The term regression is often used in industry, law, medical, and education settings as a way to demonstrate how statistical methods have been used to draw conclusions or provide evidence in support of certain claims. However, many people confuse regression with regression testing and regression with regression analysis.

As statistics professionals, many analysts require the use of more-technical statistical techniques than simple regression analysis or correlation analysis between a single independent factor and an outcome. For example, a brokerage firm seeking to determine the cost of trading NASDAQ shares would likely want more detailed information on how the price of NASDAQ shares is related to the demand for these shares. Regression analysis is a statistical method that attempts to mimic this behavior. In the case of NASDAQ shares, this would include the relationship between the supply and demand for NASDAQ shares and the price of NASDAQ shares.

To understand the relationship between multiple regression and correlation and regression testing, it is important to understand that they are two different methods. Many people confuse correlation and regression analysis, when they consider the relationship between the cost of NASDAQ shares and the demand for NASDAQ shares.

In a statistical process called regression, an effect of a variable on another variable is considered by examining how a known variable varies in tandem with that variable’s effect. For example, the relationship between the demand for NASDAQ shares and NASDAQ share prices can be measured through this statistical process. Once a relationship is determined between the two variables, the regression is used to determine the relationship between the two variables and the effect of the first variable on the second variable, which will show if the first variable is influencing the second variable. However, there are some pitfalls that can come about when relying on regression.

For one, in the case of multiple regression, it is possible to find that a relationship does exist between the variables, but once more variables are controlled the relationship disappears. A simple example of this can be seen by taking a look at the relationship between the price of NASDAQ shares and the demand for NASDAQ shares.

Assuming that the relationship between the price of NASDAQ shares and the demand for NASDAQ shares is a true relationship, it is possible for a firm to find a connection between the relationship between the demand for NASDAQ shares and the price of NASDAQ shares and conclude that the price of NASDAQ shares were influenced by the demand for NASDAQ shares. However, if other factors such as the cost of NASDAQ shares and the demand for NASDAQ shares were controlled, then the relationship between NASDAQ shares and NASDAQ share prices would still disappear.

In addition, when multiple regression is not used, it may be difficult to show a statistically significant relationship between the variable being controlled and the variable of interest. If the controlling variable is the demand for NASDAQ shares, then it is difficult to find a statistically significant relationship between the relationship between NASDAQ share prices and the controlling variable because the relationship may not actually exist between the two variables. In the case of NASDAQ shares, for example, it is impossible to actually find a relationship between the demand for NASDAQ shares and NASDAQ share prices. Therefore, the conclusion reached using regression analysis would fail to reach statistical significance, because the relationship between the two variables was not actually statistically significant.

When a firm uses a statistical technique to make a causal link between variables, regression and correlation testing, the results can be found by using a statistical method known as Regression and Correlation Testing (RCT). RCT is a statistical technique that compares the data from a number of studies (or “studies” in statistics terminology) to see if there is a relationship between a particular variable and the other variables being studied. If the relationship between the variables is found to be statistically significant, then the relationship between the variable and the other variables has been found to be causal.