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Other applications of
logarithms: Trends |
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| ROW/COL |
B |
C |
D |
E |
F |
G |
H |
I |
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Other applications of
logarithms |
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| 7 |
Suppose you have the
following series of numbers for which you are trying to forecast the next one |
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| 8 |
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10% |
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| 9 |
Year |
Sales |
Year |
ln(sales) |
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| 10 |
1 |
$100.00 |
1 |
4.6052 |
<=LN(C10) |
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| 11 |
2 |
$110.00 |
2 |
4.7005 |
<=LN(C11) |
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| 12 |
3 |
$121.00 |
3 |
4.7958 |
<=LN(C12) |
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| 13 |
4 |
$133.10 |
4 |
4.8911 |
<=LN(C13) |
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| 14 |
5 |
$146.41 |
5 |
4.9864 |
<=LN(C14) |
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| 15 |
6 |
$161.05 |
6 |
5.0817 |
<=LN(C15) |
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| 16 |
7 |
$177.16 |
7 |
5.1770 |
<=LN(C16) |
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| 17 |
8 |
$194.87 |
8 |
5.2723 |
<=LN(C17) |
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| 18 |
9 |
$214.36 |
9 |
5.3677 |
<=LN(C18) |
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| 19 |
10 |
$235.79 |
10 |
5.4630 |
<=LN(C19) |
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| 20 |
11 |
$259.37 |
11 |
5.5583 |
<=LN(C20) |
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| 21 |
12 |
$285.31 |
12 |
5.6536 |
<=LN(C21) |
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| 22 |
13 |
? |
13 |
? |
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| 23 |
The linear trend forecasts: |
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| 24 |
Forecast |
Series |
|
ln(series) |
Exp(ln forecast) |
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| 25 |
|
$286.25 |
13 |
|
$313.84 |
<=EXP(E25) |
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| 26 |
=FORECAST(B22,C10:C21,B10:B21) |
=FORECAST(D25,E10:E21,D10:D21) |
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It is clear that the
process is not linear. Thus a linear estimate would not be expected to
produce good estimate |
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