The qualitative methods that can be used within the scenario are forecasting methods like the linear trend model and the seasonal variations. The linear trend model is when the dependent variable is regressed against only one independent variable, times series data. The times series data is defined as a sequence of the values of an economic variable at different points of time. For example, in the scenario the dependent variable is the quantity of the Katrina's sugar free candy and the independent variable for time is the corresponding year. Seasonal variations are short- run predictive power that can significantly improve and can be estimated in a number of ways. One approach that can be used are the dummy variables that takes of the two values, 0 or 1 and they are used to capture the impact of certain qualitative factors in an econometric relationship. 

Now, assume you have acquired some time series data that would enable you to make short, medium, and long term forecasts. Ascertain the quantitative technique that will provide you with the most accurate forecast. Provide a rationale for your responses.

The time series data used should be seasonal variations because they tend to be more or less consistent year to year. For example, throughout the year Katrina Candies can shoe different stages of increases and decrease in sales due to the time of the year. In the summer most consumers do not purchase chocolate because of the heat that makes it melt quickly, cause a decrease in sales. During the holiday season such as Christmas, there is an increase in sales because the weather is cooler and chocolates make a perfect gift.

In your opinion, which method is better for short term analysis? Which method is based on long term analysis? Why did you determine your decision for both cases?