(a) By looking at the summary output, write down the fitted model. (b) Write down the formula to compute the 95% confidence interval for 3₁. (c) Compute the 95% confidence interval for ₁.
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- In terms of probability and consequence, it is unclear why intervals rather than precise numbers are preferred for risk assessments.Minimum support=3 minimum confidence=60 %I need to know the distinction between a casual analysis, a statistical analysis, and a Pareto analysis. Among these, which best describes the ODC method?
- Briefly explain why students doing this assignment will not all get the same bootstrap confidence interval.Which model coverage is the best compromise between effort and percentage of detected failures? Justify.Instead of exact statistics based on past data, risk calculation use a "informed estimate" based on observation. (To assist the auto-grader, type your response in ALL CAPS or ALL LOWERCASE.)
- Reasoning for the preference for risk analysis intervals rather than specific numbers in terms of chance and consequence is unclear.Why is RMSE generally the preferred performance measure for regression tasks? A. Because it gives an idea of the average percentage deviation of the predictions from the actual values. B. Because it gives an idea of the average absolute deviation of the predictions from the actual values. C. Because it gives an idea of how much error the system typically makes in its predictions, with a higher weight given to large errors. D. Because it gives an idea of the average deviation of the predictions from the actual values.7. Give an example of how ordinal data may be gathered during a usability test.
- The ROC curve graphically shows the tradeoff between Question 6 options: The true positive rate and the false positive rate Class 1 and Class 0 observations Sensitivity and specificity Overall accuracy and overall errorDo you think this graphical error was intentional? What reasons do you think contributed to this misleading statistic?Plot following curves in the SAME figure where x-axis is "Model Complexity" and y-axis is the value: (1) Bias; (2) Variance; (3) Training Accuracy; (4) Testing Accuracy; [Paste your plot here]