There are several decision support tools commonly used in regression analysis. Here are a few examples:
1. Scatter plots: Scatter plots are used to visualize the relationship between two variables in a regression analysis. They help identify patterns, trends, and potential outliers in the data.
2. Residual plots: Residual plots are used to analyze the residuals (or errors) of a regression model. They help assess the model's assumptions, such as linearity, homoscedasticity, and independence of errors.
3. Cook's distance: Cook's distance is a measure used to identify influential data points in a regression analysis. It helps identify observations that have a significant impact on the regression coefficients when removed from the analysis.
4. Variance inflation factor (VIF): VIF is used to assess multicollinearity in a regression model. It measures how much the variance of the estimated regression coefficients is increased due to multicollinearity.
5. Akaike information criterion (AIC) and Bayesian information criterion (BIC): AIC and BIC are used for model selection in regression analysis. They provide a way to compare different regression models and select the one that best balances goodness of fit and model complexity.
6. Cross-validation: Cross-validation is a technique used to assess the performance of a regression model. It involves splitting the data into training and testing sets, fitting the model on the training set, and evaluating its performance on the testing set.
These are just a few examples of decision support tools used in regression analysis. The choice of tools depends on the specific objectives and requirements of the analysis.