Page 29 - Regression Guideline

Important"Terms"You"Need"to"Know"
As"the"preceding"indicates,"regression"modeling"has"its"own"language."If"you’re"not" familiar"with"some"of"the"main"terms,"explanaJons"of"regression"procedures"and" outcomes"can"sound"like"unintelligible"“gobbledygook”"and"make"the"use"of"these" valuable"staJsJcal"tools"more"complex"than"need"be."Fortunately,"there"are"only"a"few" main"terms"you"need"to"know."These"can"be"explained"in"(mostly)"plain"English"and" related"directly"to"your"work"as"an"appraiser.""You"need"to"know"what"these"terms"mean" so"you"understand"how"the"modeling"works"and"also"so"you"can"explain"the"results"in" ways"your"clients"will"understand."Here"are"the"main"terms:"
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Variable:&&Perhaps"you"recall"from"high"school"algebra"(or"perhaps"you"blocked"that" memory),"a"variable"in"a"mathemaJcal"equaJon"is"anything"that"can"vary."So"in"the" equaJon,"Y"="4""+"2X,"the"lesers"Y"and"X"are"variables"because"their"values"can"vary" depending"on"what"numbers"you"put"into"the"equaJon."The"number"4"is"a"constant" because"its"value"does"not"vary."It’s"always"the"same"no"maser"how"Y"and"X"change."A" regression"equaJon"looks"very"similar."The"variable"Y"is"the"sale"price"and"the"variable"X" (usually"more"than"one"X)"are"the"property"characterisJcs"you"put"into"the"equaJon."The" different"values"they"take"on"come"from"a"database"of"properJes"such"as"the"MLS"with" known"values"for"sale"prices"and"property"characterisJcs.""
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Kinds&of&Variables."A"variable"can"either"be"independent"(predictor)"or"dependent" (predicted)."Variables"are"also"defined"by"how"they"are"measured."They"can"be" conDnuous"if"they"have"a"range"of"possible"values"that"indicate"increasing"or"decreasing" quanJJes"such"as"number"of"bedrooms,"garage"square"footage,"acres"of"land."They"can" also"be"discrete"if"they"instead"mean"the"presence"or"absence"of"some"quality"or" characterisJc"such"as"lakefront"property"(yes/no),"great"view"(yes/no),"finished"garage"(y/ n),"type"of"garage,"etc."SomeJmes,"discrete"variables"in"regression"models"are"also" referred"to"as"dummy"or"indicator"variables."The"property"characterisJcs"used"in"a" regression"analysis"as"price"predictors"can"be"either"conJnuous"or"discrete.""
& Coefficient:&&In"a"regression"model,"each"predictor"variable"has"an"associated"b"coefficient" or"regression"“weight”."In"an"appraisal"context,"the"interpretaJon"of"this"coefficient"is"the" amount"of"change"in"the"esJmated"sales"price"when"there"is"a"onePunit"change"in"the" associated"property"characterisJc."This"can"best"be"illustrated"with"an"example."Here"is" what"a"simple"regression"equaJon"with"a"variable"to"represent"square"footage"and" another"to"represent"lakefront"property"(yes"or"no)"might"look"like:""
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