Summarize paragraph please.

Quantitative forecasting techniquesare sometimes termed objective or mathemati- cal techniques as they rely more upon mathematics and less upon judgement in their computation. These techniques are now very popular as a result of sophisticated computer packages, some being tailor-made for the company needing the forecast.

It is not proposed to go into the detailed working of such techniques because they re- quire specialist skills in their own right; indeed a single technique could take up an entire textbook. Some quantitative techniques are simple while others are extremely complex. We now explain such techniques so you will have an appreciation of their usefulness and applicability. If the forecasting problem calls for specialist mathematical techniques then the answer is to consult a specialist and not attempt it on the basis of incomplete information given here. Quantitative techniques can be divided into two types:

1.Time series analysis. The only variable that the forecaster considers is time. These techniques are relatively simple to apply, but the danger is that too much emphasis might be placed upon past events to predict the future. The techniques are useful in predicting sales in markets that are relatively stable and not susceptible to sudden

irrational changes in demand. In other words, it is not possible to predict down- turns or upturns in the market, unless the forecaster deliberately manipulates the forecast to incorporate such a downturn or upturn.

2.Causal techniques. It is assumed that there is a relationship between the measurable independent variable and the forecasted dependent variable. The forecast is pro- duced by putting the value of the independent variable into the calculation. One must choose a suitable independent variable and the period of the forecast to be produced must be considered carefully. The techniques are thus concerned with cause and effect. The problem arises when one attempts to establish reasons behind these cause and effect relationships; in many cases there is no logical explanation. Indeed, there is quite often nothing to suppose that the relationship should hold good in the future. Reasoning behind causal relationships may not be too clear at this stage, but once the techniques are examined later in the chapter it should become self-evident. The first set of techniques examined are those concerned withtime series analysis.