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	.687a	.635	.417	21.285
a. Predictors : (Constant), Internet , Openness, Growth, Price
b. Dependent variable: Tourists

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

Coefficientsa
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.