Free Hand Curve Method, Business Mathematics and Statistics B Com Notes | EduRev

Business Mathematics and Statistics

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B Com : Free Hand Curve Method, Business Mathematics and Statistics B Com Notes | EduRev

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Freehand Curve Method : The term freehand is used to any non-mathematical curve in statistical analysis even if it is drawn with the aid of drafting instruments. This is the simplest method of studying trend of a time series. The procedure for drawing free hand curve is an follows :
(i) The original data are first plotted on a graph paper.
(ii) The direction of the plotted data is carefully observed.
(iii) A smooth line is drawn through the plotted points.
While fitting a trend line by the freehand method, an attempt should be made that the fitted curve conforms to these conditions.
(i) The curve should be smooth either a straight line or a combination of long gradual curves.
(ii) The trend line or curve should be drawn through the graph of the data in such a way that the areas below and above the trend line are equal to each other.
(iii) The vertical deviations of the data above the trend line must equal to the deviations below the line.
(iv) Sum of the squares of the vertical deviations of the observations from the trend should be minimum.

Illustration : Draw a time series graph relating to the following data and fit the trend by freehand method :

Year              Production of Steel
(million tonnes)
1994          20
1995          22
1996          30
1997          28
1998          32
1999          25
2000          29
2001          35
2002          40
2003          32

The trend line drawn by the freehand method can be extended to project future values. However, the freehand curve fitting is too subjective and should not be used as a basis for prediction. Method of Moving Averages : The moving average is a simple and flexible process of trend measurement which is quite accurate under certain conditions. This method establishes a trend by means of a series of averages covering overlapping periods of the data.
The process of successively averaging, say, three years data, and establishing each average as the moving-average value of the central year in the group, should be carried throughout the entire series. For a five-item, seven-item or other moving averages, the same procedure is followed : the average obtained each time being considered as representative of the middle period of the group.
The choice of a 5-year, 7-year, 9-year, or other moving average is determined by the length of period necessary to eliminate the effects of the business cycle and erratic fluctuations. A good trend must be free from such movements, and if there is any definite periodicity to the cycle, it is well to have the moving average to cover one cycle period. Ordinarily, the necessary periods will range between three and ten years for general business series but even longer periods are required for certain industries.
In the preceding discussion, the moving averages of odd number of years were representatives of the middle years. If the moving average covers an even number of years, each average will still be representative of the midpoint of the period covered, but this mid-point will fall halfway between the two middle years. In the case of a four-year moving average, for instance each average represents a point halfway between the second and third years . In such a case, a second moving average may be used to ‘recentre’ the averages.
That is, if the first moving averages gives averages centering half-way between the years, a further two-point moving average will recentre the data exactly on the years.

This method, however, is valuable in approximating trends in a period of transition when the mathematical lines or curves may be inadequate. This method provides a basis for testing other types of trends, even though the data are not such as to justify its use otherwise.

Illustration : Calculate 5-yearly moving average trend for the time series given below.

Year :       1990     1991     1992     1993     1994     1995     1996     1997     1998     1999     2000
Quantity :   239     242       238      252       257       250      273       270      268       288      284
Year :        2001    2002     2003     2004     2005     2006     2007     2008     2009     2010
Quantity :  282     300       303       298      313       317       309      329       333      327

Solution :

Year    Quantity     5-yearly moving total       5-yearly moving average

1990     239
1991     242
1992     238            1228                                 245.6
1993     252            1239                                 247.8
1994     257            1270                                 254.0
1995     250            1302                                 260.4
1996     273            1318                                 263.6
1997     270            1349                                 269.8
1998     268            1383                                 276.6
1999     288            1392                                 278.4
1990     284            1422                                 284.4
2001     282            1457                                 291.4
2002     300            1467                                 293.4
2003     303            1496                                 299.2
2004     298            1531                                 306.2
2005     313            1540                                 308.0
2006     317            1566                                 313.2
2007     309            1601                                 320.2
2008     329            1615                                 323.0
2009     333
2010     327

To simplify calculation work: Obtain the total of first five years deta. Find out the difference between the first and sixth term and add to the total to obtain the total of second to sixth term. In this way the difference between the term to be omitted and the term to be included is added to the preceding total in order to obtain the next successive total.

Illustration : Fit a trend line by the method of four-yearly moving average to the following time series data.

Year :                                  1995   1996   1997   1998   1999   2000   2001   2002
Sugar production (lakh tons) : 5       6        7        7        6       8        9        10
Year :                                  2003   2004   2005   2006
Sugar production (lakh tons) : 9       10      11      11

Solution :

Remark : Observe carefully the placement of totals, averages between the lines.
Merits
1. This is a very simple method.
2. The element of flexibility is always present in this method as all the calculations have not to be altered if same data is added. It only provides additional trend values.
3. If there is a coincidence of the period of moving averages and the period of cyclical fluctuations, the fluctuations automatically disappear.
4. The pattern of moving average is determined in the trend of data and remains unaffected by the choice of method to be employed.
5. It can be put to utmost use in case of series having strikingly irregular trend.

Limitations
1. It is not possible to have a trend value for each and every year. As the period of moving average increases, there is always an increase in the number of years for which trend values cannot be calculated and known. For example, in a five yearly moving average, trend value cannot be obtained for the first two years and last two years, in a seven yearly moving average for the first three years and last three years and so on. But usually values of the extreme years are of great interest.
2. There is no hard and fast rule for the selection of a period of moving average.
3. Forecasting is one of the leading objectives of trend analysis. But this objective remains unfulfilled because moving average is not represented by a mathematical function.
4. Theoretically it is claimed that cyclical fluctuations are ironed out if period of moving average coincide with period of cycle, but in practice cycles are not perfectly periodic.

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