ISYE 6501 Midterm 1 Intro to Analytics Modeling (Georgia Institute of Technology)

Due Jun 16 at 2am • Points 100 • Questions 45 • Available Jun 5 at 2am - Jun 16 at 2am • Time Limit 95 Minutes InstructionsThis quiz was locked Jun 16 at 2am.Attempt History Attempt Time Sc LATEST Attempt 1 50 minutes 78 Score for this quiz: 78.44 out of 100 Submitted Jun 15 at 6:48pm This attempt took 50 minutes. 95 Minute Time Limit Instructions • Work alone. Do not collaborate with or copy from anyone else. • You may use any of the following resources: o One sheet (both sides) of handwritten (not photocopied, scanned, or printed) notes • If any question seems ambiguous, use the most reasonable interpretation (i.e., don't be like Calvin): • If you experience any technical issues (i.e. images not loading) you may refresh the page without interrupting your exam attempt. If the issue persists, then please finish the exam and let the Instructors know about the issue in a private Piazza post afterwards. • Good luck! 1. For each of the 13 models/methods, select the choice that includes the category of question it is commonly used for. For models/methods that have Question 1 11 / 13 ptsmore than one correct category, the one it is most commonly used for; for models/methods that have no correct category listed, select "None". i. ARIMA Response prediction ii. CART [ Select ] ["Variance estimation", "Validation", "Classification and Response prediction", "Clustering", "None of the other choices"] iii. Cross validation [ Select ] ["Variance estimation", "Clustering", "None of the other choices", "Validation", "Classification and Response prediction"] iv. CUSUM [ Select ] ["Variance estimation", "None of the other choices", "Classification and Response prediction", "Clustering", "Validation"] v. Exponential smoothing [ Select ] ["Response prediction", "Variance estimation", "Clustering", "None of the other choices", "Validation", "Classification"] vi. GARCH [ Select ] ["Classification", "None of the other choices", "Respnse prediction", "Validation", "Clustering", "Variance estimation"] vii. kmeans [ Select ] ["Validation", "Response prediction", "Clustering", "Variance estimation", "None of the other choices", "Classification"] viii. k-nearest-neighbor [ Select ] ["None of the other choices", "Validation", "Classification and Response prediction", "Variance estimation", "Clustering"] ix. Linear regression [ Select ] ["Validation", "Clustering", "None of the other choices", "Response prediction", "Variance estimation", "Classification"] x. Logistic regression [ Select ] ["Classification and Response prediction", "Validation", "Clustering", "Variance estimation", "None of the other choices"] xi. Principal component analysis [ Select ] ["Validation", "Clustering", "Variance estimation", "Classification and Response prediction", "None of the other choices"] xii. Random forest [ Select ] ["None of the other choices", "Validation", "Variance estimation", "Clustering", "Classification and Response prediction"] xiii. Support vector machine [ Select ] ["Validation", "Variance estimation", "Clustering", "Classification", "Response prediction", "None of the other choices"] Answer 1: Response predictionAnswer 2: Classification and Response prediction Answer 3: Validation Answer 4: None of the other choices Answer 5: Response prediction Answer 6: Variance estimation Answer 7: Clustering Answer 8: Correct Answer Classification and Response prediction Clustering Answer 9: Response prediction Answer 10: Classification and Response prediction Answer 11:None of the other choices Answer 12: Clustering Correct Answer Classification and Response prediction Answer 13: Classification 2. For each of the following models, specify whether it is designed for use with attribute/feature data or time-series data: a. Exponential smoothing [ Select ] ["Time series data", "Attribute/feature data"] b. ARIMA [ Select ] ["Attribute/feature data", "Time series data"] c. k-means [ Select ] ["Time series data", "Attribute/feature data"] d. Principal component analysis [ Select ] ["Attribute/feature data", "Time series data"] e. Linear regression Attribute/feature data f. k-nearest-neighbor [ Select ] ["Time series data", "Attribute/feature data"] g. Random forest [ Select ] ["Time series data", "Attribute/feature data"] h. CUSUM [ Select ] ["Attribute/feature data", "Time series data"] i. Logistic regression [ Select ] ["Attribute/feature data", "Time series data"] j. Support vector machine [ Select ] ["Time series data", "Attribute/feature data"] k. GARCH [ Select ] ["Attribute/feature data", "Time series data"] Answer 1: