\fQuiz: Midterm 2 - PASSWORD EN X + structure.com/courses/3320095/quizzes/8249580/take Question 28 1 pts How will you classify auction #1 in the dataset based on the above probability (assuming a default cut-off, i.e., cut-off=0.5)? Auction #1 refers to the first observation in Question 17 (ProjectID=1). O Awarded O Cannot be determined O Not awarded Question 29 1 pts m DELL\fz: Midterm 2 - PASSWORD El X + ucture.com/courses/3320095/quizzes/8249580/take 2754 598 21.71 3319 673 20.28 Overall 6073 1271 20.93 Validation Data scoring - Summary Report Cut off Prob. Val. for Success (Updatable) Classification Confusion Matrix Predicted Class Actual Class 1329 332 413 1570 Error Report Class # Cases # Errors % Error 1661 332 19.99 1983 413 20.83 Overall 3644 745 20.44 Output from Logistic Regression with 2 predictors The Regression Model Input variables Coefficient Sid. Emon p value Odds Constant term -1.57911074 0.17917429 Residual of 6070 ngDuration 0.46244276 0.033046/2 0.U29/4346 Realdual Dev. 5185.016406 Log% BuyerAwarded 7.94765806 0.21624994 2828 34165 Success In training data 45.3482628 # Iterations used Multiple R-squared 0 38015598 Training Data scoring - Summary Report Cut off Prob. Val. for Success (Updatable) Classification Confusion Matrix Predicted Class Actual Class 2179 575 688 2631 Error Report Class # Cases # Errors % Error 2154 576 20 88 3319 688 20.7 Overall 6073 1263 20.80 Validation Data scoring - Summary Report Cut off Prob Val for Success (Updatable) 1 0.5 Classification Confusion Matrix Predicted Class Actual Class 1343 318\fQuiz: Midterm 2 - PASSWORD EI X structure.com/courses/3320095/quizzes/8249580/take For questions 24 - 29 consider the same case study description and data as before, only the modeling details are different as shown before. Logistic Regression Exercise Modeling Two classifiers were fit to the data (all with cutoff = 0.5 and success class = "yes") (a) Logistic regression with all 8 predictors, and (b) Logistic regression with only 2 predictors Output is listed below. Output from Logistic Regression with all 8 predictors The Regression Model Input variables Coefficient Sta. Error p-Value Odds Constant term -1.376019 0.207 76069 Realdual of GOG4 TypeSelect -0.31873995 0.09538124 0.00083253 0.72706461 Residual Dev. 5103.298828 InvitedSuppliers 0.22390178 0.07342895 0.00229429 1.25094819 % Success in training data 45.3482628 AttachedFile 0.22084135 0.08915953 0.01325202 1.24712551 * Iterations used 9 DescFlag 0. 13870492 0.077967 28 0 07523724 1.14878511 Multiple R-squared D.39001903 BudgetCategory_NUMERIC 0 19340988 0.07073542 0.00625183 1.21337998 LogDuration -D.45363587 0 03381945 0 63531405 LogBuyerAuctions -0.21 144222 0.03394548 0.80941606 Log% BuyerAwarded 8:04864597 0.21763757 3129.554685 Training Data scoring - Summary Report Cut off Prob Val, for Success (Updatable) 0. 5 Classification Confusion Matrix Predicted Class Actual Class 2156 598 673 2646 Error Report Class " Cases # Errors % Error 2754 598 21.71 3319 673 20 28 Overall 6073 1271 20.93 Validation Data scoring - Summary Report