As you might guess we are looking at some of the most Fundamental approaches to forecasting. But hopefully these are at least a feasible introduction to some of the computing issues related to executing forecasts in spreadsheets. A moving average is a procedure to get an overall idea of the trends in a data set. It is a subset of an average numbers. The moving average forecast is eminently useful for **Forecasting Long-term Trends****. **You can calculate a moving average for any period of time easily.

## Assumption of Constant Model:

Practically the moving average will assist a good estimation time series of the average, if the average is slowly changing or constant. In the situation of constant mean, the largest value of “*m”* will give the better estimation of the underlying average. A period of longer observation will mean out the effects of variability.

## Example of Moving Average Forecasting:

An average impersonate the “middle” value of a set of numbers. The moving average is precisely the same, but **the average is calculated for several subsets in several times of data****.**

For example, if you want a moving average for two years for a data sets from 2001, 2002, 2003 and 2004 you will find the averages for the subsets of 2001/2002, 2002/2003 and 2003/2004. Moving averages are commonly plotted and are best* **conjecture.*

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*Calculating a moving average for 5 years data:*

Following are the data which is use to calculate moving average for 5 years.

Year | $ Sale(M) |

2001 | 2 |

2002 | 4 |

2003 | 6 |

2004 | 8 |

2005 | 10 |

The average sales for the five years from 2001 to 2005 is calculated by finding the average from the five years by adding the sales of five years and divided by 5.

**2+4+6+8+10/5= 6M**

# Z-scores Improve Forecasting with Moving Average:

Forecasting is an important part of business management. The management can plan well, if the forecasting is good. There are many ways to make forecasts, some are used for specific situations and some can be used in any case. Black Belts is used for short term forecasting that can provide benefits such as looking for special causes of variation and analyzing production trends. Whereas, for making long-term forecasting, a method that is use for a Z-scores and normal curve may be the better to apply. Both methods are easy to use.

# Forecasting in Excel:

To take existing data, like sales, and extrapolate forward extrapolate is a common technique of forecasting. Excel contains a very simple and easy function for doing this i.e. **trend.** **Trend is known as _y’s,known_ x’s,constant****, _****x’s,new** projects presume that there is a connection between two sets of variables **x** i.e. independent variable and **y** i.e. dependent variable. By using a formula **y = βx + c**, you can prepare a forecasting moving average trend. The equation is a straight line i.e. **β** is the line gradient and **c** is the y intercept.