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Using regression to find correlating factors provides a greater understanding of the associations among factors and the ability to forecast factors that drive demand, revenues, profit, etc.
What it is: Regression analysis involves identifying the relationship between a dependent variable and one or more independent variables. Regression is used to look for associations within data sets, identifying which factors are related. Knowing the relationship helps a manager make business decisions about which factors they could modify to impact revenues and profitability, or they could predict the impact of changes created by the movement of independent factors they do not control. Correlation is the measure of how closely the independent and the dependent variables move together. Perfect correlation is measured as 1 and perfectly inverse relationships are measured as -1.
What it does: Armed with the relationship correlations among factors within a market, industry etc., a manager can predict what will happen as individual factors are impacted or modified. As an example, if you can correlate the relationship between price and demand, you can predict the increased volume demand if the price of a product was reduced by 10%.
How is it used: Correlations are used to understand how different factors interact with a business system and how they impact each other. The correlation can be causal (A causes B to happen) or correlated (When A moves, then B moves the same or the inverse way) or have no relationship whatsoever (when A moves there is no indication of what B will do). Knowing which levers you can pull to impact change can allow a business to make proactive moves. Knowing how the movement of certain trends or factors in the industry and economy will affect your business will allow you to make reactive, proactive, or pre-emptive actions in the marketplace.
Where: Regressions are run to find correlations in many applications in business today. Some examples include the following:
Find the correlation of price versus demand to be able to predict the impact of price changes on volume demand. This same type of correlation can be used to determine the impact of price on market share between competitors.
Forecast product demand growth based on economic variables (e.g., What is the impact of changes in disposable income on automobile sales?).
Forecast product demand based on correlated factors (e.g., What is the impact of housing starts on dishwasher sales since there is an average of .95 dishwashers per housing start?).
Forecast manufacturing efficiencies and costs through correlations of manufacturing data.
Find relationships between certain employee behaviors and success in the company (this could be used in screening potential employees).
Determine inventory order quantities, order points, inventory quantities in storage, safety stocks, length of storage for perishable products, etc.
Determine the factors impacting inventory shrinkage and indicate actions that could be taken to reduce shrinkage.
Identify factors for prospective customers (lead scoring analysis).
Predict which customers will leave (churn analysis).
Identify the factors driving safety failures and predict the impact of new safety procedures in avoiding future accidents.
Find the financial correlations among price, volume, fixed costs, and variable costs in predicting the impact on the profitability of changes to these factors.
Why: Without regression and correlation, insights managers would be making many decisions blind, without knowing how their decisions will impact their business.
Where it shouldn't be used: There are no situations where the use of regression analysis to find correlations should not be used.
Any restrictions: Do not use regression and correlation when the data quality is poor.
Warnings: Regression analysis to find correlations is a science, but knowing which factors to regress and interpreting the results in an art. Therefore, you must interpret the results with care and apply common sense tests to the results and interpretations. It is also best to test the results to see if the predicted outcomes turn out as expected. Regressions are more effective when there is a lot of variation in the variables. There is also the risk of an omitted variable that could have a major impact but was not included in the regression and correlation analysis.
Correlation and Linear Regression (See 3. Analysis Phase, the 7th topic)
Correlation and Regression (See 3. Causation does not imply causation: topic 3.)
Gathering data: Gather the data for the analysis.
Independent variables that will be analyzed
The dependent variables that will be regressed against the independent variables
Analysis of data: Run the regression analysis and look for correlated factors.
Interpretation of results: Determine if any of the dependent variables have a direct or inverse relationship to the independent variable. Try to determine if there is causality or merely correlated responses. Is there a third variable that both the dependent and the independent variables are correlated with?
Presentation of results: Regression analysis is shown as a combination of regression charts that show the scatter plot with the regression line. Then the variability and confidence levels are defined. This demonstrates the predictive elements that drive the recommendations.
Regression analysis is shown as a combination of the regression charts that show the scatter plot with the regression line included. Then the variability and confidence levels are defined. This demonstrates the predictive elements that drive the recommendations.
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Access it online or download it at https://ensign.edtechbooks.org/projectbased_internships/using_regression_to_find_correlation.
