Unit 6 Model Analysis Key Terms Review

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Across
  1. 6. Used for visualizing regression results, residual plots, and diagnosing model performance.
  2. 7. Model is too simple, fails to capture data patterns.
  3. 10. Used for numerical operations, handling arrays, and performing matrix calculations.
  4. 12. A condition where residual variance changes across values of the independent variable, indicating a potential issue in the model.
  5. 13. Informal term for a model that fits the data well
  6. 16. A modified version of R² that accounts for the number of independent variables in the model.
  7. 17. A condition where residuals have constant variance; a key assumption of linear regression.
Down
  1. 1. Measures how well the regression model explains variability in the dependent variable.
  2. 2. The difference between actual and predicted values; helps assess model accuracy.
  3. 3. The constant term in the regression equation, representing the predicted value when all independent variables are zero.
  4. 4. The weight assigned to each independent variable, showing its impact on the dependent variable.
  5. 5. Provides the LinearRegression model for fitting linear and multiple linear regression.
  6. 8. Useful for detailed statistical analysis, such as checking p-values, R², and performing residual analysis
  7. 9. Model fits training data too closely, performs poorly on new data.
  8. 11. A statistical measure that helps determine if a feature significantly contributes to the prediction.
  9. 14. Used for data manipulation, loading datasets, and feature selection.
  10. 15. residuals Total difference between actual and predicted values (ideally near zero).