Project 3
Veronica Chunikhin
12-3-2025
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## cov, smooth, varRenewable_Energy <- read.csv("C:/Users/nika/Downloads/R/csv/Project3.csv")Introduction
What factors predict whether households adopt renewable energy sources (solar panels, wind, geothermal)? The dataset used in this project is called Residential Energy Consumption Survey, taken from the US Energy Information Administration (https://www.eia.gov/consumption/residential/data/2020/index.php?view=microdata). This dataset receives information from a national sample survey that collects energy-related data for housing units occupied as a primary residence and the households that live in them. The dataset itself has 799 variables but I will be focusing on these 14: renewable energy adoption, household age, household size, square footage, electricity cost, total energy cost, cooling degree days, state, region, urban or rural setting, energy assistance, whether home is owned or not, household income, and the year the home was built in. Majority of these variable are quantitative, except for the renewable energy adoption which is a categorical variable. Given that the outcome of this research question is binary, I will be using a logistic regression to model the probability of adoption based on households and environmental characteristics.
Data Analysis
#Cleaning the Data
colnames(Renewable_Energy)## [1] "DOEID" "REGIONC" "DIVISION"
## [4] "STATE_FIPS" "state_postal" "state_name"
## [7] "BA_climate" "IECC_climate_code" "UATYP10"
## [10] "HDD65" "CDD65" "HDD30YR_PUB"
## [13] "CDD30YR_PUB" "TYPEHUQ" "CELLAR"
## [16] "CRAWL" "CONCRETE" "BASEOTH"
## [19] "BASEFIN" "ATTIC" "ATTICFIN"
## [22] "STORIES" "PRKGPLC1" "SIZEOFGARAGE"
## [25] "KOWNRENT" "YEARMADERANGE" "BEDROOMS"
## [28] "NCOMBATH" "NHAFBATH" "OTHROOMS"
## [31] "TOTROOMS" "STUDIO" "WALLTYPE"
## [34] "ROOFTYPE" "HIGHCEIL" "DOOR1SUM"
## [37] "WINDOWS" "TYPEGLASS" "ORIGWIN"
## [40] "WINFRAME" "TREESHAD" "ADQINSUL"
## [43] "DRAFTY" "UGASHERE" "SWIMPOOL"
## [46] "MONPOOL" "POOLPUMP" "FUELPOOL"
## [49] "RECBATH" "MONTUB" "FUELTUB"
## [52] "NUMFRIG" "SIZRFRI1" "TYPERFR1"
## [55] "AGERFRI1" "ICE" "SIZRFRI2"
## [58] "TYPERFR2" "AGERFRI2" "LOCRFRI2"
## [61] "WINECHILL" "NUMFREEZ" "UPRTFRZR"
## [64] "SIZFREEZ" "FREEZER" "AGEFRZR"
## [67] "RANGE" "COOKTOP" "OVEN"
## [70] "RANGEFUEL" "RANGEINDT" "RCOOKUSE"
## [73] "ROVENUSE" "COOKTOPFUEL" "COOKTOPINDT"
## [76] "COOKTOPUSE" "OVENFUEL" "OVENUSE"
## [79] "MICRO" "AMTMICRO" "OUTGRILLFUEL"
## [82] "OUTGRILL" "NUMMEAL" "USECOFFEE"
## [85] "TOAST" "TOASTOVN" "CROCKPOT"
## [88] "PRSSCOOK" "RICECOOK" "BLENDER"
## [91] "APPOTHER" "ELFOOD" "LPCOOK"
## [94] "UGCOOK" "DISHWASH" "DWASHUSE"
## [97] "DWCYCLE" "AGEDW" "CWASHER"
## [100] "TOPFRONT" "WASHLOAD" "WASHTEMP"
## [103] "AGECWASH" "DRYER" "DRYRFUEL"
## [106] "DRYRUSE" "AGECDRYER" "TVCOLOR"
## [109] "TVSIZE1" "TVTYPE1" "TVUSE1"
## [112] "TVONWD1" "TVONWE1" "TVSIZE2"
## [115] "TVTYPE2" "TVUSE2" "TVONWD2"
## [118] "TVONWE2" "TVSIZE3" "TVTYPE3"
## [121] "TVUSE3" "TVONWD3" "TVONWE3"
## [124] "CABLESAT" "COMBODVR" "SEPDVR"
## [127] "INTSTREAM" "PLAYSTA" "DVD"
## [130] "VCR" "TVAUDIOSYS" "DESKTOP"
## [133] "NUMLAPTOP" "NUMTABLET" "ELPERIPH"
## [136] "NUMSMPHONE" "CELLPHONE" "TELLWORK"
## [139] "TELLDAYS" "TLDESKTOP" "TLLAPTOP"
## [142] "TLTABLET" "TLMONITOR" "TLOTHER"
## [145] "ONLNEDUC" "INTERNET" "INTYPECELL"
## [148] "INTYPEBROAD" "INTYPEOTH" "SMARTSPK"
## [151] "SSLIGHT" "SSTEMP" "SSSECURE"
## [154] "SSTV" "SSOTHER" "HEATHOME"
## [157] "DNTHEAT" "HEATAPT" "EQUIPM"
## [160] "FUELHEAT" "EQUIPAGE" "GEOHP"
## [163] "EQUIPAUXTYPE" "EQUIPAUX" "FUELAUX"
## [166] "USEEQUIPAUX" "NUMPORTEL" "NUMFIREPLC"
## [169] "NUMDLHP" "BASEHEAT" "ATTCHEAT"
## [172] "GARGHEAT" "HUMIDTYPE" "NUMPORTHUM"
## [175] "USEHUMID" "ELWARM" "UGWARM"
## [178] "LPWARM" "FOWARM" "WDWARM"
## [181] "AIRCOND" "COOLAPT" "ACEQUIPM_PUB"
## [184] "ACEQUIPAGE" "ACEQUIPAUXTYPE_PUB" "NUMDLHPAC"
## [187] "NUMWWAC" "NUMPORTAC" "BASECOOL"
## [190] "ATTCCOOL" "GARGCOOL" "NUMCFAN"
## [193] "NUMFLOORFAN" "USECFAN" "HOUSEFAN"
## [196] "ATTICFAN" "DEHUMTYPE" "NUMPORTDEHUM"
## [199] "USEDEHUM" "ELCOOL" "TYPETHERM"
## [202] "HEATCNTL" "TEMPHOME" "TEMPGONE"
## [205] "TEMPNITE" "COOLCNTL" "TEMPHOMEAC"
## [208] "TEMPGONEAC" "TEMPNITEAC" "H2OAPT"
## [211] "H2OMAIN" "WHEATSIZ" "WHEATBKT"
## [214] "WHEATAGE" "FUELH2O" "MORETHAN1H2O"
## [217] "FUELH2O2" "ELWATER" "FOWATER"
## [220] "LPWATER" "SOLWATER" "WDWATER"
## [223] "UGWATER" "LGTIN1TO4" "LGTIN4TO8"
## [226] "LGTINMORE8" "LGTINLED" "LGTINCFL"
## [229] "LGTINCAN" "LGTOUTANY" "LGTOUTNITE"
## [232] "LGTOUTLED" "LGTOUTCFL" "LGTOUTCAN"
## [235] "ELPAY" "NGPAY" "LPGPAY"
## [238] "FOPAY" "SMARTMETER" "INTDATAACC"
## [241] "MEDICALDEV" "POWEROUT" "WHYPOWEROUT"
## [244] "BACKUP" "SOLAR" "ELOTHER"
## [247] "UGOTH" "LPOTHER" "FOOTHER"
## [250] "USEEL" "USENG" "USELP"
## [253] "USEFO" "USESOLAR" "USEWOOD"
## [256] "ALLELEC" "HHSEX" "HHAGE"
## [259] "EMPLOYHH" "EDUCATION" "SDESCENT"
## [262] "HOUSEHOLDER_RACE" "NHSLDMEM" "NUMCHILD"
## [265] "NUMADULT1" "NUMADULT2" "ATHOME"
## [268] "MONEYPY" "SCALEB" "SCALEG"
## [271] "SCALEE" "PAYHELP" "NOHEATBROKE"
## [274] "NOHEATEL" "NOHEATNG" "NOHEATBULK"
## [277] "NOHEATDAYS" "NOHEATHELP" "COLDMA"
## [280] "NOACBROKE" "NOACEL" "NOACDAYS"
## [283] "NOACHELP" "HOTMA" "ENERGYASST"
## [286] "ENERGYASST20" "ENERGYASST19" "ENERGYASST18"
## [289] "ENERGYASST17" "ENERGYASST16" "ENERGYASSTOTH"
## [292] "SQFTEST" "SQFTRANGE" "SQFTINCB"
## [295] "SQFTINCA" "SQFTINCG" "TOTSQFT_EN"
## [298] "TOTHSQFT" "TOTCSQFT" "ZACEQUIPAGE"
## [301] "ZADQINSUL" "ZAGECDRYER" "ZAGECWASH"
## [304] "ZAGEDW" "ZAGEFRZR" "ZAGERFRI1"
## [307] "ZAGERFRI2" "ZAIRCOND" "ZAMTMICRO"
## [310] "ZATHOME" "ZATTCCOOL" "ZATTCHEAT"
## [313] "ZATTIC" "ZATTICFAN" "ZATTICFIN"
## [316] "ZBACKUP" "ZBASECOOL" "ZBASEFIN"
## [319] "ZBASEHEAT" "ZBASEOTH" "ZBEDROOMS"
## [322] "ZBLENDER" "ZCABLESAT" "ZCELLAR"
## [325] "ZCELLPHONE" "ZCOLDMA" "ZCOMBODVR"
## [328] "ZCONCRETE" "ZCOOKTOP" "ZCOOKTOPFUEL"
## [331] "ZCOOKTOPINDT" "ZCOOKTOPUSE" "ZCOOLAPT"
## [334] "ZCOOLCNTL" "ZCRAWL" "ZCROCKPOT"
## [337] "ZCWASHER" "ZDEHUMTYPE" "ZDESKTOP"
## [340] "ZDISHWASH" "ZDOOR1SUM" "ZDRAFTY"
## [343] "ZDRYER" "ZDRYRFUEL" "ZDRYRUSE"
## [346] "ZDVD" "ZDWASHUSE" "ZDWCYCLE"
## [349] "ZEDUCATION" "ZELPAY" "ZELPERIPH"
## [352] "ZEMPLOYHH" "ZENERGYASST" "ZENERGYASST16"
## [355] "ZENERGYASST17" "ZENERGYASST18" "ZENERGYASST19"
## [358] "ZENERGYASST20" "ZENERGYASSTOTH" "ZEQUIPAGE"
## [361] "ZEQUIPAUXTYPE" "ZEQUIPM" "ZFOPAY"
## [364] "ZFREEZER" "ZFUELAUX" "ZFUELH2O"
## [367] "ZFUELH2O2" "ZFUELHEAT" "ZFUELPOOL"
## [370] "ZFUELTUB" "ZGARGCOOL" "ZGARGHEAT"
## [373] "ZH2OAPT" "ZH2OMAIN" "ZHEATAPT"
## [376] "ZHEATCNTL" "ZHEATHOME" "ZHHAGE"
## [379] "ZHHSEX" "ZHIGHCEIL" "ZHOTMA"
## [382] "ZHOUSEFAN" "ZHUMIDTYPE" "ZICE"
## [385] "ZINTERNET" "ZINTSTREAM" "ZINTYPEBROAD"
## [388] "ZINTYPECELL" "ZINTYPEOTH" "ZKOWNRENT"
## [391] "ZLGTIN1TO4" "ZLGTIN4TO8" "ZLGTINCAN"
## [394] "ZLGTINCFL" "ZLGTINLED" "ZLGTINMORE8"
## [397] "ZLGTOUTANY" "ZLGTOUTCAN" "ZLGTOUTCFL"
## [400] "ZLGTOUTLED" "ZLGTOUTNITE" "ZLOCRFRI2"
## [403] "ZLPGPAY" "ZMICRO" "ZMONEYPY"
## [406] "ZMONPOOL" "ZMONTUB" "ZMORETHAN1H2O"
## [409] "ZNCOMBATH" "ZNGPAY" "ZNHAFBATH"
## [412] "ZNHSLDMEM" "ZNOACBROKE" "ZNOACDAYS"
## [415] "ZNOACEL" "ZNOACHELP" "ZNOHEATBROKE"
## [418] "ZNOHEATBULK" "ZNOHEATDAYS" "ZNOHEATEL"
## [421] "ZNOHEATHELP" "ZNOHEATNG" "ZNUMADULT1"
## [424] "ZNUMADULT2" "ZNUMCFAN" "ZNUMCHILD"
## [427] "ZNUMDLHP" "ZNUMDLHPAC" "ZNUMFIREPLC"
## [430] "ZNUMFLOORFAN" "ZNUMFREEZ" "ZNUMFRIG"
## [433] "ZNUMLAPTOP" "ZNUMMEAL" "ZNUMPORTAC"
## [436] "ZNUMPORTDEHUM" "ZNUMPORTEL" "ZNUMPORTHUM"
## [439] "ZNUMSMPHONE" "ZNUMTABLET" "ZNUMWWAC"
## [442] "ZONLNEDUC" "ZORIGWIN" "ZOTHROOMS"
## [445] "ZOUTGRILLFUEL" "ZOUTLET" "ZOVEN"
## [448] "ZOVENFUEL" "ZOVENUSE" "ZPAYHELP"
## [451] "ZPLAYSTA" "ZPOOLPUMP" "ZPOWEROUT"
## [454] "ZPRKGPLC1" "ZPRSSCOOK" "ZRANGE"
## [457] "ZRANGEFUEL" "ZRANGEINDT" "ZRCOOKUSE"
## [460] "ZRECBATH" "ZRICECOOK" "ZROOFTYPE"
## [463] "ZROVENUSE" "ZSCALEB" "ZSCALEE"
## [466] "ZSCALEG" "ZSDESCENT" "ZSEPDVR"
## [469] "ZSIZEOFGARAGE" "ZSIZFREEZ" "ZSIZRFRI1"
## [472] "ZSIZRFRI2" "ZSMARTSPK" "ZSQFTEST"
## [475] "ZSQFTINCA" "ZSQFTINCB" "ZSQFTINCG"
## [478] "ZSQFTRANGE" "ZSSLIGHT" "ZSSOTHER"
## [481] "ZSSSECURE" "ZSSTEMP" "ZSSTV"
## [484] "ZSTORIES" "ZSWIMPOOL" "ZTELLDAYS"
## [487] "ZTELLWORK" "ZTEMPGONE" "ZTEMPGONEAC"
## [490] "ZTEMPHOME" "ZTEMPHOMEAC" "ZTEMPNITE"
## [493] "ZTEMPNITEAC" "ZTLDESKTOP" "ZTLLAPTOP"
## [496] "ZTLMONITOR" "ZTLOTHER" "ZTLTABLET"
## [499] "ZTOAST" "ZTOASTOVN" "ZTOPFRONT"
## [502] "ZTREESHAD" "ZTVAUDIOSYS" "ZTVCOLOR"
## [505] "ZTVONWD1" "ZTVONWD2" "ZTVONWD3"
## [508] "ZTVONWE1" "ZTVONWE2" "ZTVONWE3"
## [511] "ZTVSIZE1" "ZTVSIZE2" "ZTVSIZE3"
## [514] "ZTVTYPE1" "ZTVTYPE2" "ZTVTYPE3"
## [517] "ZTVUSE1" "ZTVUSE2" "ZTVUSE3"
## [520] "ZTYPEGLASS" "ZTYPERFR1" "ZTYPERFR2"
## [523] "ZTYPETHERM" "ZUGASHERE" "ZUPRTFRZR"
## [526] "ZUSECFAN" "ZUSECOFFEE" "ZUSEDEHUM"
## [529] "ZUSEEQUIPAUX" "ZUSEHUMID" "ZVCR"
## [532] "ZWALLTYPE" "ZWASHLOAD" "ZWASHTEMP"
## [535] "ZWHEATAGE" "ZWHEATBKT" "ZWHEATSIZ"
## [538] "ZWHYPOWEROUT" "ZWINDOWS" "ZWINECHILL"
## [541] "ZWINFRAME" "ZYEARMADERANGE" "ZTOTROOMS"
## [544] "ZDNTHEAT" "ZTYPEHUQ" "ZSTUDIO"
## [547] "ZOUTGRILL" "ZHOUSEHOLDER_RACE" "ZACEQUIPM_PUB"
## [550] "ZACEQUIPAUXTYPE_PUB" "NWEIGHT" "NWEIGHT1"
## [553] "NWEIGHT2" "NWEIGHT3" "NWEIGHT4"
## [556] "NWEIGHT5" "NWEIGHT6" "NWEIGHT7"
## [559] "NWEIGHT8" "NWEIGHT9" "NWEIGHT10"
## [562] "NWEIGHT11" "NWEIGHT12" "NWEIGHT13"
## [565] "NWEIGHT14" "NWEIGHT15" "NWEIGHT16"
## [568] "NWEIGHT17" "NWEIGHT18" "NWEIGHT19"
## [571] "NWEIGHT20" "NWEIGHT21" "NWEIGHT22"
## [574] "NWEIGHT23" "NWEIGHT24" "NWEIGHT25"
## [577] "NWEIGHT26" "NWEIGHT27" "NWEIGHT28"
## [580] "NWEIGHT29" "NWEIGHT30" "NWEIGHT31"
## [583] "NWEIGHT32" "NWEIGHT33" "NWEIGHT34"
## [586] "NWEIGHT35" "NWEIGHT36" "NWEIGHT37"
## [589] "NWEIGHT38" "NWEIGHT39" "NWEIGHT40"
## [592] "NWEIGHT41" "NWEIGHT42" "NWEIGHT43"
## [595] "NWEIGHT44" "NWEIGHT45" "NWEIGHT46"
## [598] "NWEIGHT47" "NWEIGHT48" "NWEIGHT49"
## [601] "NWEIGHT50" "NWEIGHT51" "NWEIGHT52"
## [604] "NWEIGHT53" "NWEIGHT54" "NWEIGHT55"
## [607] "NWEIGHT56" "NWEIGHT57" "NWEIGHT58"
## [610] "NWEIGHT59" "NWEIGHT60" "KWH"
## [613] "BTUEL" "DOLLAREL" "ELXBTU"
## [616] "PERIODEL" "ZELAMOUNT" "KWHSPH"
## [619] "KWHCOL" "KWHWTH" "KWHRFG"
## [622] "KWHRFG1" "KWHRFG2" "KWHFRZ"
## [625] "KWHCOK" "KWHMICRO" "KWHCW"
## [628] "KWHCDR" "KWHDWH" "KWHLGT"
## [631] "KWHTVREL" "KWHTV1" "KWHTV2"
## [634] "KWHTV3" "KWHAHUHEAT" "KWHAHUCOL"
## [637] "KWHCFAN" "KWHDHUM" "KWHHUM"
## [640] "KWHPLPMP" "KWHHTBPMP" "KWHHTBHEAT"
## [643] "KWHEVCHRG" "KWHNEC" "KWHOTH"
## [646] "BTUELSPH" "BTUELCOL" "BTUELWTH"
## [649] "BTUELRFG" "BTUELRFG1" "BTUELRFG2"
## [652] "BTUELFRZ" "BTUELCOK" "BTUELMICRO"
## [655] "BTUELCW" "BTUELCDR" "BTUELDWH"
## [658] "BTUELLGT" "BTUELTVREL" "BTUELTV1"
## [661] "BTUELTV2" "BTUELTV3" "BTUELAHUHEAT"
## [664] "BTUELAHUCOL" "BTUELCFAN" "BTUELDHUM"
## [667] "BTUELHUM" "BTUELPLPMP" "BTUELHTBPMP"
## [670] "BTUELHTBHEAT" "BTUELEVCHRG" "BTUELNEC"
## [673] "BTUELOTH" "DOLELSPH" "DOLELCOL"
## [676] "DOLELWTH" "DOLELRFG" "DOLELRFG1"
## [679] "DOLELRFG2" "DOLELFRZ" "DOLELCOK"
## [682] "DOLELMICRO" "DOLELCW" "DOLELCDR"
## [685] "DOLELDWH" "DOLELLGT" "DOLELTVREL"
## [688] "DOLELTV1" "DOLELTV2" "DOLELTV3"
## [691] "DOLELAHUHEAT" "DOLELAHUCOL" "DOLELCFAN"
## [694] "DOLELDHUM" "DOLELHUM" "DOLELPLPMP"
## [697] "DOLELHTBPMP" "DOLELHTBHEAT" "DOLELEVCHRG"
## [700] "DOLELNEC" "DOLELOTH" "CUFEETNG"
## [703] "BTUNG" "DOLLARNG" "NGXBTU"
## [706] "PERIODNG" "ZNGAMOUNT" "BTUNGSPH"
## [709] "BTUNGWTH" "BTUNGCOK" "BTUNGCDR"
## [712] "BTUNGPLHEAT" "BTUNGHTBHEAT" "BTUNGNEC"
## [715] "BTUNGOTH" "CUFEETNGSPH" "CUFEETNGWTH"
## [718] "CUFEETNGCOK" "CUFEETNGCDR" "CUFEETNGPLHEAT"
## [721] "CUFEETNGHTBHEAT" "CUFEETNGNEC" "CUFEETNGOTH"
## [724] "DOLNGSPH" "DOLNGWTH" "DOLNGCOK"
## [727] "DOLNGCDR" "DOLNGPLHEAT" "DOLNGHTBHEAT"
## [730] "DOLNGNEC" "DOLNGOTH" "GALLONLP"
## [733] "BTULP" "DOLLARLP" "LPXBTU"
## [736] "PERIODLP" "ZLPAMOUNT" "BTULPSPH"
## [739] "BTULPWTH" "BTULPCOK" "BTULPCDR"
## [742] "BTULPNEC" "BTULPOTH" "GALLONLPSPH"
## [745] "GALLONLPWTH" "GALLONLPCOK" "GALLONLPCDR"
## [748] "GALLONLPNEC" "GALLONLPOTH" "DOLLPSPH"
## [751] "DOLLPWTH" "DOLLPCOK" "DOLLPCDR"
## [754] "DOLLPNEC" "DOLLPOTH" "GALLONFO"
## [757] "BTUFO" "DOLLARFO" "FOXBTU"
## [760] "PERIODFO" "ZFOAMOUNT" "BTUFOSPH"
## [763] "BTUFOWTH" "BTUFONEC" "BTUFOOTH"
## [766] "GALLONFOSPH" "GALLONFOWTH" "GALLONFONEC"
## [769] "GALLONFOOTH" "DOLFOSPH" "DOLFOWTH"
## [772] "DOLFONEC" "DOLFOOTH" "BTUWD"
## [775] "ZWDAMOUNT" "TOTALBTUSPH" "TOTALDOLSPH"
## [778] "TOTALBTUWTH" "TOTALDOLWTH" "TOTALBTUOTH"
## [781] "TOTALDOLOTH" "TOTALBTU" "TOTALDOL"
## [784] "DBT1" "DBT99" "GWT"
## [787] "WOODTYPE" "OUTLET" "ELECVEH"
## [790] "EVCHRGHOME" "EVCHRGAPT" "EVCHRGWKS"
## [793] "EVCHRGBUS" "EVCHRGMUNI" "EVCHRGDLR"
## [796] "EVCHRGHWY" "EVCHRGOTH" "EVHOMEAMT"
## [799] "EVCHRGTYPE"head(Renewable_Energy)## DOEID REGIONC DIVISION STATE_FIPS state_postal state_name
## 1 100001 WEST Mountain South 35 NM New Mexico
## 2 100002 SOUTH West South Central 5 AR Arkansas
## 3 100003 WEST Mountain South 35 NM New Mexico
## 4 100004 SOUTH South Atlantic 45 SC South Carolina
## 5 100005 NORTHEAST Middle Atlantic 34 NJ New Jersey
## 6 100006 SOUTH West South Central 48 TX Texas
## BA_climate IECC_climate_code UATYP10 HDD65 CDD65 HDD30YR_PUB CDD30YR_PUB
## 1 Mixed-Dry 4B U 3844 1679 4451 1027
## 2 Mixed-Humid 4A U 3766 1458 4429 1305
## 3 Mixed-Dry 4B U 3819 1696 4500 1010
## 4 Mixed-Humid 3A U 2614 1718 3229 1653
## 5 Mixed-Humid 4A U 4219 1363 4896 1059
## 6 Hot-Humid 2A U 901 3558 1150 3588
## TYPEHUQ CELLAR CRAWL CONCRETE BASEOTH BASEFIN ATTIC ATTICFIN STORIES PRKGPLC1
## 1 2 0 0 1 0 -2 0 -2 1 1
## 2 5 -2 -2 -2 -2 -2 -2 -2 -2 -2
## 3 5 -2 -2 -2 -2 -2 -2 -2 -2 -2
## 4 2 0 1 0 0 -2 0 -2 2 1
## 5 5 -2 -2 -2 -2 -2 -2 -2 -2 -2
## 6 2 0 0 1 0 -2 1 1 2 0
## SIZEOFGARAGE KOWNRENT YEARMADERANGE BEDROOMS NCOMBATH NHAFBATH OTHROOMS
## 1 2 1 4 4 3 0 4
## 2 -2 2 5 1 1 0 2
## 3 -2 2 3 1 1 0 3
## 4 2 1 5 2 2 1 7
## 5 -2 2 3 1 1 0 2
## 6 -2 2 6 4 2 1 4
## TOTROOMS STUDIO WALLTYPE ROOFTYPE HIGHCEIL DOOR1SUM WINDOWS TYPEGLASS ORIGWIN
## 1 8 -2 1 5 0 2 4 2 0
## 2 3 0 1 -2 1 0 1 2 1
## 3 4 0 1 -2 0 1 1 1 1
## 4 9 -2 3 5 1 0 5 2 0
## 5 3 0 7 -2 0 0 1 2 0
## 6 8 -2 1 5 1 0 4 1 1
## WINFRAME TREESHAD ADQINSUL DRAFTY UGASHERE SWIMPOOL MONPOOL POOLPUMP FUELPOOL
## 1 3 1 2 3 1 0 -2 -2 -2
## 2 1 0 2 3 1 -2 -2 -2 -2
## 3 2 0 2 4 1 -2 -2 -2 -2
## 4 3 1 2 4 1 0 -2 -2 -2
## 5 2 0 2 3 1 -2 -2 -2 -2
## 6 1 1 1 4 1 0 -2 -2 -2
## RECBATH MONTUB FUELTUB NUMFRIG SIZRFRI1 TYPERFR1 AGERFRI1 ICE SIZRFRI2
## 1 0 -2 -2 2 4 5 3 0 3
## 2 0 -2 -2 1 3 1 1 0 -2
## 3 1 0 -2 0 -2 -2 -2 -2 -2
## 4 0 -2 -2 2 4 2 3 1 2
## 5 0 -2 -2 2 4 2 3 1 1
## 6 0 -2 -2 2 4 2 4 1 3
## TYPERFR2 AGERFRI2 LOCRFRI2 WINECHILL NUMFREEZ UPRTFRZR SIZFREEZ FREEZER
## 1 3 3 2 0 1 1 4 2
## 2 -2 -2 -2 0 1 2 1 1
## 3 -2 -2 -2 0 0 -2 -2 -2
## 4 3 3 2 0 0 -2 -2 -2
## 5 -2 1 4 1 0 -2 -2 -2
## 6 3 5 4 0 0 -2 -2 -2
## AGEFRZR RANGE COOKTOP OVEN RANGEFUEL RANGEINDT RCOOKUSE ROVENUSE COOKTOPFUEL
## 1 5 1 0 0 1 -2 21 10 -2
## 2 1 1 0 1 5 1 7 2 -2
## 3 -2 1 0 0 5 0 2 1 -2
## 4 -2 1 0 1 1 -2 12 0 -2
## 5 -2 1 0 0 1 -2 18 0 -2
## 6 -2 0 1 2 -2 -2 -2 -2 5
## COOKTOPINDT COOKTOPUSE OVENFUEL OVENUSE MICRO AMTMICRO OUTGRILLFUEL OUTGRILL
## 1 -2 -2 -2 -2 1 50 2 1
## 2 -2 -2 5 2 0 -2 -2 0
## 3 -2 -2 -2 -2 1 7 -2 0
## 4 -2 -2 5 6 1 30 2 1
## 5 -2 -2 -2 -2 1 5 -2 0
## 6 0 20 5 15 1 18 2 1
## NUMMEAL USECOFFEE TOAST TOASTOVN CROCKPOT PRSSCOOK RICECOOK BLENDER APPOTHER
## 1 2 1 1 0 1 1 0 0 0
## 2 2 1 1 0 0 0 0 0 0
## 3 3 3 1 0 0 0 0 0 0
## 4 1 3 1 0 0 0 0 0 0
## 5 2 1 0 1 0 0 0 0 0
## 6 1 1 1 0 0 0 0 0 1
## ELFOOD LPCOOK UGCOOK DISHWASH DWASHUSE DWCYCLE AGEDW CWASHER TOPFRONT
## 1 0 0 1 1 10 2 1 1 2
## 2 1 0 0 1 1 2 3 1 1
## 3 1 0 0 1 1 2 3 0 -2
## 4 1 0 1 1 7 1 3 1 1
## 5 0 0 1 1 6 2 3 0 -2
## 6 1 0 0 1 6 2 5 1 1
## WASHLOAD WASHTEMP AGECWASH DRYER DRYRFUEL DRYRUSE AGECDRYER TVCOLOR TVSIZE1
## 1 10 3 2 1 5 15 2 3 3
## 2 1 3 2 1 5 1 1 1 2
## 3 -2 -2 -2 0 -2 -2 -2 1 3
## 4 4 2 4 1 5 4 4 2 4
## 5 -2 -2 -2 0 -2 -2 -2 2 3
## 6 5 3 4 1 1 5 4 2 2
## TVTYPE1 TVUSE1 TVONWD1 TVONWE1 TVSIZE2 TVTYPE2 TVUSE2 TVONWD2 TVONWE2 TVSIZE3
## 1 1 1 4 6 1 1 2 5 5 1
## 2 1 99 3 3 -2 -2 -2 -2 -2 -2
## 3 2 1 5 5 -2 -2 -2 -2 -2 -2
## 4 2 1 8 8 3 2 1 4 4 -2
## 5 1 3 5 6 2 3 1 1 2 -2
## 6 1 3 4 6 1 1 3 2 2 -2
## TVTYPE3 TVUSE3 TVONWD3 TVONWE3 CABLESAT COMBODVR SEPDVR INTSTREAM PLAYSTA DVD
## 1 1 1 0.5 0.5 2 1 0 3 0 1
## 2 -2 -2 -2.0 -2.0 0 0 0 0 0 1
## 3 -2 -2 -2.0 -2.0 1 0 0 0 0 0
## 4 -2 -2 -2.0 -2.0 2 1 0 1 0 1
## 5 -2 -2 -2.0 -2.0 2 0 0 0 0 0
## 6 -2 -2 -2.0 -2.0 0 0 0 2 1 0
## VCR TVAUDIOSYS DESKTOP NUMLAPTOP NUMTABLET ELPERIPH NUMSMPHONE CELLPHONE
## 1 0 0 0 1 1 1 2 0
## 2 0 0 0 0 1 0 1 0
## 3 0 0 0 0 0 0 1 0
## 4 0 1 0 1 2 1 2 0
## 5 0 0 1 2 0 0 3 0
## 6 0 1 2 3 1 1 5 0
## TELLWORK TELLDAYS TLDESKTOP TLLAPTOP TLTABLET TLMONITOR TLOTHER ONLNEDUC
## 1 0 -2 -2 -2 -2 -2 -2 0
## 2 0 -2 -2 -2 -2 -2 -2 0
## 3 0 -2 -2 -2 -2 -2 -2 0
## 4 0 -2 -2 -2 -2 -2 -2 0
## 5 1 7 1 0 0 1 0 0
## 6 1 5 1 1 0 0 0 0
## INTERNET INTYPECELL INTYPEBROAD INTYPEOTH SMARTSPK SSLIGHT SSTEMP SSSECURE
## 1 1 1 1 0 0 -2 -2 -2
## 2 1 1 0 0 0 -2 -2 -2
## 3 0 -2 -2 -2 -2 -2 -2 -2
## 4 1 0 1 0 1 0 0 0
## 5 1 1 1 0 0 -2 -2 -2
## 6 1 1 1 0 0 -2 -2 -2
## SSTV SSOTHER HEATHOME DNTHEAT HEATAPT EQUIPM FUELHEAT EQUIPAGE GEOHP
## 1 -2 -2 1 -2 -2 3 1 4 -2
## 2 -2 -2 1 -2 0 3 5 5 -2
## 3 -2 -2 1 -2 1 2 1 6 -2
## 4 0 0 1 -2 -2 3 1 3 -2
## 5 -2 -2 1 -2 0 3 1 4 -2
## 6 -2 -2 1 -2 -2 3 1 1 -2
## EQUIPAUXTYPE EQUIPAUX FUELAUX USEEQUIPAUX NUMPORTEL NUMFIREPLC NUMDLHP
## 1 0 0 -2 -2 -2 -2 -2
## 2 10 1 5 4 1 -2 -2
## 3 0 0 -2 -2 -2 -2 -2
## 4 99 1 5 1 -2 -2 -2
## 5 0 0 -2 -2 -2 -2 -2
## 6 9 1 7 4 -2 1 -2
## BASEHEAT ATTCHEAT GARGHEAT HUMIDTYPE NUMPORTHUM USEHUMID ELWARM UGWARM LPWARM
## 1 -2 -2 0 0 -2 -2 0 1 0
## 2 -2 -2 -2 0 -2 -2 1 0 0
## 3 -2 -2 -2 0 -2 -2 0 1 0
## 4 -2 -2 0 0 -2 -2 1 1 0
## 5 -2 -2 -2 0 -2 -2 0 1 0
## 6 -2 0 -2 0 -2 -2 0 1 0
## FOWARM WDWARM AIRCOND COOLAPT ACEQUIPM_PUB ACEQUIPAGE ACEQUIPAUXTYPE_PUB
## 1 0 0 1 -2 1 3 0
## 2 0 0 1 1 1 4 0
## 3 0 0 1 1 1 5 0
## 4 0 0 1 -2 1 3 1
## 5 0 0 1 0 1 4 0
## 6 0 1 1 -2 1 1 0
## NUMDLHPAC NUMWWAC NUMPORTAC BASECOOL ATTCCOOL GARGCOOL NUMCFAN NUMFLOORFAN
## 1 -2 -2 -2 -2 -2 0 3 3
## 2 -2 -2 -2 -2 -2 -2 1 1
## 3 -2 -2 -2 -2 -2 -2 0 0
## 4 -2 -2 -2 -2 -2 0 4 0
## 5 -2 -2 -2 -2 -2 -2 0 0
## 6 -2 -2 -2 -2 0 -2 2 0
## USECFAN HOUSEFAN ATTICFAN DEHUMTYPE NUMPORTDEHUM USEDEHUM ELCOOL TYPETHERM
## 1 4 0 0 0 -2 -2 1 1
## 2 1 -2 -2 0 -2 -2 1 1
## 3 -2 -2 -2 0 -2 -2 1 1
## 4 2 0 0 0 -2 -2 1 1
## 5 -2 -2 -2 0 -2 -2 1 2
## 6 1 0 0 0 -2 -2 1 3
## HEATCNTL TEMPHOME TEMPGONE TEMPNITE COOLCNTL TEMPHOMEAC TEMPGONEAC TEMPNITEAC
## 1 1 70 70 68 1 71 71 71
## 2 4 70 65 65 4 68 68 68
## 3 1 69 68 67 1 70 68 68
## 4 1 68 68 68 1 72 72 72
## 5 1 68 68 68 2 72 72 72
## 6 3 76 76 68 3 69 74 68
## H2OAPT H2OMAIN WHEATSIZ WHEATBKT WHEATAGE FUELH2O MORETHAN1H2O FUELH2O2
## 1 -2 1 3 0 1 1 0 -2
## 2 0 -2 2 0 4 1 0 -2
## 3 1 -2 4 -2 5 1 0 -2
## 4 -2 4 3 0 3 1 0 -2
## 5 1 -2 2 0 4 1 0 -2
## 6 -2 99 3 1 6 1 0 -2
## ELWATER FOWATER LPWATER SOLWATER WDWATER UGWATER LGTIN1TO4 LGTIN4TO8
## 1 0 0 0 0 0 1 6 8
## 2 0 0 0 0 0 1 1 1
## 3 0 0 0 0 0 1 6 0
## 4 0 0 0 0 0 1 14 2
## 5 0 0 0 0 0 1 5 4
## 6 0 0 0 0 0 1 19 4
## LGTINMORE8 LGTINLED LGTINCFL LGTINCAN LGTOUTANY LGTOUTNITE LGTOUTLED
## 1 3 2 4 0 1 3 1
## 2 2 2 4 0 1 1 0
## 3 0 0 1 0 0 -2 -2
## 4 1 4 4 3 0 -2 -2
## 5 4 3 4 2 0 -2 -2
## 6 0 2 0 4 1 6 1
## LGTOUTCFL LGTOUTCAN ELPAY NGPAY LPGPAY FOPAY SMARTMETER INTDATAACC MEDICALDEV
## 1 0 0 1 1 -2 -2 0 -2 1
## 2 1 0 1 1 -2 -2 0 -2 0
## 3 -2 -2 1 1 -2 -2 0 -2 0
## 4 -2 -2 1 1 -2 -2 1 0 0
## 5 -2 -2 1 1 -2 -2 -4 -2 0
## 6 0 0 1 1 -2 -2 1 0 0
## POWEROUT WHYPOWEROUT BACKUP SOLAR ELOTHER UGOTH LPOTHER FOOTHER USEEL USENG
## 1 0 -2 0 0 1 0 0 0 1 1
## 2 0 -2 0 -2 1 0 0 0 1 1
## 3 0 -2 1 -2 1 0 0 0 1 1
## 4 0 -2 0 0 1 0 0 0 1 1
## 5 0 -2 0 -2 1 0 0 0 1 1
## 6 0 -2 0 0 1 1 0 0 1 1
## USELP USEFO USESOLAR USEWOOD ALLELEC HHSEX HHAGE EMPLOYHH EDUCATION SDESCENT
## 1 0 0 0 0 0 1 65 3 5 0
## 2 0 0 0 0 0 1 79 3 3 0
## 3 0 0 0 0 0 2 82 3 4 0
## 4 0 0 0 0 0 2 70 3 3 0
## 5 0 0 0 0 0 2 30 1 5 0
## 6 0 0 0 1 0 1 54 4 4 1
## HOUSEHOLDER_RACE NHSLDMEM NUMCHILD NUMADULT1 NUMADULT2 ATHOME MONEYPY SCALEB
## 1 1 2 0 0 2 0 13 0
## 2 1 1 0 0 1 5 6 0
## 3 1 1 0 0 1 3 11 0
## 4 1 2 0 0 2 5 10 0
## 5 1 2 0 2 0 5 16 0
## 6 1 4 2 2 0 5 15 0
## SCALEG SCALEE PAYHELP NOHEATBROKE NOHEATEL NOHEATNG NOHEATBULK NOHEATDAYS
## 1 0 0 -2 0 0 0 0 -2
## 2 0 0 -2 0 0 0 0 -2
## 3 0 0 -2 0 0 0 0 -2
## 4 0 0 -2 0 0 0 0 -2
## 5 0 0 -2 0 0 0 0 -2
## 6 0 3 0 0 0 0 0 -2
## NOHEATHELP COLDMA NOACBROKE NOACEL NOACDAYS NOACHELP HOTMA ENERGYASST
## 1 -2 0 0 0 -2 -2 0 0
## 2 -2 0 0 0 -2 -2 0 0
## 3 -2 0 0 0 -2 -2 0 0
## 4 -2 0 0 0 -2 -2 0 0
## 5 -2 0 0 0 -2 -2 0 0
## 6 -2 0 0 0 -2 -2 0 0
## ENERGYASST20 ENERGYASST19 ENERGYASST18 ENERGYASST17 ENERGYASST16
## 1 -2 -2 -2 -2 -2
## 2 -2 -2 -2 -2 -2
## 3 -2 -2 -2 -2 -2
## 4 -2 -2 -2 -2 -2
## 5 -2 -2 -2 -2 -2
## 6 -2 -2 -2 -2 -2
## ENERGYASSTOTH SQFTEST SQFTRANGE SQFTINCB SQFTINCA SQFTINCG TOTSQFT_EN
## 1 -2 2100 6 -2 -2 0 2100
## 2 -2 590 1 -2 -2 -2 590
## 3 -2 900 3 -2 -2 -2 900
## 4 -2 2100 6 -2 -2 0 2100
## 5 -2 800 3 -2 -2 -2 800
## 6 -2 3010 8 -2 0 -2 4520
## TOTHSQFT TOTCSQFT ZACEQUIPAGE ZADQINSUL ZAGECDRYER ZAGECWASH ZAGEDW ZAGEFRZR
## 1 2100 2100 0 0 0 0 0 0
## 2 590 590 1 0 1 1 1 0
## 3 900 900 0 0 -2 -2 0 -2
## 4 2100 2100 0 0 0 0 0 -2
## 5 800 800 0 0 -2 -2 0 -2
## 6 3010 3010 1 0 0 0 1 -2
## ZAGERFRI1 ZAGERFRI2 ZAIRCOND ZAMTMICRO ZATHOME ZATTCCOOL ZATTCHEAT ZATTIC
## 1 0 0 0 0 0 -2 -2 0
## 2 0 -2 0 -2 0 -2 -2 -2
## 3 -2 -2 0 0 0 -2 -2 -2
## 4 0 0 0 0 1 -2 -2 0
## 5 0 0 0 0 0 -2 -2 -2
## 6 0 0 0 0 0 0 0 0
## ZATTICFAN ZATTICFIN ZBACKUP ZBASECOOL ZBASEFIN ZBASEHEAT ZBASEOTH ZBEDROOMS
## 1 0 -2 0 -2 -2 -2 0 0
## 2 -2 -2 0 -2 -2 -2 -2 0
## 3 -2 -2 0 -2 -2 -2 -2 0
## 4 0 -2 0 -2 -2 -2 0 0
## 5 -2 -2 0 -2 -2 -2 -2 0
## 6 0 0 0 -2 -2 -2 1 0
## ZBLENDER ZCABLESAT ZCELLAR ZCELLPHONE ZCOLDMA ZCOMBODVR ZCONCRETE ZCOOKTOP
## 1 0 0 0 0 0 0 0 0
## 2 0 0 -2 0 0 0 -2 1
## 3 0 0 -2 0 0 0 -2 0
## 4 0 0 0 0 0 0 0 0
## 5 0 0 -2 0 0 0 -2 0
## 6 0 0 1 0 0 0 1 0
## ZCOOKTOPFUEL ZCOOKTOPINDT ZCOOKTOPUSE ZCOOLAPT ZCOOLCNTL ZCRAWL ZCROCKPOT
## 1 -2 -2 -2 -2 0 0 0
## 2 -2 -2 -2 1 1 -2 0
## 3 -2 -2 -2 0 0 -2 0
## 4 -2 -2 -2 -2 0 0 0
## 5 -2 -2 -2 0 0 -2 0
## 6 0 0 1 -2 0 1 0
## ZCWASHER ZDEHUMTYPE ZDESKTOP ZDISHWASH ZDOOR1SUM ZDRAFTY ZDRYER ZDRYRFUEL
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 -2
## 4 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 -2
## 6 0 0 0 0 0 0 0 0
## ZDRYRUSE ZDVD ZDWASHUSE ZDWCYCLE ZEDUCATION ZELPAY ZELPERIPH ZEMPLOYHH
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0
## 3 -2 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0
## 5 -2 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## ZENERGYASST ZENERGYASST16 ZENERGYASST17 ZENERGYASST18 ZENERGYASST19
## 1 0 -2 -2 -2 -2
## 2 0 -2 -2 -2 -2
## 3 0 -2 -2 -2 -2
## 4 0 -2 -2 -2 -2
## 5 0 -2 -2 -2 -2
## 6 0 -2 -2 -2 -2
## ZENERGYASST20 ZENERGYASSTOTH ZEQUIPAGE ZEQUIPAUXTYPE ZEQUIPM ZFOPAY ZFREEZER
## 1 -2 -2 0 0 0 -2 0
## 2 -2 -2 1 0 0 -2 0
## 3 -2 -2 0 0 1 -2 -2
## 4 -2 -2 0 0 0 -2 -2
## 5 -2 -2 0 0 0 -2 -2
## 6 -2 -2 1 0 0 -2 -2
## ZFUELAUX ZFUELH2O ZFUELH2O2 ZFUELHEAT ZFUELPOOL ZFUELTUB ZGARGCOOL ZGARGHEAT
## 1 -2 0 -2 0 -2 -2 0 0
## 2 0 0 -2 0 -2 -2 -2 -2
## 3 -2 0 -2 1 -2 -2 -2 -2
## 4 0 0 -2 0 -2 -2 0 0
## 5 -2 1 -2 0 -2 -2 -2 -2
## 6 0 0 -2 0 -2 -2 -2 -2
## ZH2OAPT ZH2OMAIN ZHEATAPT ZHEATCNTL ZHEATHOME ZHHAGE ZHHSEX ZHIGHCEIL ZHOTMA
## 1 -2 0 -2 0 0 0 0 0 0
## 2 0 -2 0 0 0 0 0 0 0
## 3 1 -2 0 0 0 0 0 0 0
## 4 -2 0 -2 0 0 0 0 0 0
## 5 0 -2 0 0 0 0 0 0 0
## 6 -2 0 -2 0 0 0 0 0 0
## ZHOUSEFAN ZHUMIDTYPE ZICE ZINTERNET ZINTSTREAM ZINTYPEBROAD ZINTYPECELL
## 1 0 0 0 0 0 0 0
## 2 -2 0 0 0 0 0 0
## 3 -2 0 -2 0 0 -2 -2
## 4 0 0 0 0 0 0 0
## 5 -2 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## ZINTYPEOTH ZKOWNRENT ZLGTIN1TO4 ZLGTIN4TO8 ZLGTINCAN ZLGTINCFL ZLGTINLED
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 -2 0 0 0 1 0 1
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## ZLGTINMORE8 ZLGTOUTANY ZLGTOUTCAN ZLGTOUTCFL ZLGTOUTLED ZLGTOUTNITE ZLOCRFRI2
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 -2
## 3 0 0 -2 -2 -2 -2 -2
## 4 0 0 -2 -2 -2 -2 0
## 5 0 0 -2 -2 -2 -2 0
## 6 0 0 1 1 1 1 1
## ZLPGPAY ZMICRO ZMONEYPY ZMONPOOL ZMONTUB ZMORETHAN1H2O ZNCOMBATH ZNGPAY
## 1 -2 0 0 -2 -2 0 0 0
## 2 -2 0 0 -2 -2 0 0 0
## 3 -2 0 0 -2 1 0 0 0
## 4 -2 0 1 -2 -2 0 0 0
## 5 -2 0 0 -2 -2 1 0 0
## 6 -2 0 0 -2 -2 0 0 0
## ZNHAFBATH ZNHSLDMEM ZNOACBROKE ZNOACDAYS ZNOACEL ZNOACHELP ZNOHEATBROKE
## 1 0 0 0 -2 0 -2 0
## 2 0 0 0 -2 0 -2 0
## 3 0 0 0 -2 0 -2 0
## 4 0 0 0 -2 0 -2 0
## 5 0 0 0 -2 0 -2 0
## 6 0 0 0 -2 0 -2 0
## ZNOHEATBULK ZNOHEATDAYS ZNOHEATEL ZNOHEATHELP ZNOHEATNG ZNUMADULT1 ZNUMADULT2
## 1 0 -2 0 -2 0 0 0
## 2 0 -2 0 -2 0 0 0
## 3 0 -2 0 -2 0 0 0
## 4 0 -2 0 -2 0 0 0
## 5 0 -2 0 -2 0 0 0
## 6 0 -2 0 -2 0 0 0
## ZNUMCFAN ZNUMCHILD ZNUMDLHP ZNUMDLHPAC ZNUMFIREPLC ZNUMFLOORFAN ZNUMFREEZ
## 1 0 0 -2 -2 -2 0 0
## 2 0 0 -2 -2 -2 0 0
## 3 0 0 -2 -2 -2 0 0
## 4 0 0 -2 -2 -2 0 0
## 5 0 0 -2 -2 -2 0 0
## 6 0 0 -2 -2 0 0 0
## ZNUMFRIG ZNUMLAPTOP ZNUMMEAL ZNUMPORTAC ZNUMPORTDEHUM ZNUMPORTEL ZNUMPORTHUM
## 1 0 0 0 -2 -2 -2 -2
## 2 0 0 0 -2 -2 0 -2
## 3 0 0 0 -2 -2 -2 -2
## 4 0 0 0 -2 -2 -2 -2
## 5 0 0 0 -2 -2 -2 -2
## 6 0 0 0 -2 -2 -2 -2
## ZNUMSMPHONE ZNUMTABLET ZNUMWWAC ZONLNEDUC ZORIGWIN ZOTHROOMS ZOUTGRILLFUEL
## 1 0 0 -2 0 0 0 0
## 2 0 0 -2 0 0 0 -2
## 3 0 0 -2 0 0 0 -2
## 4 0 0 -2 0 0 0 0
## 5 0 0 -2 0 1 0 -2
## 6 0 0 -2 0 1 0 0
## ZOUTLET ZOVEN ZOVENFUEL ZOVENUSE ZPAYHELP ZPLAYSTA ZPOOLPUMP ZPOWEROUT
## 1 0 0 -2 -2 -2 0 -2 0
## 2 -2 1 1 1 -2 0 -2 0
## 3 -2 0 -2 -2 -2 0 -2 0
## 4 0 0 0 0 -2 0 -2 0
## 5 -2 0 -2 -2 -2 0 -2 0
## 6 0 0 0 0 0 0 -2 0
## ZPRKGPLC1 ZPRSSCOOK ZRANGE ZRANGEFUEL ZRANGEINDT ZRCOOKUSE ZRECBATH ZRICECOOK
## 1 0 0 0 0 -2 0 0 0
## 2 -2 0 0 0 1 0 0 0
## 3 -2 0 0 0 0 0 0 0
## 4 0 0 0 0 -2 0 0 0
## 5 -2 0 0 0 -2 0 0 0
## 6 0 0 0 -2 -2 -2 0 0
## ZROOFTYPE ZROVENUSE ZSCALEB ZSCALEE ZSCALEG ZSDESCENT ZSEPDVR ZSIZEOFGARAGE
## 1 0 0 0 0 0 0 1 0
## 2 -2 0 0 0 0 0 0 -2
## 3 -2 0 0 0 0 0 0 -2
## 4 0 0 0 0 0 0 0 0
## 5 -2 0 0 0 0 0 0 -2
## 6 0 -2 0 0 0 0 0 -2
## ZSIZFREEZ ZSIZRFRI1 ZSIZRFRI2 ZSMARTSPK ZSQFTEST ZSQFTINCA ZSQFTINCB
## 1 0 0 0 0 0 -2 -2
## 2 0 0 -2 0 0 -2 -2
## 3 -2 -2 -2 -2 0 -2 -2
## 4 -2 0 0 0 0 -2 -2
## 5 -2 0 0 0 0 -2 -2
## 6 -2 0 0 0 0 1 -2
## ZSQFTINCG ZSQFTRANGE ZSSLIGHT ZSSOTHER ZSSSECURE ZSSTEMP ZSSTV ZSTORIES
## 1 0 0 -2 -2 -2 -2 -2 0
## 2 -2 0 -2 -2 -2 -2 -2 -2
## 3 -2 0 -2 -2 -2 -2 -2 -2
## 4 0 0 0 1 0 0 0 0
## 5 -2 0 -2 -2 -2 -2 -2 -2
## 6 -2 0 -2 -2 -2 -2 -2 0
## ZSWIMPOOL ZTELLDAYS ZTELLWORK ZTEMPGONE ZTEMPGONEAC ZTEMPHOME ZTEMPHOMEAC
## 1 0 -2 0 0 0 0 0
## 2 -2 -2 0 0 0 0 0
## 3 -2 -2 0 0 0 0 0
## 4 0 -2 0 0 0 0 0
## 5 -2 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## ZTEMPNITE ZTEMPNITEAC ZTLDESKTOP ZTLLAPTOP ZTLMONITOR ZTLOTHER ZTLTABLET
## 1 0 0 -2 -2 -2 -2 -2
## 2 0 0 -2 -2 -2 -2 -2
## 3 0 0 -2 -2 -2 -2 -2
## 4 0 0 -2 -2 -2 -2 -2
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## ZTOAST ZTOASTOVN ZTOPFRONT ZTREESHAD ZTVAUDIOSYS ZTVCOLOR ZTVONWD1 ZTVONWD2
## 1 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 -2
## 3 0 0 -2 0 0 0 0 -2
## 4 0 0 0 0 0 0 0 0
## 5 0 0 -2 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0
## ZTVONWD3 ZTVONWE1 ZTVONWE2 ZTVONWE3 ZTVSIZE1 ZTVSIZE2 ZTVSIZE3 ZTVTYPE1
## 1 0 0 0 0 0 0 0 0
## 2 -2 0 -2 -2 0 -2 -2 1
## 3 -2 0 -2 -2 0 -2 -2 0
## 4 -2 0 0 -2 0 0 -2 0
## 5 -2 0 0 -2 0 0 -2 0
## 6 -2 0 0 -2 0 0 -2 0
## ZTVTYPE2 ZTVTYPE3 ZTVUSE1 ZTVUSE2 ZTVUSE3 ZTYPEGLASS ZTYPERFR1 ZTYPERFR2
## 1 0 0 0 0 0 0 0 0
## 2 -2 -2 0 -2 -2 0 0 -2
## 3 -2 -2 0 -2 -2 0 -2 -2
## 4 0 -2 0 0 -2 0 0 0
## 5 0 -2 0 0 -2 0 0 -2
## 6 0 -2 0 0 -2 0 0 0
## ZTYPETHERM ZUGASHERE ZUPRTFRZR ZUSECFAN ZUSECOFFEE ZUSEDEHUM ZUSEEQUIPAUX
## 1 0 0 0 0 0 -2 -2
## 2 0 0 0 0 0 -2 0
## 3 0 0 -2 -2 0 -2 -2
## 4 0 0 -2 0 0 -2 0
## 5 0 1 -2 -2 0 -2 -2
## 6 0 1 -2 0 0 -2 0
## ZUSEHUMID ZVCR ZWALLTYPE ZWASHLOAD ZWASHTEMP ZWHEATAGE ZWHEATBKT ZWHEATSIZ
## 1 -2 0 0 0 0 0 0 0
## 2 -2 0 0 0 0 1 0 0
## 3 -2 0 0 -2 -2 0 -2 1
## 4 -2 0 0 0 0 0 0 0
## 5 -2 0 0 -2 -2 1 1 1
## 6 -2 0 0 0 0 1 0 0
## ZWHYPOWEROUT ZWINDOWS ZWINECHILL ZWINFRAME ZYEARMADERANGE ZTOTROOMS ZDNTHEAT
## 1 -2 0 0 0 0 0 -2
## 2 -2 0 0 0 0 0 -2
## 3 -2 0 0 0 0 0 -2
## 4 -2 0 0 0 0 0 -2
## 5 -2 0 0 0 0 0 -2
## 6 -2 0 0 0 0 0 -2
## ZTYPEHUQ ZSTUDIO ZOUTGRILL ZHOUSEHOLDER_RACE ZACEQUIPM_PUB
## 1 0 -2 0 0 0
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 -2 0 0 0
## 5 0 0 0 0 0
## 6 0 -2 0 0 0
## ZACEQUIPAUXTYPE_PUB NWEIGHT NWEIGHT1 NWEIGHT2 NWEIGHT3 NWEIGHT4 NWEIGHT5
## 1 0 3284.104 3273.053 3349.139 3344.876 3437.284 3415.582
## 2 0 9007.387 9019.564 9081.268 9020.409 9213.074 9117.337
## 3 0 5669.002 5793.353 5913.554 5762.743 5870.261 5720.669
## 4 0 5294.239 5361.146 5361.706 5371.011 5392.846 5327.617
## 5 0 9935.465 10047.545 10261.682 10036.522 9960.953 10107.863
## 6 0 7249.540 7339.185 7434.688 7335.660 7426.389 7309.238
## NWEIGHT6 NWEIGHT7 NWEIGHT8 NWEIGHT9 NWEIGHT10 NWEIGHT11 NWEIGHT12
## 1 3354.813 3372.342 3364.035 3361.912 3301.569 3211.291 3500.495
## 2 9178.697 9095.936 8920.480 9188.981 9060.009 9127.404 9264.304
## 3 5662.939 5699.536 5704.027 5667.670 5793.325 5806.321 5650.394
## 4 5353.957 5325.316 5375.732 5391.379 5500.628 5427.320 5384.442
## 5 10298.428 10064.709 10096.509 10321.424 9943.547 10266.593 10127.061
## 6 7472.039 7381.015 7364.809 7335.551 7318.790 7381.779 7318.179
## NWEIGHT13 NWEIGHT14 NWEIGHT15 NWEIGHT16 NWEIGHT17 NWEIGHT18 NWEIGHT19
## 1 3313.754 3359.110 3423.682 3383.601 3312.112 3324.383 3366.644
## 2 9222.011 9199.014 9143.214 9042.382 9416.815 9162.681 9191.950
## 3 5648.461 5828.712 5641.887 5717.847 5968.713 5828.370 5814.049
## 4 5302.085 5362.226 5383.136 5380.916 5418.300 5356.271 5343.187
## 5 10240.975 9871.649 10275.303 9921.199 10311.952 10004.213 10437.297
## 6 7255.528 7405.833 7463.357 7380.276 7386.023 7499.718 7510.696
## NWEIGHT20 NWEIGHT21 NWEIGHT22 NWEIGHT23 NWEIGHT24 NWEIGHT25 NWEIGHT26
## 1 3326.643 3339.910 3292.197 3277.697 3340.406 3386.445 3300.574
## 2 9091.550 0.000 9097.877 9319.896 9080.729 9406.487 9255.867
## 3 5697.447 5686.769 5738.946 5944.649 5819.996 5823.075 5650.188
## 4 5360.409 5336.323 5389.830 5397.093 5448.089 5382.111 5386.710
## 5 10100.730 9981.635 10000.278 10179.723 9825.700 10149.386 0.000
## 6 7367.150 7427.533 7353.516 7182.691 7465.987 7245.855 7309.158
## NWEIGHT27 NWEIGHT28 NWEIGHT29 NWEIGHT30 NWEIGHT31 NWEIGHT32 NWEIGHT33
## 1 3311.546 3347.637 3355.638 3322.423 3255.840 3317.937 3401.811
## 2 9318.078 9154.189 9371.695 9137.197 9233.363 9114.608 9176.872
## 3 5862.116 5706.909 5618.615 5795.544 5994.544 0.000 5637.872
## 4 5351.082 5371.439 5361.572 5381.218 5319.728 5338.558 0.000
## 5 10140.604 9948.403 10064.708 10082.927 10132.977 9978.370 10213.075
## 6 7291.371 7500.510 7336.362 7306.935 7314.812 7302.535 7326.240
## NWEIGHT34 NWEIGHT35 NWEIGHT36 NWEIGHT37 NWEIGHT38 NWEIGHT39 NWEIGHT40
## 1 3363.592 3303.528 3333.027 3389.869 3381.503 3328.893 3292.829
## 2 9191.207 9100.344 9071.530 9263.141 9077.901 9011.009 9166.222
## 3 5619.040 5652.289 5834.171 5712.198 5765.422 5887.338 5649.809
## 4 5379.523 5363.277 5476.866 5386.333 5326.402 5420.540 5370.189
## 5 9964.337 10070.847 9987.947 10120.314 10023.636 10023.919 10184.527
## 6 7241.113 0.000 7355.712 7358.491 7390.966 7312.146 7328.747
## NWEIGHT41 NWEIGHT42 NWEIGHT43 NWEIGHT44 NWEIGHT45 NWEIGHT46 NWEIGHT47
## 1 3295.089 3413.593 3263.710 3342.446 3275.274 3364.248 3336.066
## 2 9091.334 9193.664 9215.337 9048.092 9258.580 9170.518 9260.064
## 3 5957.748 5592.619 6035.472 5732.384 5876.696 5653.511 5763.458
## 4 5339.323 5328.788 5409.435 5416.488 5453.165 5449.444 5375.551
## 5 10069.084 9958.721 10352.485 10091.807 10227.529 10069.384 9995.686
## 6 7323.158 7434.563 7337.426 7460.085 7415.261 7398.415 7326.669
## NWEIGHT48 NWEIGHT49 NWEIGHT50 NWEIGHT51 NWEIGHT52 NWEIGHT53 NWEIGHT54
## 1 3329.151 3348.061 3357.231 3335.188 3240.132 3429.728 3294.084
## 2 9105.220 9116.891 9261.127 8955.288 9000.296 9290.375 9199.326
## 3 5928.968 5772.333 5785.452 5635.561 5944.330 5683.500 5735.631
## 4 5407.834 5399.779 5359.408 5447.619 5344.453 5437.803 5377.898
## 5 10197.608 10093.620 10196.251 10017.094 9954.142 10050.961 10018.844
## 6 7307.527 7406.582 7300.678 7379.108 7498.391 7417.977 7230.113
## NWEIGHT55 NWEIGHT56 NWEIGHT57 NWEIGHT58 NWEIGHT59 NWEIGHT60 KWH BTUEL
## 1 3397.713 3292.610 0.000 3369.768 3358.163 3404.031 12521.48 42723.28
## 2 8958.782 9232.597 9140.427 9306.997 9061.782 8957.915 5243.05 17889.29
## 3 5674.564 5660.854 5917.193 5571.015 5887.092 5837.846 2387.64 8146.63
## 4 5357.470 5421.025 5365.230 5402.057 5402.772 5350.860 9275.07 31646.53
## 5 10309.728 10142.763 10176.828 10043.143 10247.911 10110.301 5869.70 20027.42
## 6 7516.936 7370.323 7383.275 7507.097 7335.022 7594.642 14351.64 48967.79
## DOLLAREL ELXBTU PERIODEL ZELAMOUNT KWHSPH KWHCOL KWHWTH KWHRFG KWHRFG1
## 1 1955.06 3.412 1 0 0.00 3275.90 0 958.40 588.62
## 2 713.27 3.412 1 0 1844.58 1359.81 0 375.64 375.64
## 3 334.51 3.412 5 1 0.00 1583.68 0 0.00 0.00
## 4 1424.86 3.412 1 0 2994.31 1472.33 0 965.40 639.25
## 5 1087.00 3.412 1 0 0.00 1872.74 0 987.24 673.42
## 6 1895.66 3.412 1 0 0.00 7310.65 0 1374.54 772.88
## KWHRFG2 KWHFRZ KWHCOK KWHMICRO KWHCW KWHCDR KWHDWH KWHLGT KWHTVREL KWHTV1
## 1 369.78 809.41 0.00 374.14 115.25 2717.97 365.30 630.38 649.12 144.84
## 2 0.00 184.91 250.14 0.00 32.00 164.54 40.10 220.08 87.55 63.52
## 3 0.00 0.00 65.06 66.73 0.00 0.00 39.84 69.11 237.95 154.61
## 4 326.14 0.00 266.78 206.94 65.31 562.51 204.07 683.32 887.08 361.18
## 5 313.82 0.00 0.00 53.55 0.00 0.00 195.47 844.72 360.43 162.67
## 6 601.65 0.00 1175.08 140.43 89.14 0.00 184.83 920.25 358.34 86.22
## KWHTV2 KWHTV3 KWHAHUHEAT KWHAHUCOL KWHCFAN KWHDHUM KWHHUM KWHPLPMP KWHHTBPMP
## 1 60.88 24.33 309.06 644.76 432.46 0 0 0 0
## 2 0.00 0.00 27.36 192.09 19.93 0 0 0 0
## 3 0.00 0.00 16.37 132.49 0.00 0 0 0 0
## 4 125.75 0.00 314.40 288.50 80.71 0 0 0 0
## 5 30.36 0.00 44.32 309.60 0.00 0 0 0 0
## 6 36.27 0.00 89.56 961.41 30.50 0 0 0 0
## KWHHTBHEAT KWHEVCHRG KWHNEC KWHOTH BTUELSPH BTUELCOL BTUELWTH BTUELRFG
## 1 0 0 1239.43 8287.27 0.00 11177.38 0 3270.05
## 2 0 0 444.39 1663.09 6293.72 4639.66 0 1281.67
## 3 0 0 176.40 803.96 0.00 5403.53 0 0.00
## 4 0 0 283.54 3843.17 10216.57 5023.60 0 3293.93
## 5 0 0 1201.78 3009.87 0.00 6389.78 0 3368.46
## 6 0 0 1716.98 5666.52 0.00 24943.94 0 4689.92
## BTUELRFG1 BTUELRFG2 BTUELFRZ BTUELCOK BTUELMICRO BTUELCW BTUELCDR BTUELDWH
## 1 2008.36 1261.69 2761.71 0.00 1276.56 393.24 9273.72 1246.40
## 2 1281.67 0.00 630.92 853.47 0.00 109.19 561.41 136.82
## 3 0.00 0.00 0.00 222.00 227.68 0.00 0.00 135.94
## 4 2181.13 1112.80 0.00 910.26 706.09 222.83 1919.28 696.29
## 5 2297.72 1070.74 0.00 0.00 182.72 0.00 0.00 666.93
## 6 2637.08 2052.84 0.00 4009.38 479.16 304.15 0.00 630.63
## BTUELLGT BTUELTVREL BTUELTV1 BTUELTV2 BTUELTV3 BTUELAHUHEAT BTUELAHUCOL
## 1 2150.85 2214.79 494.21 207.71 83.02 1054.52 2199.90
## 2 750.90 298.73 216.73 0.00 0.00 93.35 655.41
## 3 235.81 811.89 527.53 0.00 0.00 55.87 452.05
## 4 2331.49 3026.71 1232.35 429.06 0.00 1072.75 984.36
## 5 2882.19 1229.79 555.02 103.57 0.00 151.21 1056.36
## 6 3139.90 1222.64 294.18 123.75 0.00 305.57 3280.33
## BTUELCFAN BTUELDHUM BTUELHUM BTUELPLPMP BTUELHTBPMP BTUELHTBHEAT BTUELEVCHRG
## 1 1475.55 0 0 0 0 0 0
## 2 67.99 0 0 0 0 0 0
## 3 0.00 0 0 0 0 0 0
## 4 275.40 0 0 0 0 0 0
## 5 0.00 0 0 0 0 0 0
## 6 104.08 0 0 0 0 0 0
## BTUELNEC BTUELOTH DOLELSPH DOLELCOL DOLELWTH DOLELRFG DOLELRFG1 DOLELRFG2
## 1 4228.93 28276.18 0.00 511.49 0 149.64 91.90 57.74
## 2 1516.27 5674.46 250.94 184.99 0 51.10 51.10 0.00
## 3 601.88 2743.10 0.00 221.88 0 0.00 0.00 0.00
## 4 967.44 13112.90 459.99 226.18 0 148.31 98.20 50.10
## 5 4100.46 10269.66 0.00 346.81 0 182.83 124.71 58.12
## 6 5858.32 19334.17 0.00 965.64 0 181.56 102.09 79.47
## DOLELFRZ DOLELCOK DOLELMICRO DOLELCW DOLELCDR DOLELDWH DOLELLGT DOLELTVREL
## 1 126.38 0.00 58.42 17.99 424.38 57.04 98.43 101.35
## 2 25.16 34.03 0.00 4.35 22.38 5.46 29.94 11.91
## 3 0.00 9.12 9.35 0.00 0.00 5.58 9.68 33.34
## 4 0.00 40.98 31.79 10.03 86.41 31.35 104.97 136.28
## 5 0.00 0.00 9.92 0.00 0.00 36.20 156.43 66.75
## 6 0.00 155.21 18.55 11.77 0.00 24.41 121.55 47.33
## DOLELTV1 DOLELTV2 DOLELTV3 DOLELAHUHEAT DOLELAHUCOL DOLELCFAN DOLELDHUM
## 1 22.62 9.50 3.8 48.26 100.67 67.52 0
## 2 8.64 0.00 0.0 3.72 26.13 2.71 0
## 3 21.66 0.00 0.0 2.29 18.56 0.00 0
## 4 55.49 19.32 0.0 48.30 44.32 12.40 0
## 5 30.12 5.62 0.0 8.21 57.33 0.00 0
## 6 11.39 4.79 0.0 11.83 126.99 4.03 0
## DOLELHUM DOLELPLPMP DOLELHTBPMP DOLELHTBHEAT DOLELEVCHRG DOLELNEC DOLELOTH
## 1 0 0 0 0 0 193.52 1293.95
## 2 0 0 0 0 0 60.46 226.25
## 3 0 0 0 0 0 24.71 112.64
## 4 0 0 0 0 0 43.56 590.40
## 5 0 0 0 0 0 222.56 557.39
## 6 0 0 0 0 0 226.79 748.47
## CUFEETNG BTUNG DOLLARNG NGXBTU PERIODNG ZNGAMOUNT BTUNGSPH BTUNGWTH
## 1 1217.39 101924.43 701.83 83.723 1 0 73942.18 21241.34
## 2 104.51 10145.32 261.73 97.076 1 0 0.00 10145.32
## 3 266.93 22603.08 188.14 84.678 5 1 16993.72 5609.36
## 4 552.91 55118.66 636.91 99.687 1 0 32595.55 21061.21
## 5 376.68 39099.51 376.04 103.802 1 0 8818.47 27944.03
## 6 357.42 36432.85 439.42 101.934 1 0 14561.39 19508.22
## BTUNGCOK BTUNGCDR BTUNGPLHEAT BTUNGHTBHEAT BTUNGNEC BTUNGOTH CUFEETNGSPH
## 1 6740.89 0.00 0 0 0 6740.89 883.17
## 2 0.00 0.00 0 0 0 0.00 0.00
## 3 0.00 0.00 0 0 0 0.00 200.69
## 4 1461.88 0.00 0 0 0 1461.88 326.98
## 5 2336.99 0.00 0 0 0 2336.99 84.96
## 6 0.00 2363.24 0 0 0 2363.24 142.85
## CUFEETNGWTH CUFEETNGCOK CUFEETNGCDR CUFEETNGPLHEAT CUFEETNGHTBHEAT
## 1 253.71 80.51 0.00 0 0
## 2 104.51 0.00 0.00 0 0
## 3 66.24 0.00 0.00 0 0
## 4 211.27 14.66 0.00 0 0
## 5 269.21 22.51 0.00 0 0
## 6 191.38 0.00 23.18 0 0
## CUFEETNGNEC CUFEETNGOTH DOLNGSPH DOLNGWTH DOLNGCOK DOLNGCDR DOLNGPLHEAT
## 1 0 80.51 509.15 146.26 46.42 0.0 0
## 2 0 0.00 0.00 261.73 0.00 0.0 0
## 3 0 0.00 141.45 46.69 0.00 0.0 0
## 4 0 14.66 376.65 243.37 16.89 0.0 0
## 5 0 22.51 84.81 268.75 22.48 0.0 0
## 6 0 23.18 175.62 235.29 0.00 28.5 0
## DOLNGHTBHEAT DOLNGNEC DOLNGOTH GALLONLP BTULP DOLLARLP LPXBTU PERIODLP
## 1 0 0 46.42 0 0 0 -2 -2
## 2 0 0 0.00 0 0 0 -2 -2
## 3 0 0 0.00 0 0 0 -2 -2
## 4 0 0 16.89 0 0 0 -2 -2
## 5 0 0 22.48 0 0 0 -2 -2
## 6 0 0 28.50 0 0 0 -2 -2
## ZLPAMOUNT BTULPSPH BTULPWTH BTULPCOK BTULPCDR BTULPNEC BTULPOTH GALLONLPSPH
## 1 -2 0 0 0 0 0 0 0
## 2 -2 0 0 0 0 0 0 0
## 3 -2 0 0 0 0 0 0 0
## 4 -2 0 0 0 0 0 0 0
## 5 -2 0 0 0 0 0 0 0
## 6 -2 0 0 0 0 0 0 0
## GALLONLPWTH GALLONLPCOK GALLONLPCDR GALLONLPNEC GALLONLPOTH DOLLPSPH DOLLPWTH
## 1 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0
## DOLLPCOK DOLLPCDR DOLLPNEC DOLLPOTH GALLONFO BTUFO DOLLARFO FOXBTU PERIODFO
## 1 0 0 0 0 0 0 0 -2 -2
## 2 0 0 0 0 0 0 0 -2 -2
## 3 0 0 0 0 0 0 0 -2 -2
## 4 0 0 0 0 0 0 0 -2 -2
## 5 0 0 0 0 0 0 0 -2 -2
## 6 0 0 0 0 0 0 0 -2 -2
## ZFOAMOUNT BTUFOSPH BTUFOWTH BTUFONEC BTUFOOTH GALLONFOSPH GALLONFOWTH
## 1 -2 0 0 0 0 0 0
## 2 -2 0 0 0 0 0 0
## 3 -2 0 0 0 0 0 0
## 4 -2 0 0 0 0 0 0
## 5 -2 0 0 0 0 0 0
## 6 -2 0 0 0 0 0 0
## GALLONFONEC GALLONFOOTH DOLFOSPH DOLFOWTH DOLFONEC DOLFOOTH BTUWD ZWDAMOUNT
## 1 0 0 0 0 0 0 0 -2
## 2 0 0 0 0 0 0 0 -2
## 3 0 0 0 0 0 0 0 -2
## 4 0 0 0 0 0 0 0 -2
## 5 0 0 0 0 0 0 0 -2
## 6 0 0 0 0 0 0 3000 0
## TOTALBTUSPH TOTALDOLSPH TOTALBTUWTH TOTALDOLWTH TOTALBTUOTH TOTALDOLOTH
## 1 73942.18 509.15 21241.34 146.26 35017.07 1340.37
## 2 6293.72 250.94 10145.32 261.73 5674.46 226.25
## 3 16993.72 141.45 5609.36 46.69 2743.10 112.64
## 4 42812.12 836.64 21061.21 243.37 14574.78 607.29
## 5 8818.47 84.81 27944.03 268.75 12606.65 579.87
## 6 14561.39 175.62 19508.22 235.29 21697.41 776.97
## TOTALBTU TOTALDOL DBT1 DBT99 GWT WOODTYPE OUTLET ELECVEH EVCHRGHOME
## 1 144647.71 2656.89 92.9 21.6 58.5 -2 1 0 -2
## 2 28034.61 975.00 91.3 16.1 57.2 -2 -2 0 -2
## 3 30749.71 522.65 92.9 21.6 58.5 -2 -2 0 -2
## 4 86765.19 2061.77 91.8 25.1 61.5 -2 1 0 -2
## 5 59126.93 1463.04 81.8 13.0 55.9 -2 -2 0 -2
## 6 85400.64 2335.08 93.4 35.5 72.2 1 1 0 -2
## EVCHRGAPT EVCHRGWKS EVCHRGBUS EVCHRGMUNI EVCHRGDLR EVCHRGHWY EVCHRGOTH
## 1 -2 -2 -2 -2 -2 -2 -2
## 2 -2 -2 -2 -2 -2 -2 -2
## 3 -2 -2 -2 -2 -2 -2 -2
## 4 -2 -2 -2 -2 -2 -2 -2
## 5 -2 -2 -2 -2 -2 -2 -2
## 6 -2 -2 -2 -2 -2 -2 -2
## EVHOMEAMT EVCHRGTYPE
## 1 -2 -2
## 2 -2 -2
## 3 -2 -2
## 4 -2 -2
## 5 -2 -2
## 6 -2 -2clean_Renewable_Energy <- Renewable_Energy |>
mutate(
renewable_adoption = case_when(
SOLAR == 1 | USESOLAR == 1 ~ 1,
SOLAR == 0 | USESOLAR == 0 ~ 0,
TRUE ~ NA_real_
)
) |>
mutate(
household_income = MONEYPY,
household_age = HHAGE,
household_size = NHSLDMEM,
square_footage = TOTSQFT_EN,
electricity_cost = DOLLAREL,
cooling_degree_days = CDD65,
state = state_postal,
region = REGIONC,
urban_rural = UATYP10,
energy_assistance = ifelse(ENERGYASST == 1, 1, 0),
owns_home = ifelse(KOWNRENT == 1, 1, 0),
year_built_range = YEARMADERANGE) |>
select(
renewable_adoption,
household_income,
household_age,
household_size,
square_footage,
electricity_cost,
cooling_degree_days,
energy_assistance,
owns_home,
state,
region,
urban_rural) |>
filter(!is.na(renewable_adoption))
summary(clean_Renewable_Energy)## renewable_adoption household_income household_age household_size
## Min. :0.00000 Min. : 1.00 Min. :18.00 Min. :1.000
## 1st Qu.:0.00000 1st Qu.: 9.00 1st Qu.:40.00 1st Qu.:1.000
## Median :0.00000 Median :13.00 Median :56.00 Median :2.000
## Mean :0.03758 Mean :11.64 Mean :54.78 Mean :2.461
## 3rd Qu.:0.00000 3rd Qu.:15.00 3rd Qu.:68.00 3rd Qu.:3.000
## Max. :1.00000 Max. :16.00 Max. :90.00 Max. :7.000
## square_footage electricity_cost cooling_degree_days energy_assistance
## Min. : 200 Min. : -889.5 Min. : 0 Min. :0.00000
## 1st Qu.: 1100 1st Qu.: 836.5 1st Qu.: 814 1st Qu.:0.00000
## Median : 1700 Median : 1257.9 Median :1179 Median :0.00000
## Mean : 1960 Mean : 1424.8 Mean :1526 Mean :0.04617
## 3rd Qu.: 2510 3rd Qu.: 1819.0 3rd Qu.:1805 3rd Qu.:0.00000
## Max. :15000 Max. :15680.2 Max. :5534 Max. :1.00000
## owns_home state region urban_rural
## Min. :0.0000 Length:18496 Length:18496 Length:18496
## 1st Qu.:0.0000 Class :character Class :character Class :character
## Median :1.0000 Mode :character Mode :character Mode :character
## Mean :0.7272
## 3rd Qu.:1.0000
## Max. :1.0000#Data Analysis
clean_Renewable_Energy |>
mutate(renewable_adoption = factor(renewable_adoption, levels = c(0, 1), labels = c("No", "Yes"))) |>
ggplot(aes(x = renewable_adoption)) +
geom_bar(fill = c("lightblue")) +
labs(title = "Counts of Yes and No Responses",
x = "Responses",
y = "Count")
data.frame(clean_Renewable_Energy)## renewable_adoption household_income household_age household_size
## 1 0 13 65 2
## 2 0 6 79 1
## 3 0 11 82 1
## 4 0 10 70 2
## 5 0 16 30 2
## 6 0 15 54 4
## 7 0 11 40 6
## 8 0 8 51 2
## 9 0 14 32 1
## 10 0 9 58 1
## 11 1 12 69 1
## 12 0 7 53 5
## 13 0 13 63 2
## 14 0 2 59 1
## 15 0 11 64 1
## 16 0 9 22 4
## 17 0 16 84 2
## 18 0 16 83 2
## 19 0 9 28 3
## 20 0 14 20 3
## 21 0 2 66 1
## 22 0 15 51 3
## 23 0 16 61 2
## 24 0 14 83 2
## 25 0 1 43 1
## 26 1 15 28 5
## 27 0 16 28 4
## 28 1 9 74 2
## 29 0 9 69 2
## 30 0 12 75 2
## 31 0 15 70 2
## 32 0 13 59 2
## 33 0 13 44 4
## 34 0 16 49 2
## 35 1 11 71 3
## 36 0 15 39 2
## 37 0 14 80 2
## 38 0 16 41 4
## 39 0 12 83 1
## 40 0 11 68 2
## 41 0 16 41 4
## 42 0 14 54 2
## 43 0 9 68 2
## 44 0 13 80 2
## 45 0 15 35 4
## 46 0 15 75 4
## 47 0 14 43 3
## 48 0 11 40 3
## 49 0 9 36 1
## 50 0 14 51 2
## 51 0 15 68 2
## 52 0 16 60 1
## 53 0 15 28 2
## 54 0 11 45 2
## 55 0 15 73 2
## 56 0 9 40 2
## 57 0 14 29 3
## 58 0 15 70 2
## 59 0 5 46 2
## 60 0 9 82 1
## 61 0 2 32 4
## 62 0 12 40 1
## 63 0 15 60 1
## 64 0 6 62 3
## 65 0 1 29 2
## 66 0 16 56 4
## 67 0 13 41 2
## 68 0 12 75 2
## 69 0 12 37 1
## 70 0 7 66 2
## 71 0 10 50 6
## 72 0 8 26 3
## 73 0 10 48 5
## 74 0 16 37 3
## 75 0 11 72 2
## 76 0 11 89 1
## 77 0 13 36 2
## 78 0 9 63 4
## 79 0 10 53 2
## 80 0 15 46 1
## 81 0 4 72 1
## 82 0 3 62 1
## 83 0 11 66 2
## 84 0 13 24 2
## 85 0 14 70 1
## 86 0 15 35 7
## 87 0 3 61 2
## 88 0 6 90 1
## 89 0 9 43 2
## 90 0 11 64 2
## 91 0 13 37 7
## 92 0 16 66 2
## 93 1 14 75 2
## 94 0 1 26 2
## 95 0 7 76 1
## 96 0 14 55 2
## 97 0 16 74 2
## 98 0 9 53 5
## 99 0 7 74 2
## 100 0 6 73 1
## 101 0 15 39 6
## 102 0 13 64 2
## 103 0 9 70 2
## 104 0 13 53 2
## 105 0 4 90 1
## 106 0 13 69 2
## 107 0 16 62 2
## 108 0 15 51 4
## 109 0 10 47 2
## 110 0 12 33 4
## 111 0 1 24 4
## 112 0 11 36 4
## 113 0 11 35 7
## 114 0 4 89 1
## 115 0 16 65 4
## 116 0 13 69 2
## 117 0 15 27 1
## 118 0 10 42 5
## 119 0 16 22 4
## 120 0 15 55 1
## 121 0 16 63 4
## 122 0 16 47 4
## 123 0 1 32 4
## 124 0 16 58 2
## 125 0 6 85 5
## 126 0 14 66 3
## 127 0 15 29 3
## 128 1 15 73 3
## 129 0 14 35 1
## 130 0 16 59 2
## 131 0 13 71 2
## 132 0 9 78 3
## 133 0 12 63 2
## 134 0 12 30 1
## 135 0 9 63 1
## 136 0 4 63 1
## 137 0 16 44 4
## 138 0 16 50 3
## 139 0 12 57 4
## 140 0 14 48 2
## 141 0 12 56 3
## 142 0 16 71 1
## 143 0 13 37 3
## 144 0 14 41 4
## 145 0 12 36 1
## 146 0 8 86 1
## 147 0 15 56 3
## 148 0 9 51 1
## 149 0 14 38 1
## 150 0 16 65 6
## 151 0 13 70 2
## 152 0 16 46 3
## 153 0 8 84 1
## 154 0 8 35 6
## 155 0 15 59 2
## 156 0 13 82 2
## 157 0 6 73 1
## 158 1 16 66 2
## 159 0 5 88 1
## 160 0 16 69 3
## 161 0 12 85 2
## 162 0 13 78 3
## 163 0 11 75 1
## 164 0 13 34 1
## 165 1 16 19 7
## 166 0 16 53 2
## 167 0 16 57 2
## 168 0 6 69 1
## 169 0 13 82 1
## 170 0 13 63 6
## 171 0 4 47 4
## 172 0 11 34 4
## 173 0 14 61 4
## 174 0 8 68 2
## 175 0 14 61 2
## 176 0 14 61 2
## 177 0 14 79 3
## 178 0 8 71 2
## 179 0 13 72 2
## 180 0 14 62 3
## 181 0 14 54 2
## 182 0 15 65 2
## 183 0 15 29 2
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## 8285 0 9 24 2
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## square_footage electricity_cost cooling_degree_days energy_assistance
## 1 2100 1955.06 1679 0
## 2 590 713.27 1458 0
## 3 900 334.51 1696 0
## 4 2100 1424.86 1718 0
## 5 800 1087.00 1363 0
## 6 4520 1895.66 3558 0
## 7 2100 1418.04 2128 0
## 8 900 1237.05 2374 0
## 9 750 537.68 1159 0
## 10 760 625.41 3812 0
## 11 1630 65.11 2108 0
## 12 1540 1136.61 3037 0
## 13 2000 1004.20 2671 0
## 14 1450 644.85 189 1
## 15 1650 842.06 284 0
## 16 1350 2162.73 2117 0
## 17 1910 1988.48 869 0
## 18 2650 622.02 562 0
## 19 1980 1257.34 1764 0
## 20 1300 1549.78 2850 0
## 21 1850 377.64 1227 1
## 22 1130 910.09 1237 0
## 23 2730 1730.06 635 0
## 24 2640 1142.86 886 0
## 25 1430 664.17 1051 1
## 26 2440 2348.58 795 0
## 27 3000 1307.94 881 0
## 28 1490 186.16 734 1
## 29 1810 1747.09 3628 0
## 30 2500 1442.29 997 0
## 31 2850 1347.02 878 0
## 32 1980 1036.65 955 0
## 33 5450 3497.17 4424 0
## 34 2750 1523.05 872 0
## 35 2700 437.54 802 0
## 36 1600 913.75 2158 0
## 37 2250 624.81 943 0
## 38 3300 2579.28 1454 0
## 39 1700 364.17 855 0
## 40 1950 1712.81 939 0
## 41 2000 1405.98 2515 0
## 42 2000 1644.89 2987 0
## 43 780 1379.96 1129 1
## 44 820 858.50 449 0
## 45 2100 1046.10 774 0
## 46 1500 2446.33 1042 0
## 47 1300 1183.31 1640 1
## 48 1560 777.26 837 0
## 49 1100 1389.12 1501 0
## 50 2800 784.69 4097 1
## 51 3750 2390.43 748 0
## 52 3230 638.56 1134 0
## 53 1400 869.82 1526 0
## 54 900 1502.78 712 1
## 55 5510 1049.02 828 0
## 56 2000 1102.84 675 0
## 57 700 1276.88 1336 0
## 58 1730 2071.37 1151 0
## 59 750 939.24 1216 1
## 60 2050 811.04 876 0
## 61 1020 2881.34 1357 0
## 62 980 558.46 409 0
## 63 2070 1542.53 1891 0
## 64 2200 3226.06 1831 0
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## [ reached 'max' / getOption("max.print") -- omitted 10163 rows ]cor(clean_Renewable_Energy[, 1:9])## renewable_adoption household_income household_age
## renewable_adoption 1.00000000 0.07869787 0.032205418
## household_income 0.07869787 1.00000000 -0.070577655
## household_age 0.03220542 -0.07057765 1.000000000
## household_size 0.06175503 0.18099588 -0.342915384
## square_footage 0.06297297 0.37522834 0.141425294
## electricity_cost -0.07596668 0.22233532 -0.017047888
## cooling_degree_days 0.07025145 -0.04216653 -0.002049401
## energy_assistance -0.02044275 -0.24795904 -0.008380277
## owns_home 0.09485660 0.33217413 0.298308506
## household_size square_footage electricity_cost
## renewable_adoption 0.06175503 0.06297297 -0.07596668
## household_income 0.18099588 0.37522834 0.22233532
## household_age -0.34291538 0.14142529 -0.01704789
## household_size 1.00000000 0.18687343 0.33189806
## square_footage 0.18687343 1.00000000 0.37653377
## electricity_cost 0.33189806 0.37653377 1.00000000
## cooling_degree_days 0.02820336 -0.08967956 0.14255637
## energy_assistance 0.01192295 -0.09837396 -0.04231825
## owns_home 0.06026742 0.42088124 0.21769616
## cooling_degree_days energy_assistance owns_home
## renewable_adoption 0.070251451 -0.020442748 0.09485660
## household_income -0.042166534 -0.247959036 0.33217413
## household_age -0.002049401 -0.008380277 0.29830851
## household_size 0.028203361 0.011922948 0.06026742
## square_footage -0.089679560 -0.098373962 0.42088124
## electricity_cost 0.142556374 -0.042318246 0.21769616
## cooling_degree_days 1.000000000 -0.025911892 -0.03076279
## energy_assistance -0.025911892 1.000000000 -0.11106473
## owns_home -0.030762789 -0.111064730 1.00000000summary_table <- clean_Renewable_Energy |>
group_by(renewable_adoption) |>
summarize(
n = n(),
mean_income = mean(household_income, na.rm = TRUE),
mean_age = mean(household_age, na.rm = TRUE),
mean_sqft = mean(square_footage, na.rm = TRUE),
mean_elec_cost = mean(electricity_cost, na.rm = TRUE),
pct_owns_home = mean(owns_home, na.rm = TRUE) * 100
)
print(summary_table)## # A tibble: 2 × 7
## renewable_adoption n mean_income mean_age mean_sqft mean_elec_cost
## <dbl> <int> <dbl> <dbl> <dbl> <dbl>
## 1 0 17801 11.6 54.7 1945. 1438.
## 2 1 695 13.2 57.6 2331. 1093.
## # ℹ 1 more variable: pct_owns_home <dbl>clean_Renewable_Energy |>
mutate(renewable_adoption = factor(renewable_adoption, levels = c(0, 1), labels = c("No", "Yes"))) |>
ggplot(aes(x = renewable_adoption, y = household_income, fill = renewable_adoption)) +
geom_boxplot() +
scale_fill_manual(values = c("No" = "#A52A2A", "Yes" = "#5F9EA0")) +
labs(title = "Household Income by Adoption Status",
x = "Renewable Energy Adoption",
y = "Household Income") +
theme_minimal()
clean_Renewable_Energy |>
mutate(renewable_adoption = factor(renewable_adoption, levels = c(0, 1), labels = c("No", "Yes"))) |>
ggplot(aes(x = renewable_adoption, y = electricity_cost, fill = renewable_adoption)) +
geom_boxplot() +
scale_fill_manual(values = c("No" = "#A52A2A", "Yes" = "#5F9EA0")) +
labs(title = "Electricity Cost by Adoption Status",
x = "Renewable Energy Adoption",
y = "Electricity Cost ($)") +
theme_minimal()
clean_Renewable_Energy |>
group_by(region, renewable_adoption) |>
summarise(count = n(), .groups = "drop") |>
group_by(region) |>
mutate(proportion = count / sum(count)) |>
filter(renewable_adoption == 1) |>
ggplot(aes(x = reorder(region, proportion), y = proportion, fill = region)) +
geom_bar(stat = "identity") +
geom_text(aes(label = paste0(round(proportion * 100, 1), "%")), hjust = -0.1) +
scale_y_continuous(labels = scales::percent) +
labs(title = "Renewable Energy Adoption Rate by Region",
x = "Census Region",
y = "Adoption Rate") +
theme_minimal()
To address the research question, I will conduct a logistic regression
analysis to identify what factors most significantly affect and predict
renewable energy adoption.To clean the data, I use the mutate function
to create binary variable, select to chose only the relevant columns,
filter to remove missing data, group_by, and summarize to generate
summary statistics. I then create multiple visualization including: bar
charts comparing amount of households using renewable energy vs those
not using renewable energy, a data frame to structure the data and get a
general overview, a correlation matrix to see how each variable affects
whether someone chooses clean energy or not, box plots comparing
continuous predictors, a summary table showing adoption rates across the
US, and later diagnostic plots to determine the models fit and check if
all assumptions are met. The character variable “state”, “region” and
“urban_rural” are excluded from this correlation analysis as they cannot
be in numeric format, however, region was visualized separately to show
geographical adoption patterns.
Regression Analysis
logistic <- glm(renewable_adoption ~ household_income + household_age + household_size + square_footage + electricity_cost + cooling_degree_days + energy_assistance + owns_home, data=clean_Renewable_Energy, family = binomial(link = "logit"))
summary(logistic)##
## Call:
## glm(formula = renewable_adoption ~ household_income + household_age +
## household_size + square_footage + electricity_cost + cooling_degree_days +
## energy_assistance + owns_home, family = binomial(link = "logit"),
## data = clean_Renewable_Energy)
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -7.319e+00 3.113e-01 -23.515 < 2e-16 ***
## household_income 1.172e-01 1.494e-02 7.845 4.33e-15 ***
## household_age 1.400e-02 2.848e-03 4.916 8.82e-07 ***
## household_size 3.911e-01 3.074e-02 12.722 < 2e-16 ***
## square_footage 2.106e-04 3.581e-05 5.881 4.08e-09 ***
## electricity_cost -1.442e-03 7.428e-05 -19.416 < 2e-16 ***
## cooling_degree_days 4.433e-04 3.057e-05 14.504 < 2e-16 ***
## energy_assistance 2.349e-02 2.637e-01 0.089 0.929
## owns_home 1.630e+00 1.722e-01 9.467 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 5924.7 on 18495 degrees of freedom
## Residual deviance: 4997.4 on 18487 degrees of freedom
## AIC: 5015.4
##
## Number of Fisher Scoring iterations: 7r_square <- 1 - (logistic$deviance/logistic$null.deviance)
r_square## [1] 0.15650561 - pchisq(logistic$null.deviance - logistic$deviance, df = logistic$df.null - logistic$df.residual)## [1] 0confint(logistic)## Waiting for profiling to be done...## 2.5 % 97.5 %
## (Intercept) -7.9406541571 -6.7200319235
## household_income 0.0884095430 0.1469935538
## household_age 0.0084385556 0.0196046689
## household_size 0.3305594652 0.4511156181
## square_footage 0.0001395389 0.0002800605
## electricity_cost -0.0015896204 -0.0012984051
## cooling_degree_days 0.0003830764 0.0005029457
## energy_assistance -0.5289756164 0.5108881507
## owns_home 1.3045681721 1.9812405889predicted.data <- data.frame(
probability = logistic$fitted.values,
adoption = clean_Renewable_Energy$renewable_adoption)
predicted.data## probability adoption
## 1 1.619582e-02 0
## 2 4.599705e-03 0
## 3 1.742879e-02 0
## 4 2.643517e-02 0
## 5 6.451145e-03 0
## 6 3.089637e-02 0
## 7 2.224621e-02 0
## 8 4.373703e-03 0
## 9 7.081096e-03 0
## 10 1.608780e-02 0
## 11 1.490727e-01 1
## 12 1.052683e-01 0
## 13 8.750173e-02 0
## 14 8.532968e-03 0
## 15 2.075256e-02 0
## 16 1.853810e-03 0
## 17 1.914573e-02 0
## 18 1.234953e-01 0
## 19 4.899459e-03 0
## 20 7.222705e-03 0
## 21 4.690805e-03 0
## 22 7.107781e-02 0
## 23 2.152667e-02 0
## 24 5.711788e-02 0
## 25 8.608061e-03 0
## 26 1.622479e-02 1
## 27 6.170037e-02 0
## 28 8.150393e-02 1
## 29 3.178202e-02 0
## 30 2.758827e-02 0
## 31 4.219167e-02 0
## 32 3.870574e-02 0
## 33 1.945098e-02 0
## 34 2.720243e-02 0
## 35 1.256864e-01 1
## 36 6.739713e-02 0
## 37 1.038175e-01 0
## 38 1.701976e-02 0
## 39 1.555076e-02 0
## 40 1.326433e-02 0
## 41 1.027183e-01 0
## 42 8.431201e-03 0
## 43 1.473995e-02 0
## 44 4.188913e-02 0
## 45 6.911918e-02 0
## 46 1.683449e-02 0
## 47 4.947353e-02 0
## 48 8.851411e-03 0
## 49 1.537695e-03 0
## 50 2.219071e-01 0
## 51 1.073523e-02 0
## 52 8.939739e-02 0
## 53 4.563646e-02 0
## 54 9.691980e-03 0
## 55 1.078822e-01 0
## 56 1.532965e-02 0
## 57 5.479687e-03 0
## 58 1.362654e-02 0
## 59 2.621692e-03 0
## 60 3.091665e-02 0
## 61 2.221497e-04 0
## 62 4.588359e-03 0
## 63 2.531465e-02 0
## 64 1.791865e-03 0
## 65 6.063333e-04 0
## 66 4.721984e-03 0
## 67 1.140508e-02 0
## 68 3.684681e-02 0
## 69 4.987146e-03 0
## 70 4.901684e-02 0
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## 18496 1.654844e-02 0predicted_probs <- predict(logistic, type = "response")
plot(clean_Renewable_Energy$household_income, predicted_probs,
xlab = "Household Income",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Household Income")
lines(lowess(clean_Renewable_Energy$household_income, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$household_size, predicted_probs,
xlab = "Household Size",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Household Size")
lines(lowess(clean_Renewable_Energy$household_size, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$household_age, predicted_probs,
xlab = "Household Age",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Household Age")
lines(lowess(clean_Renewable_Energy$household_age, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$square_footage, predicted_probs,
xlab = "Square Footage",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Square Footage")
lines(lowess(clean_Renewable_Energy$square_footage, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$electricity_cost, predicted_probs,
xlab = "Electricity Cost",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Electricity Cost")
lines(lowess(clean_Renewable_Energy$electricity_cost, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$cooling_degree_days, predicted_probs,
xlab = "Cooling Degree Days",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Cooling Degree Days")
lines(lowess(clean_Renewable_Energy$cooling_degree_days, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$energy_assistance, predicted_probs,
xlab = "Energy Assistance",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Energy Assistance")
lines(lowess(clean_Renewable_Energy$energy_assistance, predicted_probs), col = "blue")
plot(clean_Renewable_Energy$owns_home, predicted_probs,
xlab = "Owns Home",
ylab = "Predicted Probability of Renewable Adoption",
main = "Probability of Adoption vs Owns Home")
lines(lowess(clean_Renewable_Energy$owns_home, predicted_probs), col = "blue")
The final logistic regression model predicted renewable energy adoption using eight predictor variables: household income, householder age, household size, home square footage, annual electricity cost, cooling degree days, energy assistance receipt, and home ownership status. The model equation is: log(odds of adoption) = -7.319 + 0.117(household_income) + 0.014(household_age) + 0.391(household_size) + 0.0002(square_footage) - 0.0014(electricity_cost) + 0.0004(cooling_degree_days) + 0.023(energy_assistance) + 1.630(owns_home) The model summary reveals that seven of the eight predictors are statistically significant at the p < 0.001 level, with energy assistance being the only non-significant predictor (p = 0.929). The models R squared is 0.1565056, indicating that about 15.65% of the variance in the adoption decisions is explained by the predictors. The likelihood ratio test (p_0.001) confirms that the full model significantly outpreforms the null model.
Model Assumption and Diagnostics
predicted_classes <- ifelse(predicted_probs > 0.5, 1, 0)
confusion <- table(
Predicted = factor(predicted_classes, levels = c(0, 1), labels = c("No", "Yes")),
Actual = factor(as.numeric(clean_Renewable_Energy$renewable_adoption),
levels = c(0, 1), labels = c("No", "Yes"))
)
print(confusion)## Actual
## Predicted No Yes
## No 17796 676
## Yes 5 19TP <- 19
TN <- 17796
FP <- 5
FN <- 676
accuracy <- (TP + TN) / (TP + TN + FP + FN)
sensitivity <- TP / (TP + FN)
specificity <- TN / (TN + FP)
precision <- TP / (TP + FP)
f1_score <- 2 * (precision * sensitivity) / (precision + sensitivity)
cat("Accuracy: ", accuracy, "\n")## Accuracy: 0.9631812cat("Sensitivity: ", sensitivity, "\n")## Sensitivity: 0.02733813cat("Specificity: ", specificity, "\n")## Specificity: 0.9997191cat("Precision: ", precision, "\n")## Precision: 0.7916667cat("F1 Score: ", f1_score, "\n")## F1 Score: 0.05285118The model has high accuracy (96.32% accuracy) and strong balance between detecting positives and avoiding false alarms. This model has an accuracy of 96.32% because the majority of observations were No, and the model severely fails at detecting different adopters (2.73% sensitivity). Furthermore, the near perfect specificity of 99.97% tells us that that this model almost never flags non-adopters incorrectly. When the model does predict adoption, it shows reasonable precision at 79.2% which is relatively good. Overall, this model shows us that renewable adoption is rare meaning that the models best guess is to default and predict “No” for close to everyone.
roc_obj <- roc(response = as.numeric(clean_Renewable_Energy$renewable_adoption), predictor = predicted_probs, levels = c(0, 1), direction = "<")
auc_val <- auc(roc_obj); auc_val## Area under the curve: 0.7807plot.roc(roc_obj, print.auc = TRUE, legacy.axes = TRUE,
xlab = "False Positive Rate (1 - Specificity)",
ylab = "True Positive Rate (Sensitivity)")
The AUC = 0.7807 means that the model is relatively good at
distinguishing between adopters and non-adopters of renewable energy. On
the plot, the curve is far above the diagonal “random guess” line, which
shows the model is much better than chance.
Conclusion
From the regression analysis, we see that home ownership emerges as the strongest predictor of renewable energy adoption (where the estimate coefficient = 1.630) meaning that homeowners are more likely to adopt renewable energy than renters. Instinctively this makes perfect sense as homeowners have both the authority to make structural modifications and the incentive to make long-term investments. We also see that household characteristics play a significant role where household income (estimate = 0.117) and larger household size (estimate = 0.391) are both associated with increased adoption likelihood. Lastly, environmental and economic factors show mixed results. For instance, more cooling degree days show higher adoption (estimate = 0.0004) while higher electricity costs are correlated with lower adoption rates (estimate = -0.0014). These findings suggest that programs that target low-income households are critical to encourage more clean energy use. Community solar programs, incentive structures, and targeted financial options could help expand access beyond homeowners. Furthermore, policy makers should recognize that high energy costs alone don’t drive adoption and other financial barriers should be addressed through subsidies or tax credits. The model fit shows both strengths and severe limitations. While the model achieves a 97% accuracy and an AUC of 0.7807, indicating a generically good discriminatory ability, it suffers from major class imbalances. With only 695 out of 18496 households adopting renewable energy, the model correctly identifies only 19 out of 695 adopters (2.73% sensitivity) showing that it struggles to predict actual adoption of renewable energy sources. Furthermore, a R^2 value of 0.157 indicates that about 16% of variance in adoption is explained by these predictors suggesting that there are other factors that were not included in this study. Future research can address some of the limitations that my study included. Firstly, it can incorporate policy variables such as incentives which would improve explanatory power. Furthermore, collecting data on environmental attitudes, prior knowledge of renewable energy, and perceived barriers would allow for a deeper understanding of why people chose renewable energy sources.
References
Data set used: https://www.eia.gov/consumption/residential/data/2020/index.php?view=microdata Help with cleaning: https://help.displayr.com/hc/en-us/articles/360004611335-How-to-Work-with-Conditional-R-Formulas Chart colors: https://r-charts.com/colors/