Question

After Building a GLM Regression Model, what are some ways (steps) to validate the model?

Answer 1

After building a GLM regression model, there are several ways to validate the model, including gathering data to test predictions, evaluating the model's representation of the real world, calculating the coefficient of determination, analyzing the regression equation's slope, estimating values, and detecting outliers.

Step-by-step explanation:

After building a GLM regression model, there are several ways to validate the model:

1. Gather Data to Test Predictions: Collect relevant data from the literature, new observations, or formal experiments, and replicate the testing to verify results.
2. Evaluate the Model: Assess the model based on how well it represents the real world, its limitations, and its usefulness. Compare the model's predictions with observations of the real world.
3. Calculate Coefficient of Determination: Determine the coefficient of determination to measure the proportion of the variance in the dependent variable that can be explained by the independent variables.
4. Analyze the Slope of the Regression Equation: Interpret the slope of the regression equation to understand the relationship between the independent and dependent variables.
5. Estimate Values: Use the line of best fit to estimate values for specific time points or data points.
6. Detect Outliers: Determine if there are any outliers that may affect the model's performance.