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# Purdue - IE 33000 - IE 330 - Exam 2 Study Guide - Study Guide

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Purdue - IE 33000 - IE 330 - Exam 2 Study Guide - Study Guide

##### Description: Exam 2 materials, Homework 7
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##### Description: Exam 2 materials, Homework 7
10 Pages 43 Views 34 Unlocks
• Notes, Study Guides, Flashcards + More!
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Unformatted text preview: Exam 2 Study Guide Thursday, March 8, 2018 5:42 PM Exam Info: Covers chapters l_and 12 Will be IN CLASS on Morch 23/0, 2018 at 8:30am * 8 questi on s, 4.5 of which will be T/E (4 pr ) * 50m n tu complete * NO CALCULATORS Practice exam from 2017 will be the biggest help * Most of the exam will be interpreting results forker than doing calculations Equations: Given on exomnoue Not giren on exom - (but will be useful IR': SSR S - M SE + Con stant Response = Predictor X J: 5x * ] you will NOT need to calculate these by hondKey Concepts +Ch. 10 || Simple aud Malsiple Liner Regression> A ssumptions : Normality of Residuals check "Normal Probability Plot" the closer the dots one to the line, the beter * con be checked by running a Normality tes a the residuals * P-value obove 0.05 means normaliry con be assumed - Lineocity Relationship check "Versus Fas plot locking for a curved paym (U, I, n, un) * no payern means lineority can be assumed Pohen meaus y varries with a vorisk ople thon X, or with a diAnent power of x tan, Voriouce check "Versus Fiss plos * looking for a cone or funnel shape * no patern meous Con shant variance can be assumed Patein Meons_voriouce is increosing or decreasing ogains- the voriable as he x-axis double funnel ( m ) meons some inequality of Vorionce- Independance of souples (specifically with respet 16 oder) Tudependence os a whole" can really ouly be decided based on has exactly the dora was colletect We can check oue subset of it though - order check the "Versus Order plos looking for a pahau of ouy kind * no patern means independance I order con be bssumed 'a potem of ouy kind must be interpreteel vish respect to decortext of she doto Transforming Dara g . - J.x ? lico JJ.x flineor xConskoming, ineority Lax': x* lor any other non-liven version of - Hransfoming X: - transfaming gi ' non- constant variance non - normality of lesiduals Binory Logistic Regression - when the response variable, y, is binory (Os mel 1, ) gives probability of ys

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