complete data analysis plus research methodology draft

7/31/2019 COMPLETE DATA ANALYSIS Plus Research Methodology Draft.

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ANNEXURE 3

The influence of price of petrol on foreign exchange earnings from hospitality industry is given

by the regression equation y= 11497.65 + 691.7753x. The hypothesis tested with the regression

equation is as follows.

H0: b = 0 (no influence of petrol prices on foreign exchange earnings from hospitality industry)

Ha: b 0(influence of petrol prices on foreign exchange earnings from hospitality industry)

The R2 value is 0.275919. This means that 27.59% of the variation in the foreign exchange

earnings in hospitality industry is being explained by the changes in the price of petrol. As this is

very small therefore the regression outcomes cannot be relied upon. Furthermore, the adjusted

R2 (0.185) is very small and significantly different and less than the R2. This also means that the

sample size taken into consideration is inadequate to draw any conclusion about the true relationbetween the chosen variables. The p value is 0.118956; this value is more than 0.05. This value

of p is the level of significance at which this hypothesis can be rejected. Here the null hypothesis

is accepted and that means that price of petrol has no significant influence on the foreign

exchange earnings of the hospitality industry (p value=0.118956).

ANNEXURE 5

The influence of price of diesel on foreign exchange earnings from hospitality industry is given

by the regression equation y= -7338.74+ 1556.257x. The hypothesis tested with the regression


H0: b = 0 (no influence of diesel prices on foreign exchange earnings from hospitality industry)

Ha: b 0(influence ofdiesel prices on foreign exchange earnings from hospitality industry)


earnings in hospitality industry is being explained by the changes in the price of diesel. As this isvery small therefore the regression outcomes cannot be relied upon. Furthermore, the adjusted

R2 (0.32794) is small and significantly different and less than the R2. This also means that the

sample size taken into consideration is inadequate to draw any conclusion about the true relation

between the chosen variables. The p value is 0.402614; this value is more than 0.05. This value of

p is the level of significance at which this hypothesis can be rejected. Here the null hypothesis is

accepted and that means that that price of diesel has no significant influence on the foreign

exchange earnings of the hospitality industry (p-value = 0.402614).


2/29

ANNEXURE 7

The influence of price of crude oil on foreign exchange earnings from hospitality industry is

given by the regression equation y= 23249.23+ 292.3596x. The hypothesis tested with the

regression equation is as follows.

H0: b = 0 (no influence of crude oil prices on foreign exchange earnings from hospitalityindustry)

Ha: b 0(influence ofcrude oil prices on foreign exchange earnings from hospitality industry)


earnings in hospitality industry is being explained by the changes in the price of crude oil. As

this is very small therefore the regression outcomes cannot be relied upon. Furthermore, the

adjusted R2 (0.244231) is small and significantly different and less than the R2. This also meansthat the sample size taken into consideration is inadequate to draw any conclusion about the true

relation between the chosen variables. The p value is 0.083439; this value is more than 0.05. This

value of p is the level of significance at which this hypothesis can be rejected. Here the null

hypothesis is accepted and that means that price of crude oil has no significant influence on

the foreign exchange earnings of the hospitality industry (p-value=0.083439).

ANNEXURE 9

The influence of price of domestic LPG on foreign exchange earnings from hospitality industry

is given by the regression equation y= -12867.8+ 186.2685x. The hypothesis tested with the


H0: b = 0 (no influence of domestic LPG prices on foreign exchange earnings from hospitality

industry)

Ha: b 0(influence of domestic LPG prices on foreign exchange earnings from hospitality

industry)


earnings in hospitality industry is being explained by the changes in the price of domestic LPG.

As this is very small therefore the regression outcomes cannot be relied upon. Furthermore, the

adjusted R2 (0.250274) is small and significantly different and less than the R2. This also means

that the sample size taken into consideration is inadequate to draw any conclusion about the true

relation between the chosen variables. The p value is 0.080379; this value is more than 0.05. This


3/29

value of p is the level of significance at which this hypothesis can be rejected. Here the null

hypothesis is accepted and that means that price of domestic LPG has no significant influence

on the foreign exchange earnings of the hospitality industry (p value=0.080379).

ANNEXURE 11

The influence of exchange rate(USD/RS) on foreign exchange earnings from hospitality industry

is given by the regression equation y= 92854.80044-1050.339779x. The hypothesis tested with the


H0: b = 0 (no influence of exchange rate (usd/rs) on foreign exchange earnings from hospitality

industry)

Ha: b 0(influence of exchange rate (usd/rs on foreign exchange earnings from hospitality

industry)


earnings in hospitality industry is being explained by the changes in the exchange rate of

USD/RS. As this is very small therefore the regression outcomes cannot be relied upon.

Furthermore, the adjusted R2 (-0.057685086) is very small and significantly different and less

than the R2. This also means that the sample size taken into consideration is inadequate to draw

any conclusion about the true relation between the chosen variables. The p value is 0.495794;

this value is more than 0.05. This value of p is the level of significance at which this hypothesis

can be rejected. Here the null hypothesis is accepted and that means that exchange rate usd/rs

has no significant influence on the foreign exchange earnings of the hospitality industry (p-

value=0.495794).

ANNEXURE 13

The influence of population of India on foreign exchange earnings from hospitality industry is

given by the regression equation y= -196700 + 212.602x. The hypothesis tested with the


H0: b = 0 (no influence of population of India on foreign exchange earnings from hospitality

industry)

Ha: b 0(influence of population of India on foreign exchange earnings from hospitality

industry)


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earnings in hospitality industry is being explained by the changes in the population of India. As


adjusted R2 (0.385995) is very small and significantly different and less than the R2. This also

means that the sample size taken into consideration is inadequate to draw any conclusion about

the true relation between the chosen variables. The p value is 0.385995; this value is more than

0.05. This value of p is the level of significance at which this hypothesis can be rejected. Here

the null hypothesis is accepted and that means that population of India has no significant

influence on the foreign exchange earnings of the hospitality industry (p-value=0.385995).

ANNEXURE 15

The influence of exchange rate (SDR/RS) on foreign exchange earnings from hospitality industry

is given by the regression equation y= 8157.324 + 527.704x. The hypothesis tested with the


H0: b = 0 (no influence of exchange rate of special drawing rights on foreign exchange earnings

from hospitality industry)

Ha: b 0(influence of exchange rate of special drawing rights on foreign exchange earnings



earnings in hospitality industry is being explained by the changes in the rate of special drawing

rights. As this is very small therefore the regression outcomes cannot be relied upon.

Furthermore, the adjusted R2 (-0.09215) is very small and significantly different and less than the

R2. This also means that the sample size taken into consideration is inadequate to draw any

conclusion about the true relation between the chosen variables. The p value is 0.636937; this

value is more than 0.05. This value of p is the level of significance at which this hypothesis canbe rejected. Here the null hypothesis is accepted and that means that exchange rate of special

drawing rights has no significant influence on the foreign exchange earnings of the

hospitality industry (P-VALUE=0.636937).

ANNEXURE 17


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The influence of exchange rate (POUND STERLING/RS) on foreign exchange earnings from

hospitality industry is given by the regression equation y= 150245.6 -1343.61x. The hypothesis

tested with the regression equation is as follows.

H0: b = 0 (no influence of exchange rate of POUND STERLING/RUPEE on foreign exchange

earnings from hospitality industry)

Ha: b 0(influence of exchange rate of POUND STERLING on foreign exchange earnings from

hospitality industry)


earnings in hospitality industry is being explained by the changes in the rate pound sterling. As






the null hypothesis is accepted and that means that exchange rate of pound sterling has no

significant influence on the foreign exchange earnings of the hospitality industry (p

value=0.120256).

ANNEXURE 19

The influence ofGDP GROWTH RATE on foreign exchange earnings from hospitality industry is

given by the regression equation y= 28814.84 + 2016.91x. The hypothesis tested with the


H0: b = 0 (no influence of GDP growth rate of India on foreign exchange earnings from


Ha: b 0(influence ofGDP growth rate of India on foreign exchange earnings from hospitality

industry)


earnings in hospitality industry is being explained by the changes in the GDP growth rate of

India. As this is very small therefore the regression outcomes cannot be relied upon.




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value is more than 0.05. This value of p is the level of significance at which this hypothesis can

be rejected. Here the null hypothesis is accepted and that means that GDP growth rate of India

has no significant influence on the foreign exchange earnings of the hospitality industry (p

value=0.381454).

ANNEXURE 21

The influence ofTOURIST ARRIVAL TO INDIA on foreign exchange earnings from hospitality

industry is given by the regression equation y= -19355.9+ 13677.36x. The hypothesis tested with

the regression equation is as follows.

H0: b = 0 (no influence of tourist arrival to India on foreign exchange earnings from hospitality

industry)

Ha: b 0(influence of tourist arrival to India on foreign exchange earnings from hospitality

industry)


earnings in hospitality industry is being explained by the changes in the tourist arrival to India.

Furthermore, the adjusted R2 (0.928444) is not small and significantly not different than the R2.

This also means that the sample size taken into consideration is adequate to draw any conclusion

about the true relation between the chosen variables. The p value is 0.0000738; this value is

much less than 0.05. This value of p is the level of significance at which this hypothesis can be

rejected. Here the null hypothesis is rejected and that means that tourist arrival to India has

quite a significant influence on the foreign exchange earnings of the hospitality industry (p

value=0.0000738).

ANNEXURE 23

The influence ofCAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN INDIA on foreign

exchange earnings from hospitality industry is given by the regression equation y= 30450.49+

284.3081x. The hypothesis tested with the regression equation is as follows.

H0: b = 0 (no influence of CAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN

INDIA on foreign exchange earnings from hospitality industry)

Ha: b 0(influence of CAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN

INDIA on foreign exchange earnings from hospitality industry)


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the null hypothesis is accepted and that means that CAPITAL OUTLAY FOR PROMOTION OF

ECO TOURISM IN INDIAhas no significant influence on the foreign exchange earnings of

the hospitality industry (p value=0.281372).

ANNEXURE 25

The influence of expenditure for promotion on eco tourism on foreign exchange earnings from

hospitality industry is given by the regression equation y= 17628.47+787.8395x. The hypothesis


H0: b = 0 (no influence of expenditure for promotion on eco tourism on foreign exchange


Ha: b 0(influence ofexpenditure for promotion on eco tourism on foreign exchange earnings


The R2 value is 0.9338. This means that 93.38% of the variation in the foreign exchange earnings

in hospitality industry is being explained by the changes in the expenditure for promotion on eco

tourism. The adjusted R2 (0.925525) is not small and is not significantly different from the R2.

This also means that the sample size taken into consideration is adequate to draw conclusions

about the true relation between the chosen variables. The p value is 5.4E-06; this value is less

than 0.05. This value of p is the level of significance at which this hypothesis can be rejected.

Here the null hypothesis is rejected and that means that expenditure for promotion on eco

tourism has a very significant influence on the foreign exchange earnings of the hospitality

industry. (p value= 5.4E-06)

ANNEXURE 27

The influence of % SHARE OF INDIA IN WORLD TOURIST ARRIVAL on foreign exchange

earnings from hospitality industry is given by the regression equation y= 40712.97 + 1771.802x.

The hypothesis tested with the regression equation is as follows.


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H0: b = 0 (no influence of SHARE OF INDIA IN WORLD TOURIST ARRIVAL on foreign

exchange earnings from hospitality industry)

Ha: b 0(influence of SHARE OF INDIA IN WORLD TOURIST ARRIVAL on foreign









the null hypothesis is accepted and that means that SHARE OF INDIA IN WORLD TOURIST

ARRIVALhas no significant influence on the foreign exchange earnings of the hospitality

industry. (p value = 0.334856)

ANNEXURE 29

The influence ofWORLD TOURIST ARRIVALS on foreign exchange earnings from hospitality

industry is given by the regression equation y= -95034.5+ 162.8583x. The hypothesis tested with


H0: b = 0 (no influence of WORLD TOURIST ARRIVALS on foreign exchange earnings from


Ha: b 0(influence of WORLD TOURIST ARRIVALS on foreign exchange earnings from




therefore the regression outcomes cannot be relied upon. Furthermore, the adjusted R2 (0.871003)

is not significantly different than the R2. This also means that the sample size taken into

consideration is adequate to draw any conclusion about the true relation between the chosen

variables. The p value is 0.000441; this value is less than 0.05. This value of p is the level of

significance at which this hypothesis can be rejected. Here the null hypothesis is rejected and that

means thatWORLD TOURIST ARRIVALS has a significant influence on the foreign

exchange earnings of the hospitality industry. (p value= 0.000441)


9/29

ANNEXURE 31

The influence ofdomestic tourist visits on foreign exchange earnings from hospitality industry is

given by the regression equation y= -6760.424086+ 9.67752E-05x. The hypothesis tested with the


H0: b = 0 (no influence of domestic tourist visits on foreign exchange earnings from hospitality

industry)

Ha: b 0(influence of domestic tourist visits on foreign exchange earnings from hospitality

industry)


earnings in hospitality industry is being explained by the changes in the tourist arrival to India..

Furthermore, the adjusted R2 (0.977318571) is not significantly different than the R2. This also

means that the sample size taken into consideration is adequate to draw any conclusion about the

true relation between the chosen variables. The p value is 2.3132E-06; this value is less than 0.05.

This value of p is the level of significance at which this hypothesis can be rejected. Here the null

hypothesis is rejected and that means that domestic tourist visits have a significant influence on

the foreign exchange earnings of the hospitality industry (p-value= 2.3132E-06).

ANNEXURE 33

The influence ofcement prices on foreign exchange earnings from hospitality industry is given by

the regression equation y= 211.5338 + 216.0725x. The hypothesis tested with the regression


H0: b = 0 (no influence of cement prices on foreign exchange earnings from hospitality industry)

Ha: b 0(influence ofcement prices on foreign exchange earnings from hospitality industry)


earnings in hospitality industry is being explained by the changes in the prices of cement.

Therefore, the regression outcomes cannot be relied upon. Furthermore, the adjusted R2

(0.34124) is different than the R2. This also means that the sample size taken into consideration is

adequate to draw any conclusion about the true relation between the chosen variables. The p

value is 0.04458; this value is less than 0.05. This value of p is the level of significance at which

this hypothesis can be rejected. Here the null hypothesis is rejected and that means thatprices of


10/29

cement have a significant influence on the foreign exchange earnings of the hospitality

industry(p-value= 0.04458).

ANNEXURE 35

The influence of assistance sanctioned by TFCI on foreign exchange earnings from hospitality

industry is given by the regression equation y= 30874.31+ 0.499129. The hypothesis tested with


H0: b = 0 (no influence of assistance sanctioned by TFCI on foreign exchange earnings from


Ha: b 0(influence of assistance sanctioned by TFCI on foreign exchange earnings from



earnings in hospitality industry is being explained by the changes in the assistance sanctioned by

TFCI. Therefore, the regression outcomes cannot be relied upon. Furthermore, the adjusted R2

(0.650938) is different than the R2. This also means that the sample size taken into consideration

is inadequate to draw any conclusion about the true relation between the chosen variables. The p

value is 0.009523; this value is less than 0.05. This value of p is the level of significance at which

this hypothesis can be rejected. Here the null hypothesis is rejected and that means that assistance

sanctioned by TFCI has a significant influence on the foreign exchange earnings of the

hospitality industry (p-value= 0.009523).

ANNEXURE 37

The influence of assistance disbursed by TFCI on foreign exchange earnings from hospitality

industry is given by the regression equation y= 30120.77+ 1.00949. The hypothesis tested with the


H0: b = 0 (no influence of assistance disbursed by TFCI on foreign exchange earnings from


Ha: b 0(influence of assistance disbursed by TFCI on foreign exchange earnings from



earnings in hospitality industry is being explained by the changes in the assistance sanctioned by


11/29

TFCI. Therefore, the regression outcomes cannot be relied upon. Furthermore, the adjusted R2

(0.61335) is not very different than the R2. This also means that the sample size taken into

consideration is adequate to draw conclusion about the true relation between the chosen

variables. The p value is 0.013157; this value is less than 0.05. This value of p is the level of

significance at which this hypothesis can be rejected. Here the null hypothesis is rejected and that

means that assistance disbursed by TFCI has a significant influence on the foreign exchange

earnings of the hospitality industry(p-value= 0.013157).

ANNEXURE 39

The influence of Number OF PROJECTS SANCTIONED BY MTI on foreign exchange earnings

from hospitality industry is given by the regression equation y= 20731.93 + 161.0817x. The

hypothesis tested with the regression equation is as follows.

H0: b = 0 (no influence of Number OF PROJECTS SANCTIONED BY MTI on foreign


Ha: b 0(influence ofNumber OF PROJECTS SANCTIONED BY MTI on foreign exchange



earnings in hospitality industry is being explained by the changes in the Number OF PROJECTS

SANCTIONED BY MTI. As this is very small therefore the regression outcomes cannot be relied

upon. Furthermore, the adjusted R2 (0.212376) is very small and significantly different and less


any conclusion about the true relation between the chosen variables. The p value is 0.245048; this


be rejected. Here the null hypothesis is accepted and that means that Number OF PROJECTS

SANCTIONED BY MTI has no significant influence on the foreign exchange earnings of the


ANNEXURE 41

The influence ofamount SANCTIONED BY MTI on foreign exchange earnings from hospitality

industry is given by the regression equation y= 19903.39+ 49.85517x. The hypothesis tested with


H0: b = 0 (no influence of amount SANCTIONED BY MTI on foreign exchange earnings from



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Ha: b 0(influence of amount SANCTIONED BY MTI on foreign exchange earnings from



earnings in hospitality industry is being explained by the changes in the Number OF PROJECTS

SANCTIONED BY MTI. As this is very small therefore the regression outcomes cannot be relied





be rejected. Here the null hypothesis is accepted and that means that amount SANCTIONED BY

MTI has no significant influence on the foreign exchange earnings of the hospitality

industry(p-value= 0.349926).

ANNEXURE 43

The influence of price of petrol on annual growth rate of GDP at factor cost of hospitality

industry is given by the regression equation y=15.40605 - 0.10324x. The hypothesis tested with


H0: b = 0 (no influence of petrol prices on annual growth rate of GDP at factor cost of hospitality

industry)

Ha: b 0(influence of petrol prices on annual growth rate of GDP at factor cost of hospitality

industry)

The R2 value is 0.044185. This means that 4.41% of the variation in the annual growth rate of

GDP at factor cost of the hospitality industry is being explained by the changes in the price of

petrol. As this is very small therefore the regression outcomes cannot be relied upon.

Furthermore, the adjusted R2 (-0.07529) is very small and significantly different and less than

the R2. This also means that the sample size taken into consideration is inadequate to draw any



be rejected. Here the null hypothesis is accepted and that means that price of petrol has no

significant influence on the annual growth rate of GDP at factor cost of hospitality industry

(p-value= 0.559967).

ANNEXURE 44


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The influence of price of diesel on annual growth rate of GDP at factor cost of hospitality

industry is given by the regression equation y=18.96734 - 0.25477x. The hypothesis tested with


H0: b = 0 (no influence of diesel prices on annual growth rate of GDP at factor cost of hospitality

industry)

Ha: b 0(influence of diesel prices on annual growth rate of GDP at factor cost of hospitality

industry)



diesel. As this is very small therefore the regression outcomes cannot be relied upon.





be rejected. Here the null hypothesis is accepted and that means that price of diesel has no

significant influence on the annual growth rate of GDP at factor cost of hospitality

industry. (p-value= 0.435811)

ANNEXURE 45

The influence of price of crude oil on annual growth rate of GDP at factor cost of hospitality

industry is given by the regression equation y=12.08683 -0.0221x. The hypothesis tested with


H0: b = 0 (no influence of crude oil prices on annual growth rate of GDP at factor cost of


Ha: b 0(influence of crude oil prices on annual growth rate of GDP at factor cost of hospitality

industry)



crude oil. As this is very small therefore the regression outcomes cannot be relied upon.





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be rejected. Here the null hypothesis is accepted and that means that price of crude oil has no


(p-value= 0.749381).

ANNEXURE 46

The influence of price of domestic LPG on annual growth rate of GDP at factor cost of

hospitality industry is given by the regression equation y=18.79772 - 0.027x. The hypothesis


H0: b = 0 (no influence of domestic LPG prices on annual growth rate of GDP at factor cost of


Ha: b 0(influence of domestic LPG prices on annual growth rate of GDP at factor cost of




domestic LPG. As this is very small therefore the regression outcomes cannot be relied upon.





be rejected. Here the null hypothesis is accepted and that means that price of domestic LPG has

no significant influence on the annual growth rate of GDP at factor cost of hospitality

industry (p-value= 0.532874).

ANNEXURE 47

The influence of EXCHANGE RATE USD/RS on annual growth rate of GDP at factor cost of

hospitality industry is given by the regression equation y=41.72358-0.67875x. The hypothesis


H0: b = 0 (no influence of EXCHANGE RATE USD/RS on annual growth rate of GDP at factor

cost of hospitality industry)

Ha: b 0(influence ofEXCHANGE RATE USD/RS on annual growth rate of GDP at factor cost

of hospitality industry)


15/29


GDP at factor cost of the hospitality industry is being explained by the changes in the

EXCHANGE RATE USD/RS. As this is very small therefore the regression outcomes cannot be

relied upon. Furthermore, the adjusted R2 (0.077126) is very small and significantly different and

less than the R2. This also means that the sample size taken into consideration is inadequate to

draw any conclusion about the true relation between the chosen variables. The p value is

0.222181; this value is more than 0.05. This value of p is the level of significance at which this

hypothesis can be rejected. Here the null hypothesis is accepted and that means that EXCHANGE

RATE USD/RS has no significant influence on the annual growth rate of GDP at factor cost

of hospitality industry (p-value= 0.222181).

ANNEXURE 48

The influence ofpopulation of India on annual growth rate of GDP at factor cost of hospitality

industry is given by the regression equation y=72.37894-0.05456x. The hypothesis tested with the


H0: b = 0 (no influence of population of India on annual growth rate of GDP at factor cost of


Ha: b 0(influence of population of India on annual growth rate of GDP at factor cost of



GDP at factor cost of the hospitality industry is being explained by the changes in the population

of India. As this is very small therefore the regression outcomes cannot be relied upon.

Furthermore, the adjusted R2 (0.116968) is very small and significantly different and less than the




be rejected. Here the null hypothesis is accepted and that means that population of India has no


(p-value= 0.176987).

ANNEXURE 49


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The influence ofexchange rate sdr/rs on annual growth rate of GDP at factor cost of hospitality

industry is given by the regression equation y=53.47431-0.62418x. The hypothesis tested with the


H0: b = 0 (no influence of exchange rate sdr/rs on annual growth rate of GDP at factor cost of


Ha: b 0(influence of exchange rate sdr/rs on annual growth rate of GDP at factor cost of



GDP at factor cost of the hospitality industry is being explained by the changes in the exchange

rate sdr/rs. As this is very small therefore the regression outcomes cannot be relied upon.





be rejected. Here the null hypothesis is accepted and that means that exchange rate sdr/rs has no


(p-value= 0.105601).

ANNEXURE 50

The influence of exchange rate pound sterling on annual growth rate of GDP at factor cost of

hospitality industry is given by the regression equation y=-46.0437 + 0.718234x. The hypothesis


H0: b = 0 (no influence of exchange rate pound sterling on annual growth rate of GDP at factor


Ha: b 0(influence ofexchange rate pound sterling on annual growth rate of GDP at factor cost

of hospitality industry)


GDP at factor cost of the hospitality industry is being explained by the changes in the exchange

rate pound sterling. As this is very small therefore the regression outcomes cannot be relied upon.



17/29



value is less than 0.05. This value of p is the level of significance at which this hypothesis can be

rejected. Here the null hypothesis is rejected and that means that pound sterling has a significant

influence on the annual growth rate of GDP at factor cost of hospitality industry (p-value=

0.012349).

ANNEXURE 51

The influence ofGDP growth rate on annual growth rate of GDP at factor cost of hospitality

industry is given by the regression equation y=7.045521+ 0.44145x. The hypothesis tested with


H0: b = 0 (no influence of GDP growth rate on annual growth rate of GDP at factor cost of


Ha: b 0(influence of GDP growth rate on annual growth rate of GDP at factor cost of



GDP at factor cost of the hospitality industry is being explained by the changes in the GDP

growth rate. As this is very small therefore the regression outcomes cannot be relied upon.





be rejected. Here the null hypothesis is accepted and that means that GDP growth rate has no


(p-value= 0.613539).

ANNEXURE 52

The influence ofTOURIST ARRIVAL TO INDIA on annual growth rate of GDP at factor cost of

hospitality industry is given by the regression equation y=24.15739 - 2.99301x. The hypothesis


H0: b = 0 (no influence of TOURIST ARRIVAL TO INDIA on annual growth rate of GDP at

factor cost of hospitality industry)


18/29

Ha: b 0(influence ofTOURIST ARRIVAL TO INDIA on annual growth rate of GDP at factor



GDP at factor cost of the hospitality industry is being explained by the changes in TOURIST

ARRIVAL TO INDIA. As this is very small therefore the regression outcomes cannot be relied





be rejected. Here the null hypothesis is accepted and that means that TOURIST ARRIVAL TO

INDIA has no significant influence on the annual growth rate of GDP at factor cost of


ANNEXURE 53

The influence ofCAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN INDIA on annual

growth rate of GDP at factor cost of hospitality industry is given by the regression equation

y=9.692648- 0.015926x. The hypothesis tested with the regression equation is as follows.

H0: b = 0 (no influence of CAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN

INDIA on annual growth rate of GDP at factor cost of hospitality industry)

Ha: b 0(influence of CAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN

INDIA on annual growth rate of GDP at factor cost of hospitality industry)


GDP at factor cost of the hospitality industry is being explained by the changes in CAPITAL

OUTLAY FOR PROMOTION OF ECO TOURISM IN INDIA. As this is very small therefore the regression

outcomes cannot be relied upon. Furthermore, the adjusted R2 (-0.12137) is very small and

significantly different and less than the R2. This also means that the sample size taken intoconsideration is inadequate to draw any conclusion about the true relation between the chosen

variables. The p value is 0.876171; this value is more than 0.05. This value of p is the level of

significance at which this hypothesis can be rejected. Here the null hypothesis is accepted and

that means that CAPITAL OUTLAY FOR PROMOTION OF ECO TOURISM IN INDIA has no significant


0.876171).


19/29

ANNEXURE 54

The influence ofEXPENDITURE FOR PROMOTION OF ECO TOURISM on annual growth rate of

GDP at factor cost of hospitality industry is given by the regression equation y=16.06586-


H0: b = 0 (no influence of EXPENDITURE FOR PROMOTION OF ECO TOURISM on annual

growth rate of GDP at factor cost of hospitality industry)

Ha: b 0(influence of EXPENDITURE FOR PROMOTION OF ECO TOURISM on annual



GDP at factor cost of the hospitality industry is being explained by the changes in

EXPENDITURE FOR PROMOTION OF ECO TOURISM. As this is very small therefore the

regression outcomes cannot be relied upon. Furthermore, the adjusted R2 (0.201248) is very

small and significantly different and less than the R2. This also means that the sample size taken

into consideration is inadequate to draw any conclusion about the true relation between the

chosen variables. The p value is 0.10828; this value is more than 0.05. This value of p is the level

of significance at which this hypothesis can be rejected. Here the null hypothesis is accepted and

that means that EXPENDITURE FOR PROMOTION OF ECO TOURISM has no significant


0.10828).

ANNEXURE 55

\The influence of% SHARE OF INDIA IN WORLD TOURIST ARRIVAL on annual growth rate of

GDP at factor cost of hospitality industry is given by the regression equation y=10.87715-


H0: b = 0 (no influence of % SHARE OF INDIA IN WORLD TOURIST ARRIVAL on annual


Ha: b 0(influence of % SHARE OF INDIA IN WORLD TOURIST ARRIVAL on annual



GDP at factor cost of the hospitality industry is being explained by the changes in % SHARE OF


20/29

INDIA IN WORLD TOURIST ARRIVAL. As this is very small therefore the regression outcomes

cannot be relied upon. Furthermore, the adjusted R2 (-0.11469) is very small and significantly

different and less than the R2. This also means that the sample size taken into consideration is

inadequate to draw any conclusion about the true relation between the chosen variables. The p

value is 0.792473; this value is more than 0.05. This value of p is the level of significance at

which this hypothesis can be rejected. Here the null hypothesis is accepted and that means that %

SHARE OF INDIA IN WORLD TOURIST ARRIVAL has no significant influence on the

annual growth rate of GDP at factor cost of hospitality industry (p-value= 0.792473).

ANNEXURE 56

The influence ofWORLD TOURIST ARRIVALS on annual growth rate of GDP at factor cost of

hospitality industry is given by the regression equation y=23.64968-0.01495x. The hypothesis


H0: b = 0 (no influence of WORLD TOURIST ARRIVALS on annual growth rate of GDP at


Ha: b 0(influence ofWORLD TOURIST ARRIVALS on annual growth rate of GDP at factor



GDP at factor cost of the hospitality industry is being explained by the changes in WORLD

TOURIST ARRIVALS. As this is very small therefore the regression outcomes cannot be relied

upon. Furthermore, the adjusted R2 (-0.05097) is very small and significantly different and less




be rejected. Here the null hypothesis is accepted and that means that WORLD TOURIST

ARRIVALS has no significant influence on the annual growth rate of GDP at factor cost of


ANNEXURE 57(ask)

The influence ofDOMESTIC TOURIST VISITS on annual growth rate of GDP at factor cost of

hospitality industry is given by the regression equation y=27.76235128-3.38102E-08x. The

hypothesis tested with the regression equation is as follows.


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no significant influence on the annual growth rate of GDP at factor cost of hospitality

industry (p-value= 0.102307).

ANNEXURE 59

The influence of ASSISTANCE SANCTIONED BY TFCI (TOURISM FINANCE

CORPORATION OF INDIA) on annual growth rate of GDP at factor cost of hospitality industry

is given by the regression equation y=16.24532-0.00025x. The hypothesis tested with the


H0: b = 0 (no influence of ASSISTANCE SANCTIONED BY TFCI on annual growth rate of

GDP at factor cost of hospitality industry)

Ha: b 0(influence ofASSISTANCE SANCTIONED BY TFCI on annual growth rate of GDP

at factor cost of hospitality industry)


GDP at factor cost of the hospitality industry is being explained by the changes in ASSISTANCE

SANCTIONED BY TFCI. As this is very small therefore the regression outcomes cannot be relied

upon. Furthermore, the adjusted R2 (0.627146) is small and different and less than the R2. This

also means that the sample size taken into consideration is somewhat inadequate to draw any



rejected. Here the null hypothesis is rejected and that means that ASSISTANCE SANCTIONED

BY TFCI has a significant influence on the annual growth rate of GDP at factor cost of


ANNEXURE 60

The influence ofASSISTANCE DISBURSED BY TFCI (TOURISM FINANCE CORPORATION

OF INDIA) on annual growth rate of GDP at factor cost of hospitality industry is given by the

regression equation y=16.49852-0. 00049x. The hypothesis tested with the regression equation is

as follows.

H0: b = 0 (no influence of ASSISTANCE DISBURSED BY TFCI on annual growth rate of GDP

at factor cost of hospitality industry)


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Ha: b 0(influence ofASSISTANCE DISBURSED BY TFCI on annual growth rate of GDP at



GDP at factor cost of the hospitality industry is being explained by the changes in ASSISTANCE

SANCTIONED BY TFCI. As this is very small therefore the regression outcomes cannot be relied

upon. Furthermore, the adjusted R2 (0.559852) is small and different and less than the R2. This

also means that the sample size taken into consideration is somewhat inadequate to draw any



rejected. Here the null hypothesis is rejected and that means that ASSISTANCE DISBURSED BY

TFCI has a significant influence on the annual growth rate of GDP at factor cost of

hospitality industry (p-value=0.01989).

ANNEXURE 61

The influence of NUMBER OF PROJECTS SANCTIONED BY MTI (MINISTRY OF TRADE

AND INDUSTRY)on annual growth rate of GDP at factor cost of hospitality industry is given

by the regression equation y=11.90195-0.02569x. The hypothesis tested with the regression


H0: b = 0 (no influence of NUMBER OF PROJECTS SANCTIONED BY MTI on annualgrowth rate of GDP at factor cost of hospitality industry)

Ha: b 0(influence ofNUMBER OF PROJECTS SANCTIONED BY MTI on annual growth

rate of GDP at factor cost of hospitality industry)


GDP at factor cost of the hospitality industry is being explained by the changes in NUMBER OF

PROJECTS SANCTIONED BY MTI. As this is very small therefore the regression outcomescannot be relied upon. Furthermore, the adjusted R2 (-0.24113) is very small and significantly


somewhat inadequate to draw any conclusion about the true relation between the chosen

variables. The p value is 0.669073; this value is more than 0.05. This value of p is the level of

significance at which this hypothesis can be rejected. Here the null hypothesis is accepted and

that means that NUMBER OF PROJECTS SANCTIONED BY MTI has no significant influence

on the annual growth rate of GDP at factor cost of hospitality industry (p-value=0.669073).


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ANNEXURE 62

The influence of AMOUNT SANCTIONED BY MTI (MINISTRY OF TRADE AND

INDUSTRY)on annual growth rate of GDP at factor cost of hospitality industry is given by the

regression equation y=22.95406-0.02304x. The hypothesis tested with the regression equation is

as follows.

H0: b = 0 (no influence of AMOUNT SANCTIONED BY MTI on annual growth rate of GDP at


Ha: b 0(influence of AMOUNT SANCTIONED BY MTI on annual growth rate of GDP at



GDP at factor cost of the hospitality industry is being explained by the changes in NUMBER OF

PROJECTS SANCTIONED BY MTI. As this is very small therefore the regression outcomes

cannot be relied upon. Furthermore, the adjusted R2 (0.213699) is very small and significantly


inadequate to draw any conclusion about the true relation between the chosen variables. The p

value is 0.244289; this value is more than 0.05. This value of p is the level of significance at

which this hypothesis can be rejected. Here the null hypothesis is accepted and that means that

AMOUNT SANCTIONED BY MTI has no significant influence on the annual growth rate of

GDP at factor cost of hospitality industry (p-value=0.244289).

RESEARCH METHODOLOGY DRAFT

Research Methodology is an overall framework of the project that stipulates what information is

to be collected from which source by what procedures. If descriptive information is needed, then

a quantitative study is likely to be needed. Quantitative Research is based on measurement of

quantity or amount, and Qualitative Research is concerned with qualitative phenomenon, i.e.,

phenomena relating to quality or kind. The data collected here is quantitative data relating to the

graphs and the statistics required to analyze the data. The choice of data collection methods for

this study includes secondary data from websites, reports, books and journals available in the

Learning Resource Centre of our college.


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Various secondary data have been found and from these DATA, data analysis has been done.

Charts and graphs have been used in order to analyze the data. Regression has already been used

to find out the link between various variables of the hospitality industry. Hypothesis is made in

order to examine the effect of one variable over others. Due to paucity of time, secondary data is

mainly used for this Project Report, and this research is of a quantitative nature.

The various methods used by me are:

Secondary data: Published data and the data collected in the past or other parties is

called secondary data. Secondary data is data that has already been collected by someone

else for a different purpose to yours.

The methods for collection are:

Internet

Magazines

CMIE

3.3 TOOLS RELATED TO RESEARCH METHODOLOGY:

Computation tools

Statistical tools

COMPUTATION TOOLS USED

o Microsoft Excel

o Microsoft word

STATISTICAL TOOLS USED

CORRELATION: Degree and type of relationship between any two or more quantities

(variables) in which they vary together over a period;. A positive correlation exists where the

high values of one variable are associated with the high values of the other variable(s). A

'negative correlation' means association of high values of one with the low values of the other(s).

Correlation can vary from +1 to -1. Values close to +1 indicate a high-degree of positive

correlation, and values close to -1 indicate a high degree of negative correlation. Values close to

zero indicate poor correlation of either kind, and 0 indicates no correlation at all.

Given below is the formula by Karl Pearson for sample correlation coefficient, commonly

denoted r:
http://www.businessdictionary.com/definition/party.htmlhttp://www.businessdictionary.com/definition/secondary-data.htmlhttp://www.businessdictionary.com/definition/degree.htmlhttp://www.businessdictionary.com/definition/quantity.htmlhttp://www.businessdictionary.com/definition/variable.htmlhttp://www.investorwords.com/3669/period.htmlhttp://www.businessdictionary.com/definition/positive-correlation.htmlhttp://www.investorwords.com/2306/high.htmlhttp://www.businessdictionary.com/definition/values.htmlhttp://www.businessdictionary.com/definition/associated.htmlhttp://www.businessdictionary.com/definition/negative-correlation.htmlhttp://www.businessdictionary.com/definition/mean.htmlhttp://www.businessdictionary.com/definition/association.htmlhttp://www.investorwords.com/2900/low.htmlhttp://www.investorwords.com/2900/low.htmlhttp://www.businessdictionary.com/definition/association.htmlhttp://www.businessdictionary.com/definition/mean.htmlhttp://www.businessdictionary.com/definition/negative-correlation.htmlhttp://www.businessdictionary.com/definition/associated.htmlhttp://www.businessdictionary.com/definition/values.htmlhttp://www.investorwords.com/2306/high.htmlhttp://www.businessdictionary.com/definition/positive-correlation.htmlhttp://www.investorwords.com/3669/period.htmlhttp://www.businessdictionary.com/definition/variable.htmlhttp://www.businessdictionary.com/definition/quantity.htmlhttp://www.businessdictionary.com/definition/degree.htmlhttp://www.businessdictionary.com/definition/secondary-data.htmlhttp://www.businessdictionary.com/definition/party.html


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REGRESSION: A statistical measure that attempts to determine the strength of the relationship

between one dependent variable (usually denoted by Y) and a series of other changing variables

(known as independent variables). The two basic types of regression are linear regression

and multiple regressions. Linear regression uses one independent variable to explain and/or

predict the outcome of Y, while multiple regressions use two or more independent variables

to predict the outcome. The general form of each type of regression is:

Linear Regression: Y = a + bX + u

Where:

Y= the variable that we are trying to predict (dependant variable)

X= the variable that we are using to predict Y (independent variable)

a= the intercept

b= the slope

u= the regression residual.

DEPENDENT VARIABLE: A dependent variable is a variable dependent on another variable:

the independent variable. In simple terms, the independent variable is said to cause an apparent

change in, or simply affect, the dependent variable.

INDEPENDENT VARIABLE: An independent variable is a factor that can be varied or

manipulated in an experiment (e.g. time, temperature, concentration, etc). It is usually what will

affect the dependent variable.

There are two types of independent variables, which are often treated differently in statistical

analyses: (1) quantitative variables that differ in amounts or scale and can be ordered (e.g.

weight, temperature, time). (2) Qualitative variables which differ in "types" and can not be

ordered (e.g. gender, species, method). By convention when graphing data, the independent

variable is plotted along the horizontal X-axis with the dependent variable on the vertical Y-axis.

INTERCEPT: Thex-intercepts are where the graph crosses thex-axis, and they-intercepts are

where the graph crosses the they-axis. Then, algebraically,

anx-intercept is a point on the graph wherey is zero, and

ay-intercept is a point on the graph wherex is zero.

More specifically,


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anx-intercept is a point in the equation where the y-value is zero, and

ay-intercept is a point in the equation where thex-value is zero.

CO-EFFICIENT: A coefficient is a number in front of a variable. For example, in the

expression x2-10x+25, the coefficient of the x

2is 1 and the coefficient of the x is -10. The third

term, 25, is called a constant.

If the expression were -x2+10x+25, the coefficient of the x2 would be -1, and the coefficient of

the x would be 10.

P-Value: P value is associated with a test statistic. It is "the probability, if the test statistic really

were distributed as it would be under the null hypothesis, of observing a test statistic [as extreme

as, or more extreme than] the one actually observed. The smaller the P value, the more st rongly

the test rejects the null hypothesis, that is, the hypothesis being tested. A p-value of .05 or less

rejects the null hypothesis "at the 5% level" that is, the statistical assumptions used imply that

only 5% of the time would the supposed statistical process produce a finding this extreme if the

null hypothesis were true. Consider an experiment where you've measured values in two

samples, and the means are different. How sure are you that the population means are different as

well? There are two possibilities:

The populations have different means.

The populations have the same mean, and the difference you observed is a coincidence of

random sampling.

The P value is a probability, with a value ranging from zero to one. It is the answer to this

question: If the populations really have the same mean overall, what is the probability

that random sampling would lead to a difference between sample means as large (or

larger) than we observed?

What is R2 : R-Square is the proportion of variation in the dependent variable (Y)

that can be explained by the predictors (X variables) in the regression model. As predictors (X

variables) are added to the model, each predictor will explain some of the variance in the

dependent variable (Y) simply due to chance. One could continue to add predictors to the model

which would

continue to improve the ability of the predictors to explain the dependent variable, although

some of this increase in R-Square would be simply due to chance variation. There are several


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different definitions of R2 which are only sometimes equivalent. One class of such cases

includes that oflinear regression. In this case, R2 is simply the square of the sample correlation

coefficient between the outcomes and their predicted values, or in the case of simple linear

regression, between the outcome and the values being used for prediction. In such cases, the

values vary from 0 to 1. Important cases where the computational definition of R2 can yield

negative values, depending on the definition used, arise where the predictions which are being

compared to the corresponding outcome have not derived from a model-fitting procedure using

those data.

Adjusted R2: The Adjusted R_Square value is an attempt to correct this shortcoming by

adjusting both the numerator and the denominator by their respective degrees of freedom.

Unlike the R_Square, the Adjusted R_Square can decline in value if the contribution to the

explained deviation by the additional variable is less than the impact on the degrees of

freedom. This means that the Adjusted R_Square will react to alternative equations for the

same dependent variable in a manner similar to the Standard Error of the Estimate; i.e., the

equation with the smallest Standard Error of the Estimate will most likely also have the

highest Adjusted R square.

CONFIDENCE LEVEL: Statistical measure of the number of times out of 100 that test

results can be expected to be within a specified range. For example, a confidence level of

95% means that the result of an action will probably meet expectations 95% of the time.

Most analyses of variance or correlation are described in terms of some level of confidence.

HYPOTHESES: Hypotheses may be defined as a set of proposition set forth as an explanation

for the occurrence of some specified group of phenomenon either asserted merely as a

provisional conjecture to guide some investigation or accepted as highly probable in the light of

established facts. A hypothesis should be clear and precise and also be capable of being tested. It

should state relationship between variables and must be consistent with a substantial body of

established facts. A researcher must remember those narrower hypotheses are generally moretestable and acceptable.

Null hypotheses: typically proposes a general or default position, such as that there is

no relationship between two measured phenomena or that a potential treatment has no

effect. Hypothesis testing works by collecting data and measuring how probable the data

are, assuming the null hypothesis is true. If the data are very improbable (usually defined

as observed less than 5% of the time), then the experimenter concludes that the null
http://en.wikipedia.org/wiki/Linear_regressionhttp://en.wikipedia.org/wiki/Linear_regression


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hypothesis is false. If the data do not contradict the null hypothesis, then no conclusion is

made. In this case, the null hypothesis could be true or false; the data give insufficient

evidence to make any conclusion. For instance, a certain drug may reduce the chance of

having a heart attack. Possible null hypotheses are "this drug does not reduce the chances

of having a heart attack" and "this drug has no effect on the chances of having a heart

attack".

Alternative hypothesis:the hypothesis to be accepted if the null hypothesis is rejected. It

is compared with the null hypothesis in a statistical test. It is usually the one which one

wishes to prove whereas null hypothesis is usually the one which one wishes to

disapprove.

SOURCE:

1. http://www.businessdictionary.com/definition/correlation.html#ixzz143utZ

Zyw2. http://www.investorwords.com/4137/regression_equation.html

3. http://cnx.org/content/m13446/latest/

4. http://cnx.org/content/m13448/latest/

5. http://www.purplemath.com/modules/intrcept.htm

6. http://www.ask.com/wiki/Coefficient

7. http://www.answers.com/topic/confidence-level

8. http://www.nullhypothesis.co.uk/science/item/what_is_a_nullhypothesis

9. http://www.wisegeek.com/what-is-an-alternative-hypothesis.htm
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