# Regression Model

Introduction

Forecasting is a valuable way for a business to prepare and plan the selling of their products for the upcoming years. For this reason, it is extremely important for a business to do proper planning before developing new products (WebFinance, 2011). With that being said, since Widgecorp is reflecting on divisioning out into cold beverage products for the upcoming year it is now time to discuss a regression model to estimate the monthly sales of these products. Also to be discussed is variables that WidgeCorp needs to consider when forecasting the monthly sales of the cold beverage products.

Regression Models

A regression model is a dominant tool for offering WidgeCorp calculations regarding previous, current, or upcoming events in forecasting the outcome of their cold beverage products. For WidgCcorp to create a regression model, the data that is going to be utilized to formulate the estimation and the data that is going to be calculated must both be gathered from a sample of their consumers. The connection among these gathered facts obtained by WidgeCorp is then formed by transforming the data into a straight line graph. For example, Widgecorp might obtain a sample of their consumer’s cold beverage preferences by giving them a survey to get an ideal of what brand of cold beverages each prefers. Next, Widgecorp would then predict the preferences of their consumers as it relates to cold beverages products, and the selling of all future cold beverage products by WidgeCorp would then be based on the consumer preferences of the sample taken (Stockburger, n.d.).

Variables to Consider

In my opinion Y and X are the variables to consider. Y is the independent variable which would be the variable used to predict the preferred cold beverage product that may be sold. For instance, Y = the preferred brand of cold beverage products by consumers. X is the dependent variable which would be the observed value of the prediction variable. For instance, the predicted amount of cold beverage products sold to consumers. The goal in this process of regression would be to create a model that will predict the preferred brand of cold beverages consumers prefer and the observed amount that consumers are likely to purchase from WidgeCorp (Stockburger, n.d.).

Conclusion

A common regression model for WidegeCorp will be made up of a function illustrating how one variable (Y) is connected to the other variable (X). With that being said, forecasting for WidgeCorp’s cold beverage products will start with certain assumptions based on the experience, knowledge, and judgment of the chosen forecaster. To be successful, WidgeCorp must align their capacity, resources, and products to the expected demand of their consumers. The ability to accurately forecast the demand of Widgecorps cold beverage products is significantly important, as well as continually forecasting the product on an ongoing basis (CTU Online, 2011).

References

CTU Online. (2011). Applied Managerial Decision Making. Phase 4 course materials [text]. Retrieved from https://campus.ctuonline.edu/pages/MainFrame.aspx?ContentFrame=/Home/Pages/Default.aspx

Stockburger, D. (n.d.). Introduction statistics: Concepts, models, and applications. Regression models. Retrieved from http://www.psychstat.missouristate.edu/introbook/sbk16.htm