What does linear regression output mean? Really, i have coefficients in my SPSS output, but i have no idea what they mean. I need to build a regression equation, but how???
Regression analysis is one of the statistical tools under SPSS research methods for determining whether or not there is a linear relationship between your dependent and independent variables. The regression coefficients tell you the direction and the magnitude of the relationship between your dependent and independent variables. The sign of the coefficients indicates whether the independent variable is directly (i.e. as the value of the independent variable increases, the value of the dependent variable also increases assuming all other variables in the model as constant) or inversely (i.e. as the value of the independent variable increases, the value of the dependent variable decreases assuming all other variables in the model as constant). The absolute value of the regression coefficients, on the other hand, indicates the amount of change in the dependent variable per unit change in your independent variable assuming all the other variables included in the regression model as constant. For example, a 0.43 beta coefficient for an independent variable X suggests that, if all the other variables in model are held constant, there would be a 0.43 increase in dependent variable Y for every unit increase in the independent variable. Lastly, the corresponding p-value (i.e. the Sig value) for the t-tests on the significance of each of the independent variables in the model should be less than or equal to 0.05 (or 0.1 depending on the desired significance level) to conclude that there is enough evidence to say that the impact of independent variable to the dependent variable is significant at 95% confidence level.
Regression analysis is one of the statistical tools under SPSS research methods for determining whether or not there is a linear relationship between your dependent and independent variables. The regression coefficients tell you the direction and the magnitude of the relationship between your dependent and independent variables. The sign of the coefficients indicates whether the independent variable is directly (i.e. as the value of the independent variable increases, the value of the dependent variable also increases assuming all other variables in the model as constant) or inversely (i.e. as the value of the independent variable increases, the value of the dependent variable decreases assuming all other variables in the model as constant). The absolute value of the regression coefficients, on the other hand, indicates the amount of change in the dependent variable per unit change in your independent variable assuming all the other variables included in the regression model as constant. For example, a 0.43 beta coefficient for an independent variable X suggests that, if all the other variables in model are held constant, there would be a 0.43 increase in dependent variable Y for every unit increase in the independent variable. Lastly, the corresponding p-value (i.e. the Sig value) for the t-tests on the significance of each of the independent variables in the model should be less than or equal to 0.05 (or 0.1 depending on the desired significance level) to conclude that there is enough evidence to say that the impact of independent variable to the dependent variable is significant at 95% confidence level.