By. M.A.Yulianto.*)

Many of forecasting techniques have been used since today, such as regression analysis technique.  With more sophisticated of forecasting techniques along with the advent of computers, forecasting has been accepted and got more attention.  Every manager or decision maker now has the ability to use data analysis technique for forecasting purposes, and an understanding of these techniques is now essential for business managers or decision makers.

 Along with the needs of forecasting process in business activities, new techniques for forecasting continue to be developed. This particular attention from these developments is on the errors that are important part of any forecasting procedure.  Predictions as to future outcome rarely are precisely on the mark, the forecaster can only try to make the errors as small as possible.

 In view of the inaccuracies in the process, why is forecasting necessary?  The answer is that all organizations operate in an atmosphere of uncertainty, in spite of this fact, decisions must be made for the future of the organization.  Besides of that,  in organization activities are frequently faced to the situations that are easily change so forecasting is always needed.

 Who need forecasts?  Almost every organization, large and small, private and public, uses forecasting either explicitly or implicitly, because almost every organization must plan to meet the conditions of the future for which it has imperfect knowledge.  Forecasts are needed in finance, marketing, personnel, and production areas, in government and profit seeking organizations, in social clubs, and in national political parties.

 Data are something that considered can be pictured about the situation or problem. Data are assumed as something that may be not true. However, assumption is often used as a guidance of decision making.  According to the time collection reference, data are classified into two groups that are cross sectional data and time series data.  Cross sectional data is the data collected in a certain time, while time series data is the data collected from time to time.  Data got from a survey can be analyzed by descriptive and inference way.  Descriptive statistics is a method in how to organized, summing, and presented data in comfortable and informative ways  included graphical and counting techniques.  Descriptive statistics can describes data that are analyzing, however it cannot makes decisions or inferences about population from those data.  For decision making, we need another statistical technique that is inference statistics.  Inference statistics  is a making process of estimations, forecasting, or decision about population based on a sample.

  The explanation about what and how to use forecasting techniques in application can be explained in other writing session.  Have enjoying statistics.

*)  Writer is a lecturer in Institute of Statistics, Jakarta, Indonesia.

Bachelor of  Statistics from Institute of Statistics, Jakarta, Indonesia.

Master of Science in Experimental Statistics from NMSU, USA.

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