Logistic regression classifier for the churn Data Coursework

Logistic regression classifier for the churn Data - Coursework Example The programming code is as follows: LOGISTIC  REGRESSION  VARIABLES  good_bad   Ã‚  /METHOD=ENTER  checking  duration  history  purpose  amount  savings  employed  installp  marital  coapp  resident  property  age  other  housing   Ã‚  Ã‚  Ã‚  existcr  job  depends  telephon  foreign   Ã‚  /CONTRAST  (purpose)=Indicator   Ã‚  /CLASSPLOT   Ã‚  /PRINT=CORR   Ã‚  /CRITERIA=PIN(0.05)  POUT(0.10)  ITERATE(20)  CUT(0.5). Then the analysis is presented below: Case Processing Summary Unweighted Cases N Percent Selected Cases Included in Analysis 964 96.4 Missing Cases 36 3.6 Total 1000 100.0 Unselected Cases 0 .0 Total 1000 100.0 a. If weight is in effect, see classification table for the total number of cases. Dependent Variable Encoding Original Value Internal Value Bad 0 Good 1 Categorical Variables Codings Frequency Parameter coding (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) purpose 3 1.000 .000 .000 .000 .000 .000 .000 .000 . 000 .000 0 225 .000 1.000 .000 .000 .000 .000 .000 .000 .000 .000 1 100 .000 .000 1.000 .000 .000 .000 .000 .000 .000 .000 2 174 .000 .000 .000 1.000 .000 .000 .000 .000 .000 .000 3 268 .000 .000 .000 .000 1.000 .000 .000 .000 .000 .000 4 12 .000 .000 .000 .000 .000 1.000 .000 .000 .000 .000 5 22 .000 .000 .000 .000 .000 .000 1.000 .000 .000 .000 6 47 .000 .000 .000 .000 .000 .000 .000 1.000 .000 .000 8 9 .000 .000 .000 .000 .000 .000 .000 .000 1.000 .000 9 94 .000 .000 .000 .000 .000 .000 .000 .000 .000 1.000 X 10 .000 .000 .000 .000 .000 .000 .000 .000 .000 .000 Beginning block Classification Table Observed Predicted good_bad Percentage Correct bad good Step 0 good_bad bad 0 292 .0 good 0 672 100.0 Overall Percentage 69.7 Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 0 Constant .834 .070 141.414 1 .000 2.301 Variables not in the Equation Score df Sig. Step 0 Variables checking 119.858 1 .000 duration 40.086 1 .000 History 48.045 1 .000 purpose 39.421 10 .000 purpose(1) 6.926 1 .008 purpose(2) 9.752 1 .002 purpose(3) 9.334 1 .002 purpose(4) .361 1 .548 purpose(5) 12.039 1 .001 purpose(6) .053 1 .817 purpose(7) .393 1 .531 purpose(8) 4.846 1 .028 purpose(9) 1.583 1 .208 purpose(10) .694 1 .405 amount 18.355 1 .000 savings 30.125 1 .000 employed 14.071 1 .000 installp 5.548 1 .019 marital 8.537 1 .003 coapp .419 1 .518 resident .000 1 .996 property 20.211 1 .000 age 7.933 1 .005 other 10.626 1 .001 housing .146 1 .703 existcr 2.184 1 .139 job .426 1 .514 depends .067 1 .797 telephon 2.137 1 .144 foreign 8.114 1 .004 a. Residual Chi-Squares are not computed because of redundancies. Block  1:  Method  =  Enter Omnibus Tests of Model Coefficients Chi-square df Sig. Step 1 Step 299.197 29 .000 Block 299.197 29 .000 Model 299.197 29 .000 Model Summary Step -2 Log likelihood Cox & Snell R Square Nagelkerke R Square 1 883.255a .267 .378 a. Estimation terminated at iteration number 20 because maximum iterations has been reached. Final solution canno t be found. The sensitivity and specificity analysis can be done as follows: Classification Table Observed Predicted good_bad Total Good Bad good_bad Good 596 (TP) 76 (FP) 672 Bad 140 (FN) 152 (TN) 292 Total 736 (Sensitivity) 228 (Specificity) 964 TP: True Positive; TN: True Negative; FP: False Positive; FN: False Negative Sensitivity=TP/(TP+FN)=596/(596+140)=0.812 or 81,7%

Collaborative Learning in E-learning Essay Example | Topics and Well Written Essays - 1250 words

Collaborative Learning in E-learning - Essay Example In order to ensure that collaborative learning is effective, asynchronous online discussions have been perceived as an efficient way for learners to participate in high quality discussion and intrinsic cognitive collaboration. This is because asynchronous online discussion gives learners enough time to reflect on their friend’s findings and contribution and reason about their own contributions before sending them off to their friends. Collaborative learning looks forward in fostering the argumentative quality discussions among learners in order to improve personal knowledge acquisition. In order to ensure efficiency of the collaborative learning, it is significant to apply additional instructional direction, which may assist learners to use the advantages of asynchronous communication or interaction for argumentative knowledge acquisition. For instance, knowledge construction via collaborative discussion is vital since collaboration methods sequence, define, and assign learnin g activities to distinct learners and can in turn facilitate activities such as construction of arguments during discussions. Collaborative learning in E-learning is rooted in constructivism. It intensively focuses on how productive peer collaboration can be stimulated and sustained in computer-mediated environments and how these collaborative activities facilitate learning. The use of Computers in learning will aid in providing sentence openers to ESL students, software-embedded collaboration scripts and representational guidance in order to improve the quality of online argumentation or moderation among students. In order to make collaborative learning effective, ESL students are encouraged to meet in groups. This increase explicitness due to persistence of textual messages on computer screen, more defined in expressing arguments due to lack of nonverbal communication