What Method of Forecasting Uses a Cause & Effect Relationship to Predict?

by Hunkar Ozyasar

When predicting a critical variable, such as the quantity of product that will be sold over a specific time span, the business manager can use various approaches. Methods that use cause and effect relationships are especially useful, as they help predict future trends and grasp underlying business dynamics. Thanks to affordable computers and software programs, such analysis is accessible to even the smallest business.

Regression Analysis

When intending to uncover cause and effect, statisticians rely on regression analysis. This mathematical model uses past data to quantify the relationships among variables. By understanding how one or several variables impact another variable, it is possible to understand the key drivers of a business, such as the factors affecting the number of walk-ins to a supermarket. Regression analysis also allows the statistician to run scenario analysis and produce “best,” “worst” and “likely case” predictions. Especially for the small-business owner who must constantly balance the cash flow, such scenarios help her prepare for potential hardship ahead of time.

Dependent and Independent Variables

To perform a regression analysis, the business owner or manager first identifies the dependent and independent variables. The dependent variable represents the figure the model will predict. The independent variables are those that impact the dependent variable. Therefore, the dependent variable is the effect or outcome, whereas the independent variables are the causes. A simple regression uses one dependent and one independent variable. A multiple regression model uses one dependent and multiple independent variables. A small-business owner won't have access to the same detailed data sets available to a statistician in a multinational company, which is why small-business owners tend to use simple regression models -- like the types that relate prices to sales levels.

Forecasting Example

The owner of an ice cream parlor intending to predict sales may start by building a regression model, where the sales level is the dependent variable and price and weather temperature are the independent variables. The resulting equation might look like this: Ice Cream Sales (in pounds) = 2.5*(100/Price) + 0.7*(Weather Temperature). This equation implies that the higher the price, the lower the sales, as the right side of the equation uses 100 divided by the price. So the higher the price, the lower the result of the ratio expressed by this equation. On the other hand, weather temperature has a positive influence on ice cream sales; higher temperatures elevate sales.

Unpredictable Independent Variables

A regression model is relatively useless for predictions if the independent variables are impossible to predict. If sales are dependent on the average price of competing products, for example, which is impossible to predict with accuracy, the regression model will not be useful as a predictive tool. This is especially important for the small-business owner who rarely has access to experts trying to forecast every detail for the next budgeting season. Therefore, the small-business owner must resist the temptation to use unpredictable independent variables. It's more useful to keep the equation simple and centered on publicly available data, such as economic growth and population forecasts.

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