Different determinants of demand are varied and price quantity relationships are established at different points of time in the same market or different markets.
Only one determinant varied ; others kept constant.
S IMULATED M ARKET S ITUATION
An artificial market situation is created and “consumer clinics” selected. Consumers are asked to spend time in an artificial departmental store and different prices are set for different buyer groups.
The responses to the price changes are observed and necessary decisions taken.
Shows effective demand for the product for a specified time period
The trend can be estimated by using the Least Square Method
12.
A producer of soaps decides to forecast the next years sales of his product. The data for the last five years is as follows: YEARS SALES IN Rs.LAKHS 1996 45 1997 52 1998 48 1999 55 2000 60
Hence, sales projection from 2003-2005 can be ascertained.
2003 = 280 + 22(3) = Rs.346 crores
2004 = 280 + 22(4) = Rs.368 crores
2005 = 280 + 22 (5) = Rs.390 crores
23.
“ Method of Simple linear Regression ” The linear trend can be fitted in the equation Sales = a + b (Price) i.e. S = a + bP where in, a and b are constants. b = n ∑S i P i - (∑S i )(∑P i ) n ∑P i 2 – (∑P i ) 2 a = ∑S i - b ∑ P i n
24.
e.g. fit a linear regression line to the following data & estimate the demand at price Rs.30 YEAR ’ 81 ’ 82 ’ 83 ’ 84 ’ 85 ’ 86 ’ 87 ’ 88 ’ 89 ’ 90 ’ 91 ‘ 92 PRICE (P i ) 15 15 12 26 18 12 8 38 26 19 29 22 SALES (S i ) in 1000 units 52 46 38 37 37 37 34 25 22 22 20 14
25.
To find the values of a and b the following table is constituted: P i S i P i 2 S i 2 S i P i 15 52 225 2704 780 15 46 225 2116 690 12 38 144 1444 456 26 37 676 1369 962 18 37 324 1369 666 12 37 144 1369 444 8 34 64 1156 272 38 25 1444 625 950 26 22 676 484 572 19 22 361 484 418 29 20 841 400 580 22 14 484 196 308 ∑ P i = 240 ∑ S i = 384 ∑ P i 2 = 5708 ∑ S i 2 = 13716 ∑ S i P i = 7098
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