AA7 – Customer Choice Model AssignmentNotes for Expert:1. Please read the Choice Models Case.pdf2. Watch the Video Lecture.mp4 where the professor walks through examples of this assignment, use...

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Please follow the instructions in the Case Task, simply answer 6 questions with screenshots. Preferred the expert to be adept in Marketing Analytics! American-English, please.


AA7 – Customer Choice Model Assignment Notes for Expert: 1. Please read the Choice Models Case.pdf 2. Watch the Video Lecture.mp4 where the professor walks through examples of this assignment, use the following link to access video: https://1drv.ms/v/s!AloMtfooflkegqZeq5vevL8qCVh4fQ?e=HEmZV1 3. Utilize the Enginius Predictive Modeling.xlsx to answer the 6 questions below 4. American English spelling – simply answer the 6 questions below with screenshots as instructed in the professor’s tasks below Task: · Read the case background, analyze the accompanying data sets and make necessary calculations, then respond to the following writing tasks. · Use tables and annotated screenshots copy/pasted in your answers below to display the Enginius/Excel information that is useful for the written responses. Address the following in your write up: 1. (20 pts) List out each statistically significant (i.e. p <.05) predictor of a travelers likelihood to book on airbnb. rank them by their impact on the likelihood to book (elasticities in the “1” column, not “0”). use your intuition to provide a brief explanation of what may cause there to be a significant statistical relationship for each of the variables. use the “aa7 airbnb traveler data.xlsx” to find the relevant information on enginius. 2. (20 pts) follow the same process for the prediction of hosts to have continued to remain as a host listing their property on airbnb until the city banned it in 2015. use “aa7 airbnb hosts data.xlsx”. 3. (10 pts) report on the accuracy of each model (travelers baseline & host baseline) in terms of the model’s ability to accurately predict the outcome (book a trip, remain on the service) and the alternative (not book, leave). see the “confusion matrix” and how many travelers/hosts the model accurately predicted to book/not-book or host/not-host. what are the models good/bad at predicting? 4. (20 pts) what would be your plan of attack to attract travelers to eugene to book on airbnb? who would you target? what email(s) might you send? etc. provide details. 5. (20 pts) what would be your plan of attack to entice owners to list their homes on airbnb for travelers to rent? who would you target? what characteristics would you look for? what offers might you send them? etc. provide details. 6. (10 pts) state any concerns you have with making decisions based on the analysis approach, modeling choices, or the inclusion of any problematic variables in the modeling effort. be specific as to why you think this might be a problem. marketing strategy: based on first principles and data analytics 1 marketing strategy: based on first principles and data analytics aa7: airbnb choice model marketing strategy: based on first principles and data analytics 2 acquiring new customers in the hospitality industry1 conor henderson, shrihari sridhar and alejandro lerza durant background airbnb is an internet firm that helps travelers discover and book unique accommodations offered by hosts with a room or house available for short term rental around the world. airbnb was founded in the summer of 2008 in san francisco, ca, and quickly became a disrupter to the traditional hotel and lodging industry. the company bypassed the brick and mortar business model by creating a brokerage service between prospective guests and decentralized lodging locations. airbnb’s service can be accessed via company websites or mobile applications for android, ios, and apple watch. system users may search for accommodations by filtering data such as date, location, lodging type, and price. prior to booking lodging, users must authenticate the process by providing their name, valid email address, photo id, telephone number, and payment information. airbnb earns revenue by charging service fees to both guests and hosts. the cost of airbnb lodging and accommodations is determined by the host. by not directly owning physical locations, airbnb has been able to quickly scale its business to accommodate demand. currently, the company offers more than 3 million listings in over 65,000 cities and nearly 200 countries. as of late 2016, airbnb was valued at around $30 billion, a similar amount to the marriott hotel group, even though it does not own any of its properties. some local governments have decided airbnb is illegal or highly subject to local restrictions. imagine a city, which had previously made listing homes on airbnb illegal due to concerns that it was raising home values and making ownership unaffordable, makes a decision to allow the firm to operate as long as it pays travel taxes. in order to grow the local marketplace, airbnb hires you to come up with a plan and execute it. problem statement 1 conor henderson is an assistant professor of marketing at the university of oregon, shrihari sridhar is center for executive development professor at texas a&m university, and alejandro lerza durant is an m.s. analytics student at texas a&m university. marketing strategy: based on first principles and data analytics 3 as documented in chapter 3, customers change over time due to industry fluctuations. failing to understand and address these dynamics can lead to poor business performance. since variation in customers’ preferences over time is an inherent condition facing all marketers, an effective marketing strategy must manage these dynamics (mp#2). airbnb’s problem would appear to fit the second fundamental marketing problem all firms face while formulating marketing strategy, i.e. multiple factors were working together in multifaceted ways to make all customer change in the market. so airbnb must constantly analyze which needs, desires, and preferences across their buyers are most important to attracting (onboarding) customers and retaining hosts. thus, airbnb needs answers to the following questions:  which product attributes are most desired by customers during acquisition and retention?  how can the market be effectively segmented to decide: o which customers to acquire? o which customers to expand?  how should airbnb work its targeting and positioning strategy to deal with customer dynamics? to help inform the decision, suppose airbnb embarked on an analytics exercise in eugene, or, to power a choice-model-based analysis. data traveler data as a local airbnb manager, you partner with alaska airlines to promote your firm. as a pilot test, airbnb will pay alaska airlines $9,000 for the ability to email a promotion to the next 3,000 passengers who book a flight from seattle to eugene. you design three emails: 1) promotion of $25 off an airbnb booking, 2) promotion of free transportation from the airport to the eugene destination booked on airbnb (taxi cost is $25, pre- arranged through oregon taxi), and 3) a simple welcome email from airbnb with a 30 second video that shows some of eugene’s highlights (no additional cost). the email promotions are randomly distributed to the first 3,000 passengers who booked a flight to eugene on alaska airlines and did not already have an airbnb account based on an email-matching algorithm. each promotion was mailed to 1,000 passengers. after the email campaign, alaska airlines shares the following information about the collected data:  email promotion type: 1=$25 off, 2=free taxi, 3=welcome. these are converted into “dummy variables” of 1/0 for $25 off and 1/0 for taxi, and the welcome email is used as the default reference category. marketing strategy: based on first principles and data analytics 4  customer email: the variable is used to do the following: o determine if the recipient uses any of the following email accounts:  @gmail.com;  @yahoo.com;  .edu;  or other. (these are also converted to dummy variables, with “other” as the default reference category.)  alaska account status: 1=not an alaska frequent flier member, 2=frequent flier member, 3=mvp frequent flier member.  address: 1=out of state, 2=oregon but not eugene or springfield, 3=eugene or springfield (converted to two dummy variables with “out of state” as the default reference category).  age.  outcome: indicates whether the person responded to the email by booking a stay on airbnb in eugene: 1=yes, 0=no.  number of tickets booked on the itinerary.  type of ticket: 1=round trip departing eugene in less than 14 days, 0=one way to eugene. host data separately, airbnb has data on hosts—those who list their property on the airbnb platform for travelers to rent. it is useful to know what predicts a host’s likelihood to remain on the platform over time. with a host’s retention rate and information on how much revenue they earn, you could prioritize which host to target based on their “lifetime value” (calculation can be estimated based on a customer lifetime value formula). you have the following data about 400 hosts:  whether the accommodation is still listed on airbnb: 1=yes, 0=no.  how long they were on airbnb in days.  listing type: 0=shared room or private room, 1=entire home.  number of guests the listing accommodates.  zillow estimate of property value in thousands of u.s. dollars.  average listing price on airbnb per night.  average rating by airbnb guests.  revenue generated in one year for airbnb via its property fee.  location: 1=campus to downtown to whitaker, 2=fairmont to amazon to south eugene hills, 3=friendly to west eugene, 4=north eugene, 5=springfield, 6=other outskirt locations. these are converted to dummy variables with “other outskirt locations” used as the default reference category. untitled spreadsheet frontpage enginius predictive modeling steve gallegos copyright (c) 2022, decisionpro inc. data description calibration data calibration data contains 3000 rows and 12 columns. it contains 11 predictors, to which we added an intercept. summary of predictors these data have been used for model calibration. email_25email_taxigmailyahooedualaskaffadd_oreadd_eugageticketsroundtrip average0.33300.33330.30930.22370.10531.74130.25100.379738.041.9900.8697 standard deviation0.47140.47150.46230.41680.30700.51880.43370.485410.341.1760.3367 median0.00000.00000.00000.00000.00002.00000.00000.000038.001.0001.0000 minimum0.00000.00000.00000.00000.00001.00000.00000.000018.001.0000.0000 maximum1.00001.00001.00001.00001.00003.00001.00001.000071.005.0001.0000 frequency table for observed choices alternative 0alternative 1 count2556444 frequency85.2%14.8% calibration data choiceemail_25email_taxigmailyahooedualaskaffadd_oreadd_eugageticketsroundtrip cu_id_11101001004611 cu_id_21101002002321 cu_id_30101002104741 cu_id_41101002014410 cu_id_51101001101821 cu_id_60101001013111 cu_id_70101002012411 cu_id_81101002004011 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cu_id_5721100012012311 cu_id_5731100011002611 cu_id_5740100011012611 cu_id_5750100012012220 cu_id_5761100011102311 cu_id_5771100012102111 cu_id_5780100011102211 cu_id_5791100012002521 cu_id_5800100012002511 cu_id_5811100012002421 cu_id_5820100011004411 cu_id_5830100012002411 cu_id_5840100012102421 cu_id_5850100012102811 cu_id_5860100012102611 cu_id_5871100012002311 cu_id_5881100012002710 cu_id_5890100012002511 cu_id_5900100012012511 cu_id_5911100012002611 predictor="" of="" a="" travelers="" likelihood="" to="" book="" on="" airbnb.="" rank="" them="" by="" their="" impact="" on="" the="" likelihood="" to="" book="" (elasticities="" in="" the="" “1”="" column,="" not="" “0”).="" use="" your="" intuition="" to="" provide="" a="" brief="" explanation="" of="" what="" may="" cause="" there="" to="" be="" a="" significant="" statistical="" relationship="" for="" each="" of="" the="" variables.="" use="" the="" “aa7="" airbnb="" traveler="" data.xlsx”="" to="" find="" the="" relevant="" information="" on="" enginius.="" 2.="" (20="" pts)="" follow="" the="" same="" process="" for="" the="" prediction="" of="" hosts="" to="" have="" continued="" to="" remain="" as="" a="" host="" listing="" their="" property="" on="" airbnb="" until="" the="" city="" banned="" it="" in="" 2015.="" use="" “aa7="" airbnb="" hosts="" data.xlsx”.="" 3.="" (10="" pts)="" report="" on="" the="" accuracy="" of="" each="" model="" (travelers="" baseline="" &="" host="" baseline)="" in="" terms="" of="" the="" model’s="" ability="" to="" accurately="" predict="" the="" outcome="" (book="" a="" trip,="" remain="" on="" the="" service)="" and="" the="" alternative="" (not="" book,="" leave).="" see="" the="" “confusion="" matrix”="" and="" how="" many="" travelers/hosts="" the="" model="" accurately="" predicted="" to="" book/not-book="" or="" host/not-host.="" what="" are="" the="" models="" good/bad="" at="" predicting?="" 4.="" (20="" pts)="" what="" would="" be="" your="" plan="" of="" attack="" to="" attract="" travelers="" to="" eugene="" to="" book="" on="" airbnb?="" who="" would="" you="" target?="" what="" email(s)="" might="" you="" send?="" etc.="" provide="" details.="" 5.="" (20="" pts)="" what="" would="" be="" your="" plan="" of="" attack="" to="" entice="" owners="" to="" list="" their="" homes="" on="" airbnb="" for="" travelers="" to="" rent?="" who="" would="" you="" target?="" what="" characteristics="" would="" you="" look="" for?="" what="" offers="" might="" you="" send="" them?="" etc.="" provide="" details.="" 6.="" (10="" pts)="" state="" any="" concerns="" you="" have="" with="" making="" decisions="" based="" on="" the="" analysis="" approach,="" modeling="" choices,="" or="" the="" inclusion="" of="" any="" problematic="" variables="" in="" the="" modeling="" effort.="" be="" specific="" as="" to="" why="" you="" think="" this="" might="" be="" a="" problem.="" marketing="" strategy:="" based="" on="" first="" principles="" and="" data="" analytics="" 1="" marketing="" strategy:="" based="" on="" first="" principles="" and="" data="" analytics="" aa7:="" airbnb="" choice="" model="" marketing="" strategy:="" based="" on="" first="" principles="" and="" data="" analytics="" 2="" acquiring="" new="" customers="" in="" the="" hospitality="" industry1="" conor="" henderson,="" shrihari="" sridhar="" and="" alejandro="" lerza="" durant="" background="" airbnb="" is="" an="" internet="" firm="" that="" helps="" travelers="" discover="" and="" book="" unique="" accommodations="" offered="" by="" hosts="" with="" a="" room="" or="" house="" available="" for="" short="" term="" rental="" around="" the="" world.="" airbnb="" was="" founded="" in="" the="" summer="" of="" 2008="" in="" san="" francisco,="" ca,="" and="" quickly="" became="" a="" disrupter="" to="" the="" traditional="" hotel="" and="" lodging="" industry.="" the="" company="" bypassed="" the="" brick="" and="" mortar="" business="" model="" by="" creating="" a="" brokerage="" service="" between="" prospective="" guests="" and="" decentralized="" lodging="" locations.="" airbnb’s="" service="" can="" be="" accessed="" via="" company="" websites="" or="" mobile="" applications="" for="" android,="" ios,="" and="" apple="" watch.="" system="" users="" may="" search="" for="" accommodations="" by="" filtering="" data="" such="" as="" date,="" location,="" lodging="" type,="" and="" price.="" prior="" to="" booking="" lodging,="" users="" must="" authenticate="" the="" process="" by="" providing="" their="" name,="" valid="" email="" address,="" photo="" id,="" telephone="" number,="" and="" payment="" information.="" airbnb="" earns="" revenue="" by="" charging="" service="" fees="" to="" both="" guests="" and="" hosts.="" the="" cost="" of="" airbnb="" lodging="" and="" accommodations="" is="" determined="" by="" the="" host.="" by="" not="" directly="" owning="" physical="" locations,="" airbnb="" has="" been="" able="" to="" quickly="" scale="" its="" business="" to="" accommodate="" demand.="" currently,="" the="" company="" offers="" more="" than="" 3="" million="" listings="" in="" over="" 65,000="" cities="" and="" nearly="" 200="" countries.="" as="" of="" late="" 2016,="" airbnb="" was="" valued="" at="" around="" $30="" billion,="" a="" similar="" amount="" to="" the="" marriott="" hotel="" group,="" even="" though="" it="" does="" not="" own="" any="" of="" its="" properties.="" some="" local="" governments="" have="" decided="" airbnb="" is="" illegal="" or="" highly="" subject="" to="" local="" restrictions.="" imagine="" a="" city,="" which="" had="" previously="" made="" listing="" homes="" on="" airbnb="" illegal="" due="" to="" concerns="" that="" it="" was="" raising="" home="" values="" and="" making="" ownership="" unaffordable,="" makes="" a="" decision="" to="" allow="" the="" firm="" to="" operate="" as="" long="" as="" it="" pays="" travel="" taxes.="" in="" order="" to="" grow="" the="" local="" marketplace,="" airbnb="" hires="" you="" to="" come="" up="" with="" a="" plan="" and="" execute="" it.="" problem="" statement="" 1="" conor="" henderson="" is="" an="" assistant="" professor="" of="" marketing="" at="" the="" university="" of="" oregon,="" shrihari="" sridhar="" is="" center="" for="" executive="" development="" professor="" at="" texas="" a&m="" university,="" and="" alejandro="" lerza="" durant="" is="" an="" m.s.="" analytics="" student="" at="" texas="" a&m="" university.="" marketing="" strategy:="" based="" on="" first="" principles="" and="" data="" analytics="" 3="" as="" documented="" in="" chapter="" 3,="" customers="" change="" over="" time="" due="" to="" industry="" fluctuations.="" failing="" to="" understand="" and="" address="" these="" dynamics="" can="" lead="" to="" poor="" business="" performance.="" since="" variation="" in="" customers’="" preferences="" over="" time="" is="" an="" inherent="" condition="" facing="" all="" marketers,="" an="" effective="" marketing="" strategy="" must="" manage="" these="" dynamics="" (mp#2).="" airbnb’s="" problem="" would="" appear="" to="" fit="" the="" second="" fundamental="" marketing="" problem="" all="" firms="" face="" while="" formulating="" marketing="" strategy,="" i.e.="" multiple="" factors="" were="" working="" together="" in="" multifaceted="" ways="" to="" make="" all="" customer="" change="" in="" the="" market.="" so="" airbnb="" must="" constantly="" analyze="" which="" needs,="" desires,="" and="" preferences="" across="" their="" buyers="" are="" most="" important="" to="" attracting="" (onboarding)="" customers="" and="" retaining="" hosts.="" thus,="" airbnb="" needs="" answers="" to="" the="" following="" questions:="" ="" which="" product="" attributes="" are="" most="" desired="" by="" customers="" during="" acquisition="" and="" retention?="" ="" how="" can="" the="" market="" be="" effectively="" segmented="" to="" decide:="" o="" which="" customers="" to="" acquire?="" o="" which="" customers="" to="" expand?="" ="" how="" should="" airbnb="" work="" its="" targeting="" and="" positioning="" strategy="" to="" deal="" with="" customer="" dynamics?="" to="" help="" inform="" the="" decision,="" suppose="" airbnb="" embarked="" on="" an="" analytics="" exercise="" in="" eugene,="" or,="" to="" power="" a="" choice-model-based="" analysis.="" data="" traveler="" data="" as="" a="" local="" airbnb="" manager,="" you="" partner="" with="" alaska="" airlines="" to="" promote="" your="" firm.="" as="" a="" pilot="" test,="" airbnb="" will="" pay="" alaska="" airlines="" $9,000="" for="" the="" ability="" to="" email="" a="" promotion="" to="" the="" next="" 3,000="" passengers="" who="" book="" a="" flight="" from="" seattle="" to="" eugene.="" you="" design="" three="" emails:="" 1)="" promotion="" of="" $25="" off="" an="" airbnb="" booking,="" 2)="" promotion="" of="" free="" transportation="" from="" the="" airport="" to="" the="" eugene="" destination="" booked="" on="" airbnb="" (taxi="" cost="" is="" $25,="" pre-="" arranged="" through="" oregon="" taxi),="" and="" 3)="" a="" simple="" welcome="" email="" from="" airbnb="" with="" a="" 30="" second="" video="" that="" shows="" some="" of="" eugene’s="" highlights="" (no="" additional="" cost).="" the="" email="" promotions="" are="" randomly="" distributed="" to="" the="" first="" 3,000="" passengers="" who="" booked="" a="" flight="" to="" eugene="" on="" alaska="" airlines="" and="" did="" not="" already="" have="" an="" airbnb="" account="" based="" on="" an="" email-matching="" algorithm.="" each="" promotion="" was="" mailed="" to="" 1,000="" passengers.="" after="" the="" email="" campaign,="" alaska="" airlines="" shares="" the="" following="" information="" about="" the="" collected="" data:="" ="" email="" promotion="" type:="" 1="$25" off,="" 2="free" taxi,="" 3="welcome." these="" are="" converted="" into="" “dummy="" variables”="" of="" 1/0="" for="" $25="" off="" and="" 1/0="" for="" taxi,="" and="" the="" welcome="" email="" is="" used="" as="" the="" default="" reference="" category.="" marketing="" strategy:="" based="" on="" first="" principles="" and="" data="" analytics="" 4="" ="" customer="" email:="" the="" variable="" is="" used="" to="" do="" the="" following:="" o="" determine="" if="" the="" recipient="" uses="" any="" of="" the="" following="" email="" accounts:="" ="" @gmail.com;="" ="" @yahoo.com;="" ="" .edu;="" ="" or="" other.="" (these="" are="" also="" converted="" to="" dummy="" variables,="" with="" “other”="" as="" the="" default="" reference="" category.)="" ="" alaska="" account="" status:="" 1="not" an="" alaska="" frequent="" flier="" member,="" 2="frequent" flier="" member,="" 3="MVP" frequent="" flier="" member.="" ="" address:="" 1="out" of="" state,="" 2="Oregon" but="" not="" eugene="" or="" springfield,="" 3="Eugene" or="" springfield="" (converted="" to="" two="" dummy="" variables="" with="" “out="" of="" state”="" as="" the="" default="" reference="" category).="" ="" age.="" ="" outcome:="" indicates="" whether="" the="" person="" responded="" to="" the="" email="" by="" booking="" a="" stay="" on="" airbnb="" in="" eugene:="" 1="yes," 0="no." ="" number="" of="" tickets="" booked="" on="" the="" itinerary.="" ="" type="" of="" ticket:="" 1="round" trip="" departing="" eugene="" in="" less="" than="" 14="" days,="" 0="one" way="" to="" eugene.="" host="" data="" separately,="" airbnb="" has="" data="" on="" hosts—those="" who="" list="" their="" property="" on="" the="" airbnb="" platform="" for="" travelers="" to="" rent.="" it="" is="" useful="" to="" know="" what="" predicts="" a="" host’s="" likelihood="" to="" remain="" on="" the="" platform="" over="" time.="" with="" a="" host’s="" retention="" rate="" and="" information="" on="" how="" much="" revenue="" they="" earn,="" you="" could="" prioritize="" which="" host="" to="" target="" based="" on="" their="" “lifetime="" value”="" (calculation="" can="" be="" estimated="" based="" on="" a="" customer="" lifetime="" value="" formula).="" you="" have="" the="" following="" data="" about="" 400="" hosts:="" ="" whether="" the="" accommodation="" is="" still="" listed="" on="" airbnb:="" 1="yes," 0="no." ="" how="" long="" they="" were="" on="" airbnb="" in="" days.="" ="" listing="" type:="" 0="shared" room="" or="" private="" room,="" 1="entire" home.="" ="" number="" of="" guests="" the="" listing="" accommodates.="" ="" zillow="" estimate="" of="" property="" value="" in="" thousands="" of="" u.s.="" dollars.="" ="" average="" listing="" price="" on="" airbnb="" per="" night.="" ="" average="" rating="" by="" airbnb="" guests.="" ="" revenue="" generated="" in="" one="" year="" for="" airbnb="" via="" its="" property="" fee.="" ="" location:="" 1="campus" to="" downtown="" to="" whitaker,="" 2="Fairmont" to="" amazon="" to="" south="" eugene="" hills,="" 3="Friendly" to="" west="" eugene,="" 4="North" eugene,="" 5="Springfield," 6="other" outskirt="" locations.="" these="" are="" converted="" to="" dummy="" variables="" with="" “other="" outskirt="" locations”="" used="" as="" the="" default="" reference="" category.="" untitled="" spreadsheet="" frontpage="" enginius="" predictive="" modeling="" steve="" gallegos="" copyright="" (c)="" 2022,="" decisionpro="" inc.="" data="" description="" calibration="" data="" calibration="" data="" contains="" 3000="" rows="" and="" 12="" columns.="" it="" contains="" 11="" predictors,="" to="" which="" we="" added="" an="" intercept.="" summary="" of="" predictors="" these="" data="" have="" been="" used="" for="" model="" calibration.="" email_25="" email_taxi="" gmail="" yahoo="" edu="" alaskaff="" add_ore="" add_eug="" age="" tickets="" roundtrip="" average="" 0.3330="" 0.3333="" 0.3093="" 0.2237="" 0.1053="" 1.7413="" 0.2510="" 0.3797="" 38.04="" 1.990="" 0.8697="" standard="" deviation="" 0.4714="" 0.4715="" 0.4623="" 0.4168="" 0.3070="" 0.5188="" 0.4337="" 0.4854="" 10.34="" 1.176="" 0.3367="" median="" 0.0000="" 0.0000="" 0.0000="" 0.0000="" 0.0000="" 2.0000="" 0.0000="" 0.0000="" 38.00="" 1.000="" 1.0000="" minimum="" 0.0000="" 0.0000="" 0.0000="" 0.0000="" 0.0000="" 1.0000="" 0.0000="" 0.0000="" 18.00="" 1.000="" 0.0000="" maximum="" 1.0000="" 1.0000="" 1.0000="" 1.0000="" 1.0000="" 3.0000="" 1.0000="" 1.0000="" 71.00="" 5.000="" 1.0000="" frequency="" table="" for="" observed="" choices="" alternative="" 0="" alternative="" 1="" count="" 2556="" 444="" frequency="" 85.2%="" 14.8%="" calibration="" data="" choice="" email_25="" email_taxi="" gmail="" yahoo="" edu="" alaskaff="" add_ore="" add_eug="" age="" tickets="" roundtrip="" cu_id_1="" 1="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 0="" 46="" 1="" 1="" cu_id_2="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 23="" 2="" 1="" cu_id_3="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 47="" 4="" 1="" cu_id_4="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 44="" 1="" 0="" cu_id_5="" 1="" 1="" 0="" 1="" 0="" 0="" 1="" 1="" 0="" 18="" 2="" 1="" cu_id_6="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 31="" 1="" 1="" cu_id_7="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 24="" 1="" 1="" cu_id_8="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 40="" 1="" 1="" cu_id_9="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 38="" 4="" 1="" cu_id_10="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 35="" 1="" 1="" cu_id_11="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 23="" 1="" 1="" cu_id_12="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 0="" 41="" 1="" 1="" cu_id_13="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 55="" 5="" 1="" cu_id_14="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 20="" 2="" 0="" cu_id_15="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 0="" 22="" 2="" 1="" cu_id_16="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 24="" 1="" 1="" cu_id_17="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 36="" 5="" 1="" cu_id_18="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 26="" 1="" 1="" cu_id_19="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 34="" 1="" 1="" cu_id_20="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 35="" 1="" 1="" cu_id_21="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 35="" 1="" 1="" cu_id_22="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 47="" 1="" 1="" cu_id_23="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 36="" 1="" 1="" cu_id_24="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 40="" 1="" 1="" cu_id_25="" 0="" 1="" 0="" 1="" 0="" 0="" 3="" 1="" 0="" 40="" 4="" 1="" cu_id_26="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 38="" 1="" 1="" cu_id_27="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 52="" 1="" 0="" cu_id_28="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 34="" 1="" 1="" cu_id_29="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 34="" 2="" 1="" cu_id_30="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 49="" 1="" 1="" cu_id_31="" 1="" 1="" 0="" 1="" 0="" 0="" 3="" 0="" 0="" 18="" 1="" 1="" cu_id_32="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 35="" 1="" 1="" cu_id_33="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 0="" 46="" 1="" 1="" cu_id_34="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 23="" 1="" 1="" cu_id_35="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 0="" 42="" 3="" 1="" cu_id_36="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 24="" 2="" 1="" cu_id_37="" 0="" 1="" 0="" 1="" 0="" 0="" 3="" 0="" 1="" 37="" 1="" 1="" cu_id_38="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 42="" 1="" 1="" cu_id_39="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 48="" 4="" 1="" cu_id_40="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 43="" 1="" 1="" cu_id_41="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 23="" 1="" 1="" cu_id_42="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 39="" 1="" 1="" cu_id_43="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 22="" 1="" 0="" cu_id_44="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 23="" 2="" 1="" cu_id_45="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 39="" 1="" 1="" cu_id_46="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 28="" 1="" 1="" cu_id_47="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 32="" 1="" 1="" cu_id_48="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 48="" 1="" 1="" cu_id_49="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 1="" 0="" 46="" 1="" 1="" cu_id_50="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 34="" 1="" 1="" cu_id_51="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 48="" 1="" 1="" cu_id_52="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 42="" 1="" 1="" cu_id_53="" 0="" 1="" 0="" 1="" 0="" 0="" 3="" 1="" 0="" 39="" 1="" 1="" cu_id_54="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 0="" 65="" 1="" 1="" cu_id_55="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 38="" 3="" 1="" cu_id_56="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 34="" 1="" 0="" cu_id_57="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 28="" 1="" 1="" cu_id_58="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 45="" 4="" 1="" cu_id_59="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 43="" 1="" 1="" cu_id_60="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 26="" 3="" 1="" cu_id_61="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 35="" 1="" 0="" cu_id_62="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 46="" 1="" 1="" cu_id_63="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 1="" 0="" 39="" 1="" 1="" cu_id_64="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 23="" 1="" 1="" cu_id_65="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 30="" 1="" 1="" cu_id_66="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 37="" 1="" 0="" cu_id_67="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 44="" 1="" 1="" cu_id_68="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 53="" 5="" 1="" cu_id_69="" 0="" 1="" 0="" 1="" 0="" 0="" 3="" 0="" 1="" 45="" 1="" 1="" cu_id_70="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 42="" 1="" 0="" cu_id_71="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 24="" 1="" 1="" cu_id_72="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 1="" 0="" 48="" 1="" 1="" cu_id_73="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 26="" 1="" 1="" cu_id_74="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 34="" 1="" 1="" cu_id_75="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 39="" 4="" 1="" cu_id_76="" 1="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 23="" 1="" 1="" cu_id_77="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 30="" 3="" 1="" cu_id_78="" 0="" 1="" 0="" 1="" 0="" 0="" 1="" 0="" 1="" 45="" 1="" 0="" cu_id_79="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 1="" 0="" 35="" 1="" 0="" cu_id_80="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 42="" 1="" 1="" cu_id_81="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 37="" 1="" 1="" cu_id_82="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 41="" 4="" 1="" cu_id_83="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 0="" 37="" 4="" 1="" cu_id_84="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 26="" 1="" 0="" cu_id_85="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 0="" 1="" 20="" 2="" 0="" cu_id_86="" 0="" 1="" 0="" 1="" 0="" 0="" 2="" 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27="" 1="" 1="" cu_id_546="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 24="" 2="" 1="" cu_id_547="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 26="" 3="" 1="" cu_id_548="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 1="" 23="" 1="" 1="" cu_id_549="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 24="" 1="" 1="" cu_id_550="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 27="" 1="" 1="" cu_id_551="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 25="" 1="" 1="" cu_id_552="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 25="" 2="" 1="" cu_id_553="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 24="" 1="" 1="" cu_id_554="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 25="" 1="" 1="" cu_id_555="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 27="" 1="" 1="" cu_id_556="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 23="" 2="" 1="" cu_id_557="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 1="" 0="" 25="" 1="" 1="" cu_id_558="" 1="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 0="" 28="" 1="" 1="" cu_id_559="" 0="" 1="" 0="" 0="" 0="" 1="" 3="" 0="" 1="" 21="" 2="" 0="" cu_id_560="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 1="" 19="" 2="" 0="" cu_id_561="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 24="" 1="" 1="" cu_id_562="" 1="" 1="" 0="" 0="" 0="" 1="" 1="" 1="" 0="" 27="" 2="" 1="" cu_id_563="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 1="" 28="" 3="" 1="" cu_id_564="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 0="" 24="" 1="" 1="" cu_id_565="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 24="" 2="" 1="" cu_id_566="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 24="" 2="" 1="" cu_id_567="" 0="" 1="" 0="" 0="" 0="" 1="" 3="" 0="" 1="" 23="" 1="" 1="" cu_id_568="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 28="" 2="" 1="" cu_id_569="" 1="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 0="" 39="" 1="" 1="" cu_id_570="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 24="" 1="" 0="" cu_id_571="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 25="" 1="" 1="" cu_id_572="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 23="" 1="" 1="" cu_id_573="" 1="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 0="" 26="" 1="" 1="" cu_id_574="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 1="" 26="" 1="" 1="" cu_id_575="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 22="" 2="" 0="" cu_id_576="" 1="" 1="" 0="" 0="" 0="" 1="" 1="" 1="" 0="" 23="" 1="" 1="" cu_id_577="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 21="" 1="" 1="" cu_id_578="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 1="" 0="" 22="" 1="" 1="" cu_id_579="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 25="" 2="" 1="" cu_id_580="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 25="" 1="" 1="" cu_id_581="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 24="" 2="" 1="" cu_id_582="" 0="" 1="" 0="" 0="" 0="" 1="" 1="" 0="" 0="" 44="" 1="" 1="" cu_id_583="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 24="" 1="" 1="" cu_id_584="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 24="" 2="" 1="" cu_id_585="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 28="" 1="" 1="" cu_id_586="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 1="" 0="" 26="" 1="" 1="" cu_id_587="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 23="" 1="" 1="" cu_id_588="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 27="" 1="" 0="" cu_id_589="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 25="" 1="" 1="" cu_id_590="" 0="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 1="" 25="" 1="" 1="" cu_id_591="" 1="" 1="" 0="" 0="" 0="" 1="" 2="" 0="" 0="" 26="" 1="">
Answered 1 days AfterNov 18, 2022

Answer To: AA7 – Customer Choice Model AssignmentNotes for Expert:1. Please read the Choice Models...

Priye answered on Nov 20 2022
7 Votes
Untitled Spreadsheet
Frontpage
        Enginius
        Predictive Modeling
        Steve Gallegos, Portland State University
        Copyright (c) 2022, DecisionPro Inc.
Data description
        Calibration data
        Calibration data contains 400 rows and 13 columns. It contains 12 predictors, to which we added an intercept.
        Summary of predictors
        These data have been used for model calibration.
            Length    Type_Hm    Guests    ZestimateK    ListPrice    Rating    Rev_AbB    Loc_Campus    Loc_Fairmont    Loc_Friendly    Loc_N_Eugene    Loc_Springfield
        Average    357.83    0.7750    4.805    294.15    130.73    4.2133    490.25    0.2500    0.2475    0.1000    0.1850    0.1400
        Standard deviation    145.02    0.4181    1.599    53.72    47.78    0.7323    187.41    0.4336    0.4321    0.3004    0.3888    0.3474
        Median    352.00    1.0000    5.000    286.00    128.50    4.4000    500.50    0.0000    0.0000    0.0000    0.0000    0.0000
        Minimum    21.00    0.0000    1.000    196.00    23.00    2.4000    10.00    0.0000    0.0000    0.0000    0.0000    0.0000
        Maximum    825.00    1.0000    9.000    600.00    400.00    5.0000    1002.00    1.0000    1.0000    1.0000    1.0000    1.0000
        Frequency table for observed choices
            Alternative 0    Alternative 1
        Count    110    290
        Frequency    27.5%    72.5%
        Calibration data
            choice    Length    Type_Hm    Guests    ZestimateK    ListPrice    Rating    Rev_AbB    Loc_Campus    Loc_Fairmont    Loc_Friendly    Loc_N_Eugene    Loc_Springfield
        Host_1    0    261    1    7    304    111    3.9    450    1    0    0    0    0
        Host_2    1    494    1    5    210    137    4.8    671    1    0    0    0    0
        Host_3    1    341    0    2    276    73    4.5    202    1    0    0    0    0
        Host_4    0    189    0    4    314    79    3.9    403    1    0    0    0    0
        Host_5    0    178    1    6    308    130    4.5    525    1    0    0    0    0
        Host_6    1    542    1    6    253    140    4.3    620    1    0    0    0    0
        Host_7    0    293    1    6    243    109    2.7    492    1    0    0    0    0
        Host_8    1    298    1    2    275    122    4.9    758    1    0    0    0    0
        Host_9    0    148    1    4    305    196    2.9    611    1    0    0    0    0
        Host_10    1    281    0    3    254    74    3.8    394    1    0    0    0    0
        Host_11    0    309    1    6    269    168    3.1    348    1    0    0    0    0
        Host_12    1    359    1    3    281    86    4.4    447    1    0    0    0    0
        Host_13    1    500    1    4    277    119    4.9    629    1    0    0    0    0
        Host_14    1    557    0    1    229    98    5    547    1    0    0    0    0
        Host_15    1    458    1    5    253    147    4.2    696    1    0    0    0    0
        Host_16    1    563    1    4    275    137    4.8    438    1    0    0    0    0
        Host_17    0    144    0    3    277    59    4.2    450    1    0    0    0    0
        Host_18    0    294    1    7    450    125    3.1    630    1    0    
0    0    0
        Host_19    1    303    1    4    286    154    3.8    651    1    0    0    0    0
        Host_20    1    456    1    5    307    159    4.5    761    1    0    0    0    0
        Host_21    1    484    0    2    271    60    5    320    1    0    0    0    0
        Host_22    1    547    1    5    223    117    4.3    908    1    0    0    0    0
        Host_23    1    489    1    3    224    115    4.9    714    1    0    0    0    0
        Host_24    1    481    0    3    222    77    5    435    1    0    0    0    0
        Host_25    0    233    1    5    300    134    3.8    468    1    0    0    0    0
        Host_26    1    511    1    3    267    75    4.8    527    1    0    0    0    0
        Host_27    0    280    1    5    293    101    2.6    275    1    0    0    0    0
        Host_28    0    423    1    5    310    178    4.5    450    1    0    0    0    0
        Host_29    0    433    1    4    266    125    4.9    743    1    0    0    0    0
        Host_30    0    84    1    5    236    116    4.3    55    1    0    0    0    0
        Host_31    1    350    1    7    298    117    3.8    394    1    0    0    0    0
        Host_32    1    183    1    4    299    119    4.4    359    1    0    0    0    0
        Host_33    0    787    1    4    296    67    4.5    555    1    0    0    0    0
        Host_34    1    303    0    4    285    78    4.7    59    1    0    0    0    0
        Host_35    0    339    1    5    284    203    5    468    1    0    0    0    0
        Host_36    1    487    1    4    258    164    5    440    1    0    0    0    0
        Host_37    1    253    0    1    292    40    4    451    1    0    0    0    0
        Host_38    1    178    1    4    266    109    3.3    383    1    0    0    0    0
        Host_39    1    198    1    5    262    174    4.7    414    1    0    0    0    0
        Host_40    0    398    1    4    400    138    3.7    430    1    0    0    0    0
        Host_41    1    269    1    6    390    181    4.6    485    1    0    0    0    0
        Host_42    0    351    1    2    275    104    3.9    402    1    0    0    0    0
        Host_43    0    330    0    2    287    69    4.7    503    1    0    0    0    0
        Host_44    1    283    1    6    200    186    4.8    492    1    0    0    0    0
        Host_45    1    367    0    3    299    111    4.5    502    1    0    0    0    0
        Host_46    0    676    1    6    330    147    4.6    558    1    0    0    0    0
        Host_47    1    293    1    5    301    133    2.7    544    1    0    0    0    0
        Host_48    1    296    0    3    252    87    3.2    368    1    0    0    0    0
        Host_49    1    487    1    5    210    127    4.4    659    1    0    0    0    0
        Host_50    1    197    1    2    277    134    3.9    469    1    0    0    0    0
        Host_51    0    182    1    5    263    182    3.5    496    1    0    0    0    0
        Host_52    1    381    1    5    297    123    4.9    728    1    0    0    0    0
        Host_53    1    141    1    5    311    165    5    687    1    0    0    0    0
        Host_54    1    335    1    6    275    145    2.8    611    1    0    0    0    0
        Host_55    0    317    1    5    307    166    3    651    1    0    0    0    0
        Host_56    0    185    1    4    237    180    3.2    469    1    0    0    0    0
        Host_57    1    506    0    2    279    87    5    545    1    0    0    0    0
        Host_58    1    100    1    6    240    126    4    25    1    0    0    0    0
        Host_59    0    182    1    6    294    155    2.9    80    1    0    0    0    0
        Host_60    1    344    1    6    303    121    4.7    677    1    0    0    0    0
        Host_61    1    150    1    4    294    143    4.8    455    1    0    0    0    0
        Host_62    0    410    1    4    306    151    5    642    1    0    0    0    0
        Host_63    1    496    1    4    289    105    4.7    45    1    0    0    0    0
        Host_64    1    469    1    3    222    83    4.2    589    1    0    0    0    0
        Host_65    1    203    1    3    256    119    4.9    611    1    0    0    0    0
        Host_66    1    418    0    2    269    78    4.8    585    1    0    0    0    0
        Host_67    0    179    1    5    333    158    3    333    1    0    0    0    0
        Host_68    1    278    1    4    262    147    3.9    345    1    0    0    0    0
        Host_69    0    431    1    3    273    91    5    479    1    0    0    0    0
        Host_70    0    272    0    1    272    59    5    305    1    0    0    0    0
        Host_71    0    613    0    3    269    110    3.7    658    1    0    0    0    0
        Host_72    0    21    1    4    285    163    3.9    174    1    0    0    0    0
        Host_73    1    230    1    5    289    176    4.9    393    1    0    0    0    0
        Host_74    0    269    1    7    390    225    3.4    466    1    0    0    0    0
        Host_75    1    207    1    5    325    172    4.9    722    1    0    0    0    0
        Host_76    0    390    1    6    323    197    4.3    677    1    0    0    0    0
        Host_77    1    314    0    3    299    76    5    391    1    0    0    0    0
        Host_78    1    424    1    5    295    174    4.9    928    1    0    0    0    0
        Host_79    1    553    0    3    275    104    4.8    599    1    0    0    0    0
        Host_80    1    455    1    6    311    120    4.7    700    1    0    0    0    0
        Host_81    1    406    0    3    225    115    4.3    514    1    0    0    0    0
        Host_82    1    430    1    3    286    107    4.6    686    1    0    0    0    0
        Host_83    1    531    1    5    265    109    5    781    1    0    0    0    0
        Host_84    0    170    1    4    270    140    3.4    400    1    0    0    0    0
        Host_85    1    169    1    3    283    155    3.7    528    1    0    0    0    0
        Host_86    1    357    0    2    262    86    5    443    1    0    0    0    0
        Host_87    0    316    0    2    295    93    2.5    100    1    0    0    0    0
        Host_88    1    573    1    5    272    139    5    633    1    0    0    0    0
        Host_89    1    574    1    5    280    198    4.6    731    1    0    0    0    0
        Host_90    0    21    1    4    302    160    4.2    30    1    0    0    0    0
        Host_91    1    410    1    4    215    129    5    506    1    0    0    0    0
        Host_92    1    469    1    5    293    128    5    510    1    0    0    0    0
        Host_93    1    343    1    4    298    210    4.8    728    1    0    0    0    0
        Host_94    1    208    1    4    282    198    5    671    1    0    0    0    0
        Host_95    0    334    1    5    267    184    4.3    747    1    0    0    0    0
        Host_96    0    169    1    5    340    207    5    420    1    0    0    0    0
        Host_97    1    293    1    5    425    149    4.4    579    1    0    0    0    0
        Host_98    1    495    0    2    292    45    3.9    595    1    0    0    0    0
        Host_99    1    171    0    3    302    90    4.1    456    1    0    0    0    0
        Host_100    1    266    1    4    299    94    5    415    1    0    0    0    0
        Host_101    1    309    1    4    387    198    5    653    0    1    0    0    0
        Host_102    1    375    1    4    305    128    4.2    497    0    1    0    0    0
        Host_103    1    408    1    3    258    172    3.9    626    0    1    0    0    0
        Host_104    0    79    1    3    280    125    3.1    88    0    1    0    0    0
        Host_105    0    536    1    8    300    163    4    571    0    1    0    0    0
        Host_106    1    298    1    6    272    151    3.6    588    0    1    0    0    0
        Host_107    1    173    1    6    390    212    2.4    100    0    1    0    0    0
        Host_108    1    224    1    6    328    206    5    810    0    1    0    0    0
        Host_109    1    181    1    6    399    81    3.5    477    0    1    0    0    0
        Host_110    1    241    1    6    394    169    5    412    0    1    0    0    0
        Host_111    1    296    0    2    249    79    4.6    417    0    1    0    0    0
        Host_112    0    357    1    7    322    213    5    734    0    1    0    0    0
        Host_113    1    485    0    3    248    128    3.5    586    0    1    0    0    0
        Host_114    1    122    0    3    309    120    3.3    142    0    1    0    0    0
        Host_115    1    742    1    6    349    98    4.8    658    0    1    0    0    0
        Host_116    1    264    1    6    350    164    3.5    469    0    1    0    0    0
        Host_117    1    359    1    7    311    227    5    770    0    1    0    0    0
        Host_118    1    379    1    5    316    197    4.8    628    0    1    0    0    0
        Host_119    1    754    1    5    333    165    4.5    639    0    1    0    0    0
        Host_120    1    537    1    4    273    176    4.7    514    0    1    0    0    0
        Host_121    0    401    1    7    326    150    4.9    452    0    1    0    0    0
        Host_122    1    443    0    2    272    118    3.4    458    0    1    0    0    0
        Host_123    1    522    1    6    254    184    4.7    727    0    1    0    0    0
        Host_124    0    253    1    6    350    166    2.4    450    0    1    0    0    0
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        Normalized predictors
        Box-Cox parameters for the predictors
        The lambda values are the parameters used to transform the original data with the Box-Cox transformation. The shift values are the constant added to make sure all values are positive before applying the Box-Cox transformation.
            Length    Type_Hm    Guests    ZestimateK    ListPrice    Rating    Rev_AbB    Loc_Campus    Loc_Fairmont    Loc_Friendly    Loc_N_Eugene    Loc_Springfield
        lambda    0.800    2.000    1.100    -0.900    0.800    2.000    1.200    -2.000    -2.000    -2.000    -2.000    -2.000
        shift    0.00    1.00    0.00    0.00    0.00    0.00    0.00    1.00    1.00    1.00    1.00    1.00
        You asked to apply a Box-Cox transformation to normalize the predictors.
        Normalized predictors (calibration data)
        Predictor variables after applying the Box-Cox...
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