MULTIPLE LINEAR REGRESSION A survey was carried out in 79 developed countries in order to determine the factors that would explain tourism (number of tourists received by each) The factors explaining tourism are economic growth (Growth), price level (Price), degree of openness (Openness), and number of people using the internet (Internet) Model Summary Model R R square Adjusted R square Std error of the estimate 1 687 a 635 417 21 285 a Predictors (Constant), Internet , Openness, Growth, Price b Dependent variable Tourists ANOVA a Model Sum of squares df Mean squares F Sig 1 Regression 1217 405 4 304 351 67 2 014 b Residual 33525 982 74 453 054 Total 34743 387 78 a Dependent variable Tourists b Predictors (Constant), Internet , Openness, Growth, Price Coefficients a Model Unstandardized coefficients Standardized coefficients t Sig 95 confidence interval for B Collinearity statistics B Standard error Beta Lower bound Upper bound Tolerance VIF 1 (Constant) 60 707 32 840 1 849 069 4 728 126 142 Price 199 1 515 016 131 006 3 218 2 820 007 4 102 Growth 731 1 042 082 701 005 2 806 1 345 002 4 051 Openness 023 035 075 648 519 047 092 002 4 029 Internet 516 355 174 1 455 010 1 223 191 001 4 098 a Dependent variable Tourists What does R square represent Interpret it What does the Fisher (F) statistic represent Interpret it Which variable does not significantly explain the variable Tourists Interpret the significant coefficients of the regression Do we have reason to believe that multicollinearity is an issue that affects the results of this multiple regression Explain why or why not

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MULTIPLE LINEAR REGRESSION A survey was carried out in 79 developed countries in order to determine the factors that would explain tourism (number of tourists

MULTIPLE LINEAR REGRESSION

A survey was carried out in 79 developed countries in order to determine the factors that would explain tourism (number of tourists received by each).The factors explaining tourism are economic growth (Growth), price level (Price), degree of openness (Openness), and number of people using the internet (Internet).

Model Summary
Model	R	R-square	Adjusted R-square	Std error of the estimate
1	.687^a	.635	.417	21.285
a. Predictors : (Constant), Internet , Openness, Growth, Price
b. Dependent variable: Tourists

ANOVA^a
Model	Sum of squares	df	Mean squares	F	Sig.
1	Regression	1217.405	4	304.351	67.2	.014^b
Residual	33525.982	74	453.054
Total	34743.387	78
a. Dependent variable : Tourists
b. Predictors : (Constant), Internet , Openness, Growth, Price

Coefficients^a
Model	Unstandardized coefficients	Standardized coefficients	t	Sig.	95% confidence interval for B	Collinearity statistics
B	Standard error	Beta	Lower bound	Upper bound	Tolerance	VIF
1	(Constant)	60.707	32.840		1.849	.069	-4.728	126.142
Price	.199	1.515	.016	.131	.006	-3.218	2.820	.007	4.102
Growth	.731	1.042	.082	.701	.005	-2.806	1.345	.002	4.051
Openness	.023	.035	.075	.648	.519	-.047	.092	.002	4.029
Internet	.516	.355	.174	1.455	.010	-1.223	.191	.001	4.098
a. Dependent variable : Tourists