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(2)Why should we measure domestic tourism?

Abstract: This article uses Stata software to establish a multivariate linear model, selecting three indicators: the number of travel agencies, urban residents’ disposable income, and gross domestic product as independent variables, and domestic tourism income as the dependent variable, and then Conduct multiple *** linearity tests and heteroscedasticity tests on the model to obtain the model regression model with the highest degree of fit, and make relevant suggestions.

Keywords: Stata software; domestic tourism revenue; multivariate linear model; heteroskedasticity

1. Theoretical model

Due to the easy availability of data To consider, we select domestic tourism revenue as an indicator to measure the development of my country's domestic tourism industry and as the explained variable. The development of domestic tourism is limited by people's living standards. Urban residents account for the majority of the number of tourists. Therefore, the disposable income of urban residents is used as an explanatory variable; gross domestic product (GDP) is an important measure of the level of social and economic development. Indicator, it can measure the development and construction perfection of our country. Here, GDP is used as another explanatory variable to reflect changes in the general economic environment. Finally, the number of travel agencies also has an important impact on people’s travel, so it is used as the final indicator. an explanatory variable.

2. Econometric model setting and sample data description

(1) Econometric model:

1. In order to facilitate research, the regression model is set to linear: Y=bb1X1+b2X2+b3X3.

2. Eliminate the influence of multi-linearity, the regression model is: Y=bb1X1+b2X2.

(2) Data description:

X1 - Number of travel agencies; X2 - Disposable income of urban residents; X3 - Gross domestic product; Y - Domestic tourism revenue data source: China Statistical Yearbook