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DearCustomer

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Statistics

Part I

1. What is the purpose of forecasting?

The purpose of forecasting is to predict. We look at previous data in order to make a more educated guess (a forecast) about what's going to happen in the future.

2. Why are forecasts not always correct?

They are only guesses. Sometimes unexpected things happen that make forecasting models invalid. Sometimes forecasting models are inadequate, poorly made, or otherwise misleading. Sometimes unexpected error results in incorrect forecasts even when the model is a good one.

3. What are the limitations of forecasting?

Forecasting is limited by many factors. Imperfect information upon which the model is built leads to error in forecasting. Unexpected changes in the system leads, failure to include vital information in the mdoel -- all these things limit the predictive power of forecasting models. In addition, time is a limiter as well. That is, the further into the future that we attempt to predict the less accurate our predictions are likely to be.

Part II

1. Why is the trend commonly thought to be the most important component of forecasting?

The trend tells us the pattern that can be seen in the data. If we can spot the trend (through mathematical methods such as regression), then we can make more accurate predictions.

2. How is the linear trend method related to linear regression?

Forecasting is simple linear regression in which time is the predictor variable.

3. Why are simple moving averages and weighted averages commonly used forms of forecasting?

Moving averages and weighted averages are used to smooth the data. If we can smooth the excess variability in the data, it becomes easier to see the trend in the data that we're looking at. In addition, grouping the data together in this way can lead to additional statistical methods for analyzing the data.