"author":732,"featured_media":17831,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"source":"ncstate_wire","ncst_custom_author":"","ncst_show_custom_author":false,"ncst_dynamicHeaderBlockName":"","ncst_dynamicHeaderData":"","ncst_content_audit_freq":"","ncst_content_audit_date":"","ncst_content_audit_display":false,"ncst_backToTopFlag":"","footnotes":""},"categories":[143],"tags":[166,265],"_ncst_magazine_issue":[],"series":[],"class_list":["post-17830","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-business-analytics","tag-_from-newswire-collection-259","tag-usman"],"displayCategory":null,"acf":{"ncst_posts_meta_modified_date":null},"_links":{"self":[{"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/posts\/17830","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/users\/732"}],"replies":[{"embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/comments?post=17830"}],"version-history":[{"count":7,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/posts\/17830\/revisions"}],"predecessor-version":[{"id":33482,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/posts\/17830\/revisions\/33482"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/media\/17831"}],"wp:attachment":[{"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/media?parent=17830"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/categories?post=17830"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/tags?post=17830"},{"taxonomy":"_ncst_magazine_issue","embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/_ncst_magazine_issue?post=17830"},{"taxonomy":"series","embeddable":true,"href":"https:\/\/poole.ncsu.edu\/thought-leadership\/wp-json\/wp\/v2\/series?post=17830"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}