The output sheet of regression analysis using SPSS contains various tables and statistics that can be overwhelming to a beginner. However, once you understand the purpose of each table, it becomes much easier to interpret the results. Let us discuss each table in detail.
Step 2: Model Summary
The model summary table provides a summary of the regression model and includes important statistics such as R, R square, and Adjusted R square.
R: “R represents the correlation coefficient between the dependent variable and independent variables. It measures the strength of the relationship between the variables and ranges from -1 to 1. A high value of R indicates a strong relationship between the variables, while a low value indicates a weak relationship”
R square: “R square represents the proportion of variance in the dependent variable that can be explained by the independent variables. It ranges from 0 to 1 and indicates how much of the variance in the dependent variable is explained by the independent variables”
Adjusted R square: “Adjusted R square takes into account the number of independent variables in the model and adjusts R square accordingly. It gives us a more accurate representation of how much of the variance in the dependent variable is explained by the independent variables”
Step 3: ANOVA
The ANOVA table provides us with the analysis of variance between the dependent and independent variables. It includes statistics such as the Regression Sum of Squares (SS), Residual Sum of Squares (SS), and Total Sum of Squares (SS).
Step 4: Coefficients Table
“The coefficients table provides us with the coefficients of the independent variables and the constant term. The coefficients represent the change in the dependent variable for a unit change in the independent variable while holding all other variables constant. The coefficients table also includes the standard error, t-value, and significance level (p-value) of each independent variable.”
Step 5: Residuals Plots
The residuals plots are important for checking the assumptions of regression analysis. The residuals plot shows us the difference between the observed and predicted values of the dependent variable. We want the residuals to be randomly distributed around zero, with no pattern or trend. If we observe any pattern or trend in the residuals plot, it may indicate that our regression model is not adequate.
Step 6: Conclusion
Interpreting the output sheet of regression analysis using SPSS can be intimidating, but once you understand each table and statistic, it becomes much easier. We have provided a step by step guide to help you interpret the output sheet, and we hope that this guide has been helpful. Remember to always check the assumptions of regression analysis, and to seek help if you are unsure about any aspect of the output.
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