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Strategic Thought Transformation - The IIPM Think Tank

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E N T R Y S T R A T E G I E S<br />

try shows a discrete growth. Conclusively<br />

speaking, there is lack of continuity in the<br />

data collected. As a proof, example, the Indian<br />

Per Capita Income (in terms of Purchsing<br />

Power Parity) in 1999 was $2,149 (INR<br />

99,778). <strong>The</strong> same value when measured<br />

after four years (in 2003) stood at $2,892<br />

(INR 1,34,275). <strong>The</strong> change witnessed as<br />

a result of this time-interval amounts to<br />

INR 34,497 (at the current conversion rate<br />

of $1=INR 46.43). This demonstrates that<br />

if the standard time-interval during the<br />

study is taken as four years, then the data<br />

for per capita income collected is discrete<br />

with non-continuity prevalent. Now considering<br />

that the same data for the year<br />

2005 is $3,300 (INR 1,53,219), which essentially<br />

proves that even if the standard<br />

time-interval is altered to two years, the<br />

difference from the data collected in 2003<br />

would be INR 18,944. In simpler words,<br />

there inevitably exists discontinuity in per<br />

capita purchasing power when evaluated<br />

over regular intervals of time.<br />

Procedure<br />

<strong>The</strong> per capita income (PPP) and trip per<br />

capita of 44 countries were taken, with a<br />

requisite number of countries in all classified<br />

per capita income segments (low per<br />

capita income, medium per capita income<br />

and high per capita income). It is to be noted<br />

that for all analytical purposes, number<br />

of trips per capita was considered to be<br />

the dependent variable and the per capita<br />

income (in terms of PPP) was considered<br />

as the independent variable. This also<br />

runs well along the lines of our assumption<br />

(assumption 2), whereby we assume<br />

that with growth in per capita income, the<br />

dispensable income increases and hence<br />

the frequency of air travel (number of trips<br />

per capita) also increases. This would also<br />

sufficiently imply that a fall in per capita<br />

income would lower the number of trips<br />

per capita.<br />

Also to be taken in consideration is the<br />

fact that the number of trips per capita<br />

(which is the dependent variable) may depend<br />

upon a lot of other variables besides<br />

per capita income – like changing demographics,<br />

infrastructure and airport facilities,<br />

regional factors which may be legal,<br />

political, social or cultural – yet we will<br />

<strong>The</strong>re inevitably<br />

exists discontinuity<br />

in per capita<br />

purchasing power<br />

when evaluated<br />

over regular<br />

intervals of time<br />

assume that the economic factor has the<br />

single-largest dominant role to play in effecting<br />

the trip per capita and all other factors<br />

would hardly restrict such growth.<br />

<strong>The</strong> countries were chosen for the data<br />

list following a random sampling method<br />

among those for which the required data<br />

were available. <strong>The</strong> full list of countries and<br />

data involved is given in Table I (aside).<br />

From the graph, it can be easily understood<br />

that all attempts to paint a global<br />

demand scenario for the aviation industry,<br />

would yield a very haphazard pattern and<br />

therefore for conducting a REGRESSION<br />

ANALYSIS, we consider various trend lines<br />

that aid our process and therefore make<br />

the estimation model construction more<br />

accurate and precise.<br />

Linear trend line<br />

<strong>The</strong> linear trend line gives us an approximate<br />

equation of y=0.0665x and the R 2<br />

value for the trend line is just 0.3815,<br />

Hence we do not base our projections based<br />

on this simplistic model.<br />

Logarithmic trend line<br />

<strong>The</strong> logarithmic trend line is built with the<br />

equation y=1.0873ln(x)–1.7611 with an<br />

R 2 value of just 0.2922. Hence we also do<br />

not base our projections based on this simplistic<br />

model.<br />

Trinomial Trend line<br />

<strong>The</strong> trinomial trend line gives a curve formation<br />

with the equation of y=0.0001x 3 -<br />

0.0062x 2 +0.1563x+0.6525 and R 2 value<br />

of just 0.4469 which we discard as this<br />

trend line gives an R 2 value of just 0.5.<br />

Power Series<br />

<strong>The</strong> power series trend line is constructed<br />

with the equation: y=0.0007x 2.1239 with an<br />

R 2 value of 0.6383. <strong>The</strong>refore in comparison<br />

to all previous trend lines constructed,<br />

this is the most reliable as the R-squared<br />

value is greater than 0.5.<br />

Exponential Series trend line<br />

<strong>The</strong> exponential series trend line gives us<br />

the equation of y=0.0108e 0.1465x and an<br />

R 2 value of 0.6468 which is the highest<br />

so far. Hence we can easily conclude that<br />

this exponential series with the highest R 2<br />

value is thus the most reliable trend line<br />

that can be used for estimating the future<br />

demand generation in the sector. <strong>The</strong>refore,<br />

we take the exponential series trend<br />

line as the most reliable demand curve to<br />

estimate future demand.<br />

Dismissal of Linear, Logarithmic<br />

and Trinomial Trend lines<br />

Besides the low values of R 2 obtained in<br />

terms of the linear, logarithmic and the<br />

An <strong>IIPM</strong> Intelligence Unit Publication STRATEGIC INNOVATORS 15

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