This assignment is directly linked to the following key learning outcomes from the course syllabus: Use “R” effectively to process, analyze and depict dataDevelop more advanced models to interpret dataUse advanced generalized linear methods to answer strategic and operational questionsPrepare complex dataset for analysisUse multivariable and logistic regression method to improve the predictive outcomeAssignment SummaryUse the College datasetfrom the ISLR library to build a logistic regression model to predict whether a university is private or public.Import the dataset and perform Exploratory Data Analysis by using descriptive statistics and plots to describe the dataset.Split the data into a train and test set – refer to the Feature_Selection.R document for information on how to split a dataset.Use the glm() function in the ‘stats’ package to fit a logistic regression model to the training set using at least two predictors.Create a confusion matrix and report the results of your model for the train set. Interpret and discuss the confusion matrix. Which misclassifications are more damaging for the analysis, False Positives or False Negatives?Report and interpret metrics for Accuracy, Precision, Recall, and Specificity.Create a confusion matrix and report the results of your model for the test set.Plot and interpret the ROC curve.Calculate and interpret the AUC.ReportRefer to the attached rubric for more details on the report. The report should contain a well written cover/title page, introduction, body, conclusion, and references. It must follow APA format and have at least 1000 words (excluding title page and references page. All R code used for your report should be included in an appendix at the end of the report.Graphs, figures, charts, and tables are very useful visual effects to communicate your results and impress your readers. However, such items should not be included in the report unless they are well described and interpreted. Please use subtitles to make your assignment more reader friendly as well.Format & GuidelinesThe report should follow the following format: Title pageIntroductionAnalysisConclusion/InterpretationsReferences

Review the test findings from Week 8 based on testing the parking lot calculation application located athttp://adam.goucher.ca/parkcalc/Re-test the application if needed.Formally document any defects or failures you found in an incident report. Include at least 5 defects in your incident report. At minimum, include the following information within your report for each defect or failure.Defect summaryIncident description (expected results, actual results, what you entered, date/time)Priority or severitySteps followed to create/recreate issueInclude error descriptions/messages, if appropriateProvide screen prints as neededCalculate and report basic testing metrics to help provide a feel for the status of your testing efforts, health of the software being tested, and effectiveness of your testing efforts. At minimum, include:Total Defects (overall measure of software quality)Test case testing ratio (indication of testing status) è Test cases tested / total test casesFailure Rate è # failures per unit of time (usually # failures per hour)Defect Density (measure of software quality) è total # defects / # test cases completedCritical Bug Detection Rate (measures testing effectiveness; testing resources should be focused on finding the highest risk defects) è # high priority bugs / # total bugs * 100%