Widgecorp the Idea Behind Using a Regression

WidgeCorp The idea behind using a regression model for forecast sales for cold beverages next year is to help the company make a better decision about that industry. The way this is done is to test all the possible influences on cold beverage demand, and apply the results. There are two different approaches we can take -- the simple approach and the complex approach. Which approach is better depends on what the decision we are making is.

Obviously there are differences between the amount of time, effort and most importantly money that go into the two approaches. The simple approach would be useful here if the decision is simply yes/no, should we enter the cold beverage market? In that case, precise demand statistics are probably not required.

We can use next year's sales as the dependent variable, and then have a handful of independent variables for the past several years including cold beverage sales, population, median income and weighted average summer temperature (weighted for population so that, for example, Florida counts more than New Mexico). The relationships between a handful of variables will give us a rough estimate of how the industry is expected to perform next year, given expectations from demographers and climate modelers.

If we have a general idea of how fast the cold beverage industry is rising, that is going to help us decide if it is a good industry to be in. A more complex form of analysis would be conducted the same way, but with more variables. This would allow us to fine-tune our decision making. Suppose we have already decided to enter the industry, but we had to make decisions with limited resources, so we can only enter five states.

We want to know the five with the best growth. We might want to know what states have the highest sales from independent cold beverage producers (not Coke or Pepsi). With more complex analysis a much wider range of variables can be used, because the decisions are being made at a finer level. For the initial decision, we want to set our dependent variable as the demand for whatever type of beverage we intend to produce.

If we do not know what we want to make, then regression can still help us, but it makes the demand predictions more complicated. Nevertheless, when choosing independent variables, there are two considerations. The first is that we want to limit the number we are working with, because we might have financial constraints, and some information may simply not be available to a firm that is not already in the industry. The second thing to remember when choosing independent variables is you want to identify the best ones.

The company is going to want to start with low-hanging fruit (easy to acquire) figures about demographics and weather, two key factors because rapidly growing areas are going to see a bigger change in their demand, as will areas that are expected to have warmer than average summers next year. There are other conditions that need to be tested as well. These include.