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Regression is a type of analysis that uses statistical methods in an attempt to explain and understand the relationships between the independent variable or variables (also called predictor variables or explanatory variables) and a dependent variable (also known as the outcome variable). Most often, regression models also consider the error terms, which measures the margin of error within an analysis to note deviations. There are two main reasons regression analyses are run, either to make predictions or to look for causal relationships. In order to achieve such goals, there is more than one type of regression analyses, such as linear regression, simple linear regression, multiple regression, and nonlinear regression. Most of these regression methods use machine learning to improve and speed up their analysis. It is important to choose the most relevant statistical modelling method depending on the outcome if interest, though multiple models can be run on the same data set to find the most relevant method.

Linear models are generally used to plot data sets on a graph, with a line drawn through the highest concentration of data points. A linear regression model will have a straight line drawn across the model. When looking from left to right, a line that trends upward shows a positive correlation, and a line that trends downward will show a negative correlation. A simple linear regression will have the same types of output, but will look at only one independent variable, whereas a multiple regression will have more than one independent variable. A nonlinear regression is when a straight line cannot be drawn through the congregated data points. Nonlinear regressions use a best fit curve to map the relationships between the random variable (the independent variable) and the response variable (the dependent variable). Non-linear models often do not show a clear causal relationship. Different linear and non-linear regression models can clearly show causal or noncausal relationships between dependent and independent variables. They can also be used to predict future outcomes based on data from points in the past.

Regression testing can be used by businesses and organizations in a variety of ways, including:

  • Optimization of budgets: By looking at the ways different departments have utilized their budgets, and the results from within each department, businesses can anticipate future trends and adjust spending accordingly. This ensures that the business can optimize their budget in such a way as to ensure that high performing departments are fully supported.

  • Improved business insights: Businesses can use regression analysis to identify a multitude of trends. For example, they can identify how sales change based on the season, or which marketing strategies have proved the most successful. This will help them plan how to best optimize tasks based on existing data.

  • Analyze market trends: In a larger sense, companies can also look at data outside of their business to identify potential trends and pitfalls. By looking at where other companies are succeeding or falling behind, businesses can determine areas of opportunity that will help them to better serve the needs of their customers and thereby increase market share.
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