4.11 Multiple Regression Analysis: Analysis Of Multiple Regression Analysis
4.11 Multiple Regression Analysis
For hypotheses testing of this study, multiple regression analysis was conducted. Some assumptions of the relationship between dependent and independent variables need to be met for performing multiple regression analysis like, normality, linearity, homoscedasticity and multicollinearity (Hair et al., 1998). As
mentioned earlier, the required assumptions have already been met and multiple regression analysis was appropriate.
Usually, multiple regression analyses are performed to interpret the relationship between one dependent variable (outcome) and various independent variables (predictor). Multiple regression analysis can be done in three ways such as standard regression, hierarchical …show more content…
Standardized beta weights were used for discussion of statistical significance of independent variables in OLS regression, as they allow for comparisons of variables with different metrics or when metrics are arbitrary, as in Likert-type scales (Menard 2002). The size of beta weights was also examined to determine which variables had the strongest predictive value. The independent variable with largest beta weight, after controlling for all other variables, has the largest unique explanatory effect on the dependent variable for a standard unit increase in the independent variable. Regression results were examined at 0.5 levels of significance to determine whether the relationship between dependent and independent variables was statistically …show more content…
Dependent Variable: Local residents’ attitude towards tourism impact
4.11.2.1 Evaluating the model
ANOVA was performed to assume the statistical significance of the result provided in Table 4.31. The result shows that the F-ratio is 169.625 (P=.000), which statistically supports the significance of the model. Thus, the hypotheses are accepted since the model of this study is statistically significant at p= 000. The adjusted coefficient of determination (R Squared) implies that 69.1% of the variation in the level of local residents’ attitude towards tourism i mpact can be explained by the variations in the whole set of independent variables of the proposed model in the present study.
4.11.2.2 Evaluating Independent Variables
The levels of influences the independent variables have on the dependent variable are going to be discussed in this section. In brief, this study wants to identify which variables in the model have the most significant influence on the dependent variable through Beta value.
As indicated in the table 4.31, the coefficients of the independent variables show the direction and the magnitude of the relationship with the dependent
For hypotheses testing of this study, multiple regression analysis was conducted. Some assumptions of the relationship between dependent and independent variables need to be met for performing multiple regression analysis like, normality, linearity, homoscedasticity and multicollinearity (Hair et al., 1998). As
mentioned earlier, the required assumptions have already been met and multiple regression analysis was appropriate.
Usually, multiple regression analyses are performed to interpret the relationship between one dependent variable (outcome) and various independent variables (predictor). Multiple regression analysis can be done in three ways such as standard regression, hierarchical …show more content…
Standardized beta weights were used for discussion of statistical significance of independent variables in OLS regression, as they allow for comparisons of variables with different metrics or when metrics are arbitrary, as in Likert-type scales (Menard 2002). The size of beta weights was also examined to determine which variables had the strongest predictive value. The independent variable with largest beta weight, after controlling for all other variables, has the largest unique explanatory effect on the dependent variable for a standard unit increase in the independent variable. Regression results were examined at 0.5 levels of significance to determine whether the relationship between dependent and independent variables was statistically …show more content…
Dependent Variable: Local residents’ attitude towards tourism impact
4.11.2.1 Evaluating the model
ANOVA was performed to assume the statistical significance of the result provided in Table 4.31. The result shows that the F-ratio is 169.625 (P=.000), which statistically supports the significance of the model. Thus, the hypotheses are accepted since the model of this study is statistically significant at p= 000. The adjusted coefficient of determination (R Squared) implies that 69.1% of the variation in the level of local residents’ attitude towards tourism i mpact can be explained by the variations in the whole set of independent variables of the proposed model in the present study.
4.11.2.2 Evaluating Independent Variables
The levels of influences the independent variables have on the dependent variable are going to be discussed in this section. In brief, this study wants to identify which variables in the model have the most significant influence on the dependent variable through Beta value.
As indicated in the table 4.31, the coefficients of the independent variables show the direction and the magnitude of the relationship with the dependent