Digital Themes

Linear Regression

Linear regression is an approach to modeling the relationship between a scale and one or more variables to track correlation. Linear regressions can be used to analyze dependent or independent variables. Dependent variables are data points that are believed to depend in some way on the other variables. On the other hand, independent variables (also known as predictor variables) are data points that have no preconceived dependence on the other data in the experiment. Linear regression models include simple linear regression and multiple linear regression.  

Simple linear regressions use a single explanatory variable, in other words a two-dimensional plot with one predictor variable and one dependent variable. Simple linear regressions can be used to clearly identify the correlation or the lack thereof between the two variables. Multiple linear regression equations, on the other hand, is when there is more than one explanatory variable being tested.

Linear regressions are used to create graphs with plots for data points and a line drawn through the graph that best fits the data, called a regression line. Regression lines are analyzed based on the direction when looking at the chart from left to right. A regression line that trends upward shows a positive correlation between the data, and a regression line that trends downward shows a negative correlation. The shape of the line also helps to identify the correlation, as a straight line will show a linear relationship between the data, while a curved line will show that the points do not follow a linear trend.

Oftentimes a linear regression analysis will be performed using machine learning (ML) to get results more quickly. ML allows for much larger data sets to be examined in a reasonable amount of time. In fact, ML can perform linear regression equations in real time as data is ingested from the real world. Furthermore, machine learning helps to limit the errors in models, as machines can make more accurate predictions on larger sets of data than humans can.

Linear regressions powered by machine learning can be used by businesses and organizations to:

  • Analyze marketing efficiencies: By creating a linear regression model that looks at marketing data points and compares it to revenue, businesses can figure out which marketing techniques have the highest correlation with increased revenue. That way businesses can focus on advertising that has been shown to increase sales.

  • Optimize budgets: Organizations can identify what departments’ outputs are in comparison to their budget in order to identify where spending can increase or decrease. It can also be used to identify the return on investment over a period of time for previous budget expenses and make informed decisions about how to modify spending in response.

  • Perform predictive analytics: By looking at data from past events, machine learning can make predictions about future events. For example, businesses can look at how many customers entered their shop on previous Mondays and then make an educated guess around how many will enter the next Monday. This allows businesses to identify scheduling or sales opportunities in response.

  • Gain new insights: Because machine learning can process large data sets with relative ease, businesses and organizations can utilize any of their stored data and compare different data points to see if there is any correlation that had previously gone unnoticed. For example, businesses may learn that more people interact with their application during football games, and can then ensure that there is enough available computing space available during future football games.
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