The influence of overall satisfaction, reviews, mistay etc. in driving the value of price in hospitality industry
Surabhi Srivastava
30 January 2018
1. Introduction
The hospitality industry is a broad category of fields within service industry that includes lodging, event planning, theme parks, transportation, cruise line, and additional fields within the tourism industry. The hospitality industry is a multibillion-dollar industry that depends on the availability of leisure time and disposable income. A hospitality unit such as a restaurant, hotel, or an amusement park consists of multiple groups such as facility maintenance and direct operations (servers, housekeepers, porters, kitchen workers, bartenders, management, marketing, and human resources etc.).
Usage rate, or its inverse “vacancy rate”, is an important variable for the hospitality industry. Just as a factory owner would wish a productive asset to be in use as much as possible (as opposed to having to pay fixed costs while the factory is not producing), so do restaurants, hotels, and theme parks seek to maximize the number of customers they “process” in all sectors. This led to formation of services with the aim to increase usage rate provided by hotel consolidators. Information about required or offered products are brokered on business networks used by vendors as well as purchasers.
2. Overview of the Study
Our field study concerns room prices which are made available through Airbnb Services in the region of Alpes Maritime.The Alpes-Maritimes department is surrounded by the departments of Var in the southwest, Alpes-de-Haute-Provence in the north-west, Italy, and the Mediterranean Sea to the south. It surrounds the Principality of Monaco on the west, north, and east.
3. An empirical field study of Airbnb Services
3.1 Overview
The specific objective of this Study was to investigate the pricing strategy employed by hosts in a different locale. This study analyzed room prices at Alpes Maritime,France. Our goal was to compare prices of rooms based on various factors like overall satisfaction of previous customers, their reviews, neighbourhood and other factors. Airbnb provides roomns to customers at affordable prices and desired neighbourhood( A subregion of the city or search area for which the survey is carried out).Our study is primarily focussed on services by Airbnb in Alpes Maritime region of France.
Accordingly, we construct the following hypothesis:
Hypothesis H1: The average prices of rooms are dependent on variables like overall satisfaction, review, no. of bedrooms, borough, minstay etc.
3.2 Data
For this study, we collected data from Tomslee website (http://tomslee.net/airbnb-data-collection-get-the-data). Airbnb provides roomns to customers at affordable prices and desired neighbourhood( A subregion of the city or search area for which the survey is carried out).Our study is primarily focussed on services by Airbnb in Alpes Maritime region of France. Our dataset comprises of following attributes:
room_id:
A unique number identifying an Airbnb listing. The listing has a URL on the Airbnb web site of http://airbnb.com/rooms/room_id
host_id:
A unique number identifying an Airbnb host. The host’s page has a URL on the Airbnb web site of http://airbnb.com/users/show/host_id
room_type:
One of “Entire home/apt”, “Private room”, or “Shared room”
borough:
A subregion of the city or search area for which the survey is carried out. The borough is taken from a shapefile of the city that is obtained independently of the Airbnb web site. For some cities, there is no borough information; for others the borough may be a number.
neighborhood:
As with borough: a subregion of the city or search area for which the survey is carried out. For cities that have both, a neighbourhood is smaller than a borough. For some cities there is no neighbourhood information.
reviews:
The number of reviews that a listing has received. Airbnb has said that 70% of visits end up with a review, so the number of reviews can be used to estimate the number of visits. Note that such an estimate will not be reliable for an individual listing (especially as reviews occasionally vanish from the site), but over a city as a whole it should be a useful metric of traffic.
overall_satisfaction:
The average rating (out of five) that the listing has received from those visitors who left a review.
accommodates:
The number of guests a listing can accommodate.
bedrooms:
The number of bedrooms a listing offers.
price:
The price (in $US) for a night stay. In early surveys, there may be some values that were recorded by month.
minstay:
The minimum stay for a visit, as posted by the host.
latitude and longitude:
The latitude and longitude of the listing as posted on the Airbnb site: this may be off by a few hundred metres.
3.3 Model
In order to test Hypothesis 1a, we proposed the following model:
\[Price= \alpha_0 + \alpha_1 overall_satisfaction + \alpha_2 reviews + \alpha_3 minstay +\alpha_4 bedrooms +\alpha_5 accomodates + \epsilon\]
# Read the data
setwd("C:/Users/SURABHI/Desktop/IIM INTERNSHIP")
hotel <- read.csv(paste("Airnb dataset.csv", sep=""), stringsAsFactors=FALSE)
attach(hotel)
# OLS Model
M1 <- lm(price~overall_satisfaction+reviews+minstay+bedrooms+accommodates, data=hotel)
summary(M1)##
## Call:
## lm(formula = price ~ overall_satisfaction + reviews + minstay +
## bedrooms + accommodates, data = hotel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -127.25 -36.14 -13.14 25.87 818.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -79.7084 45.4598 -1.753 0.08032 .
## overall_satisfaction 17.4767 9.6566 1.810 0.07109 .
## reviews -0.3589 0.2069 -1.735 0.08348 .
## minstay 5.5889 2.2877 2.443 0.01501 *
## bedrooms 49.7168 6.4359 7.725 9.46e-14 ***
## accommodates 9.5318 3.5190 2.709 0.00705 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.04 on 392 degrees of freedom
## (601 observations deleted due to missingness)
## Multiple R-squared: 0.4807, Adjusted R-squared: 0.4741
## F-statistic: 72.58 on 5 and 392 DF, p-value: < 2.2e-16We established the effect of various factors on the price of a room with the simplest model. We regressed Price on overall_satisfaction, reviews, minstay, bedrooms, accommodates .We estimated model, using linear regression model.
3.4 Results
We found empirical support for H1. The average room prices are independent of factors : overall_satisfaction with p-value 0.07109>0.05, reviews with p-value 0.08348>0.05. The room prices are dependent on factors: minstay with p-value 0.01501<0.05, bedroom with p-value 9.46e-14<0.05, accommodates with p-value 0.00705<0.05.
4. Conclusion
This paper was motivated by the need for research that could improve our understanding of how various factors as overall satisfaction, reviews, bedrooms, minstay, accommodates etc. influence the pricing strategies in the hospitality industry. The unique contribution of this paper is that we investigated the price premium charged by airbnb in Alpes Maritime. We observed that Airbnb charges price premiums for the number of bedrooms offered, number of guests accomodated by a host and the minimum stay of a visit by a guest.
5. References
http://tomslee.net/airbnb-data-collection-get-the-data https://en.wikipedia.org/wiki/Alpes-Maritimes https://www.luckeyhomes.com/en/cities/nice
Pictures
1- Read dataset in R and visualize length and breadth of dataset
setwd("C:/Users/SURABHI/Desktop/IIM INTERNSHIP")
hotel.df <- read.csv (paste("Airnb dataset.csv", sep=""))
View(hotel.df)
dim(hotel.df)## [1] 999 132- Create descriptive statistics of each variable
minimum of variable
min(hotel.df$reviews) ## [1] 0min(hotel.df$overall_satisfaction)## [1] NAmin(hotel.df$accommodates)## [1] 1min(hotel.df$bedrooms)## [1] NAmin(hotel.df$price)## [1] 23min(hotel.df$minstay)## [1] NAmaximum of variable
max(hotel.df$reviews)## [1] 199max(hotel.df$overall_satisfaction)## [1] NAmax(hotel.df$accommodates)## [1] 16max(hotel.df$bedrooms)## [1] NAmax(hotel.df$price)## [1] 9502max(hotel.df$minstay)## [1] NAmedian of variable
median(hotel.df$reviews)## [1] 2median(hotel.df$overall_satisfaction)## [1] NAmedian(hotel.df$accommodates)## [1] 4median(hotel.df$bedrooms)## [1] NAmedian(hotel.df$price)## [1] 103median(hotel.df$minstay)## [1] NAstandard deviation of variable
sd(hotel.df$reviews)## [1] 14.17979sd(hotel.df$overall_satisfaction)## [1] NAsd(hotel.df$accommodates)## [1] 2.120395sd(hotel.df$bedrooms)## [1] NAsd(hotel.df$price)## [1] 402.755sd(hotel.df$minstay)## [1] NA3- Create one way contingency table for categorical variables in dataset
mytable<- with(hotel.df, table(hotel.df$reviews))
mytable##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 315 171 89 69 49 38 25 25 20 17 23 10 10 14 12 4 8 8
## 18 19 20 21 23 24 25 26 27 28 29 30 31 32 34 36 38 39
## 6 7 8 1 4 2 1 4 2 3 2 3 2 3 2 1 4 3
## 40 41 42 43 45 48 50 51 52 54 57 60 63 67 74 76 78 80
## 1 2 2 4 1 1 2 2 1 2 1 2 2 1 1 1 1 1
## 82 88 95 97 140 199
## 1 1 1 1 1 1mytable<- with(hotel.df, table(hotel.df$bedrooms))
mytable##
## 0 1 2 3 4 5 6 7 8
## 158 454 227 91 36 20 6 3 1mytable<- with(hotel.df, table(hotel.df$accommodates))
mytable##
## 1 2 3 4 5 6 7 8 9 10 11 12 14 16
## 10 258 93 342 61 145 14 39 6 16 3 7 3 2mytable<- with(hotel.df, table(hotel.df$minstay))
mytable##
## 1 2 3 4 5 6 7 8 9 10 12 14 15
## 220 158 195 99 83 35 130 2 1 2 2 7 1mytable<- with(hotel.df, table(hotel.df$overall_satisfaction))
mytable##
## 2 3 3.5 4 4.5 5
## 1 1 14 49 205 1534- Create two way contingency table for categorical variables in the dataset
mytable1<- xtabs(~price+bedrooms, data=hotel.df)
mytable1## bedrooms
## price 0 1 2 3 4 5 6 7 8
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## 9502 0 0 0 0 1 0 0 0 0mytable1<- xtabs(~price+accommodates, data=hotel.df)
mytable1## accommodates
## price 1 2 3 4 5 6 7 8 9 10 11 12 14 16
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## 9502 0 0 0 0 0 0 0 1 0 0 0 0 0 0mytable1<- xtabs(~price+minstay, data=hotel.df)
mytable1## minstay
## price 1 2 3 4 5 6 7 8 9 10 12 14 15
## 23 1 2 0 0 0 0 0 0 0 0 0 0 0
## 29 3 0 1 0 0 0 0 0 0 0 0 0 0
## 30 1 0 0 0 0 0 0 0 0 0 0 0 0
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## 518 0 0 1 0 1 0 1 0 0 0 0 0 0
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## 577 0 0 0 1 0 0 0 0 0 0 0 0 0
## 599 0 0 0 1 0 0 0 0 0 0 0 0 0
## 610 0 0 0 0 0 0 1 0 0 0 0 0 0
## 611 0 0 0 1 0 0 0 0 0 0 0 0 0
## 633 0 0 1 0 0 0 0 0 0 0 0 0 0
## 645 0 0 0 0 0 0 1 0 0 0 0 0 0
## 656 0 0 0 0 0 0 1 0 0 0 0 0 0
## 683 0 0 0 0 0 0 1 0 0 0 0 0 0
## 691 1 0 1 0 1 0 1 0 0 0 0 0 0
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## 823 0 0 0 0 0 0 1 0 0 0 0 0 0
## 863 0 0 1 0 0 0 0 0 0 0 0 0 0
## 864 0 0 0 0 0 0 1 0 0 0 0 0 0
## 876 0 0 0 0 0 0 1 0 0 0 0 0 0
## 921 1 0 0 0 0 0 0 0 0 0 0 0 0
## 979 0 0 1 0 1 0 0 0 0 0 0 0 0
## 980 0 0 1 0 0 0 0 0 0 0 0 0 0
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## 3283 1 0 0 0 0 0 0 0 0 0 0 0 0
## 4607 1 0 0 0 0 0 0 0 0 0 0 0 0
## 9502 1 0 0 0 0 0 0 0 0 0 0 0 0mytable1<-xtabs(~price+overall_satisfaction, data=hotel.df)
mytable1## overall_satisfaction
## price 2 3 3.5 4 4.5 5
## 23 0 0 1 0 1 1
## 29 0 0 0 2 0 0
## 30 0 0 0 1 0 0
## 31 0 0 0 0 2 0
## 32 0 0 0 2 1 0
## 33 0 0 0 0 1 0
## 34 0 0 0 0 1 0
## 35 0 0 0 0 4 0
## 36 0 0 0 0 0 1
## 37 0 0 1 0 0 0
## 38 0 0 0 0 1 1
## 40 0 0 0 3 4 1
## 41 0 0 0 0 1 0
## 43 0 0 0 1 1 0
## 45 0 0 0 0 1 2
## 46 0 0 1 0 8 2
## 47 0 0 0 0 2 0
## 48 0 0 0 1 3 0
## 49 0 0 1 1 2 0
## 50 0 0 0 1 0 1
## 52 0 0 0 1 4 2
## 53 0 0 0 1 1 0
## 55 0 0 0 0 1 0
## 56 0 0 0 1 1 0
## 57 0 0 0 0 5 3
## 58 0 0 1 1 13 11
## 59 0 0 0 0 1 0
## 60 0 0 0 0 1 1
## 61 0 0 0 1 1 0
## 62 0 0 0 1 2 0
## 63 0 0 1 2 4 2
## 64 0 0 0 0 1 1
## 65 0 0 0 1 1 0
## 66 0 0 0 1 3 0
## 67 0 0 0 0 1 2
## 68 0 0 0 1 3 3
## 69 0 0 0 4 10 5
## 70 0 0 0 0 1 0
## 71 0 0 0 1 1 1
## 72 0 0 0 0 1 0
## 74 0 0 0 1 0 2
## 75 0 0 0 0 6 4
## 79 0 0 0 0 1 1
## 80 0 0 0 0 7 9
## 83 0 0 0 0 2 0
## 84 0 0 0 0 1 0
## 85 0 0 0 0 2 1
## 87 0 0 1 0 1 8
## 89 0 0 0 0 0 1
## 90 1 1 0 0 0 0
## 91 0 0 0 0 5 2
## 92 0 0 0 2 13 8
## 93 0 0 1 1 4 4
## 94 0 0 0 0 1 0
## 98 0 0 0 1 8 4
## 101 0 0 0 0 1 0
## 102 0 0 0 1 4 0
## 103 0 0 0 0 0 7
## 104 0 0 1 1 5 4
## 106 0 0 0 0 1 1
## 107 0 0 1 0 1 0
## 109 0 0 0 0 1 1
## 110 0 0 0 0 1 0
## 113 0 0 1 0 0 1
## 114 0 0 0 0 5 1
## 115 0 0 0 0 9 5
## 117 0 0 0 0 0 1
## 119 0 0 0 0 1 0
## 121 0 0 0 0 0 1
## 123 0 0 0 0 0 1
## 127 0 0 0 2 2 3
## 130 0 0 0 0 1 0
## 132 0 0 0 0 0 1
## 133 0 0 0 2 0 0
## 138 0 0 0 1 5 4
## 139 0 0 0 0 2 0
## 145 0 0 0 0 0 1
## 149 0 0 0 1 3 4
## 150 0 0 1 0 0 0
## 153 0 0 0 1 0 1
## 156 0 0 0 0 1 0
## 161 0 0 0 0 0 1
## 162 0 0 0 1 4 2
## 167 0 0 0 0 1 0
## 173 0 0 0 3 2 3
## 184 0 0 0 0 0 2
## 185 0 0 0 0 1 0
## 190 0 0 1 0 0 0
## 196 0 0 0 0 2 1
## 207 0 0 0 0 0 1
## 208 0 0 0 0 0 1
## 218 0 0 0 0 1 0
## 225 0 0 0 0 1 1
## 230 0 0 0 1 2 0
## 231 0 0 0 0 1 1
## 253 0 0 0 0 1 1
## 265 0 0 0 0 0 1
## 277 0 0 0 0 0 1
## 287 0 0 1 0 0 2
## 288 0 0 0 0 0 1
## 300 0 0 0 1 0 0
## 305 0 0 0 0 1 0
## 330 0 0 0 0 0 1
## 345 0 0 0 0 2 0
## 346 0 0 0 0 0 2
## 369 0 0 0 0 1 0
## 403 0 0 0 1 0 2
## 404 0 0 0 0 1 0
## 432 0 0 0 0 0 1
## 443 0 0 0 0 0 1
## 518 0 0 0 0 0 1
## 576 0 0 0 0 0 2
## 599 0 0 0 0 0 1
## 749 0 0 0 0 0 1
## 863 0 0 0 0 0 1
## 980 0 0 0 0 1 05- Draw a boxplot that belongs to your study
boxplot(hotel.df$price, xlab="price", ylab="", main="PRICE OF ROOMS", horizontal=TRUE)
boxplot(hotel.df$price~ hotel.df$bedrooms, xlab="price", ylab="No. of bedrooms", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
boxplot(hotel.df$price~ hotel.df$reviews, xlab="price", ylab="Reviews", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
boxplot(hotel.df$price~ hotel.df$overall_satisfaction, xlab="price", ylab="Overall Satisfaction", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
boxplot(hotel.df$price~ hotel.df$accommodates, xlab="price", ylab="Accomodations", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
6- Draw histogram for your suitable data fields
hist(hotel.df$price, main="PRICE FREQUENCY",xlab = "price of rooms",breaks = 25, xlim= c(0,3000), col="green")
7- Draw suitable plot for your data analysis.
plot(x= hotel.df$price, y=hotel.df$bedrooms, col="blue", main="bedrooms v/s price", xlab = "price", ylab = "number of bedrooms")
plot(x= hotel.df$price, y=hotel.df$overall_satisfaction, col="blue", main="overall satisfaction v/s price", xlab = "price", ylab = "Overall Satisfaction")
plot(x= hotel.df$price, y=hotel.df$minstay, col="blue", main="minstay v/s price", xlab = "price", ylab = "Minstay")
8- Create a correlation matrix
new_data<- hotel.df[,6:11]
r=cor(new_data)
r## reviews overall_satisfaction accommodates
## reviews 1.00000000 NA -0.1539512
## overall_satisfaction NA 1 NA
## accommodates -0.15395117 NA 1.0000000
## bedrooms NA NA NA
## price -0.09723858 NA 0.3146828
## minstay NA NA NA
## bedrooms price minstay
## reviews NA -0.09723858 NA
## overall_satisfaction NA NA NA
## accommodates NA 0.31468282 NA
## bedrooms 1 NA NA
## price NA 1.00000000 NA
## minstay NA NA 19- Visualize your correlation matrix using corrgram
library(corrgram)
corrgram(hotel.df, order = T, text.panel=panel.txt,
lower.panel = panel.shade,
upper.panel = panel.pie, main="Corrgram of all variables")
10- Create a scatterplot matrix for your dataset
library(car)
scatterplotMatrix(formula=~host_id+reviews+overall_satisfaction+price,cex=0.6,data = hotel.df,diagonal="histogram")
11- Run a suitable test to check your hypothesis for your suitable
Null Hypothesis - There is no correlation between the price and minstay
cor.test(hotel.df$price,hotel.df$minstay)##
## Pearson's product-moment correlation
##
## data: hotel.df$price and hotel.df$minstay
## t = 3.1192, df = 933, p-value = 0.001869
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03772307 0.16463048
## sample estimates:
## cor
## 0.1015901Since p<0.05 hence we reject the null hypothesis.
Null Hypothesis - There is no correlation between the price and overall satisfaction
cor.test(hotel.df$price,hotel.df$overall_satisfaction)##
## Pearson's product-moment correlation
##
## data: hotel.df$price and hotel.df$overall_satisfaction
## t = 3.0573, df = 421, p-value = 0.002375
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.05277439 0.23936118
## sample estimates:
## cor
## 0.1473787Since p>0.05 hence we accept the null hypothesis.
Null Hypothesis - There is no correlation between the price and reviews
cor.test(hotel.df$price,hotel.df$reviews)##
## Pearson's product-moment correlation
##
## data: hotel.df$price and hotel.df$reviews
## t = -3.085, df = 997, p-value = 0.002092
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.15830792 -0.03542814
## sample estimates:
## cor
## -0.09723858Since p>0.05 hence we accept the null hypothesis.
Null Hypothesis - There is no correlation between the price and bedrooms
cor.test(hotel.df$price,hotel.df$bedrooms)##
## Pearson's product-moment correlation
##
## data: hotel.df$price and hotel.df$bedrooms
## t = 12.649, df = 994, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3175707 0.4246391
## sample estimates:
## cor
## 0.3723431Since p<0.05 hence we reject the null hypothesis.
Null Hypothesis - There is no correlation between the price and accommodates
cor.test(hotel.df$price,hotel.df$accommodates)##
## Pearson's product-moment correlation
##
## data: hotel.df$price and hotel.df$accommodates
## t = 10.468, df = 997, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.2576882 0.3694952
## sample estimates:
## cor
## 0.3146828Since p<0.05 hence we reject the null hypothesis.
12- Run a t-test to analyse your hypothesis
t.test(hotel.df$price,hotel.df$minstay)##
## Welch Two Sample t-test
##
## data: hotel.df$price and hotel.df$minstay
## t = 14.133, df = 998.07, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 155.0829 205.0945
## sample estimates:
## mean of x mean of y
## 183.547548 3.458824t.test(hotel.df$price,hotel.df$overall_satisfaction)##
## Welch Two Sample t-test
##
## data: hotel.df$price and hotel.df$overall_satisfaction
## t = 14.045, df = 998, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 153.9618 203.9726
## sample estimates:
## mean of x mean of y
## 183.547548 4.580378t.test(hotel.df$price,hotel.df$reviews)##
## Welch Two Sample t-test
##
## data: hotel.df$price and hotel.df$reviews
## t = 13.89, df = 1000.5, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 152.0863 202.1279
## sample estimates:
## mean of x mean of y
## 183.54755 6.44044t.test(hotel.df$price,hotel.df$bedrooms)##
## Welch Two Sample t-test
##
## data: hotel.df$price and hotel.df$bedrooms
## t = 14.287, df = 998.02, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 157.0461 207.0570
## sample estimates:
## mean of x mean of y
## 183.547548 1.495984t.test(hotel.df$price,hotel.df$accommodates)##
## Welch Two Sample t-test
##
## data: hotel.df$price and hotel.df$accommodates
## t = 14.077, df = 998.06, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 154.3747 204.3861
## sample estimates:
## mean of x mean of y
## 183.547548 4.16716713-## Regression Model
In order to test Hypothesis 1a, we proposed the following model:
\[Price= \alpha_0 + \alpha_1 overall_satisfaction + \alpha_2 reviews + \alpha_3 minstay +\alpha_4 bedrooms +\alpha_5 accomodates + \epsilon\]
# Read the data
setwd("C:/Users/SURABHI/Desktop/IIM INTERNSHIP")
hotel <- read.csv(paste("Airnb dataset.csv", sep=""), stringsAsFactors=FALSE)
attach(hotel)
# OLS Model
M1 <- lm(price~overall_satisfaction+reviews+minstay+bedrooms+accommodates, data=hotel)
summary(M1)##
## Call:
## lm(formula = price ~ overall_satisfaction + reviews + minstay +
## bedrooms + accommodates, data = hotel)
##
## Residuals:
## Min 1Q Median 3Q Max
## -127.25 -36.14 -13.14 25.87 818.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -79.7084 45.4598 -1.753 0.08032 .
## overall_satisfaction 17.4767 9.6566 1.810 0.07109 .
## reviews -0.3589 0.2069 -1.735 0.08348 .
## minstay 5.5889 2.2877 2.443 0.01501 *
## bedrooms 49.7168 6.4359 7.725 9.46e-14 ***
## accommodates 9.5318 3.5190 2.709 0.00705 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.04 on 392 degrees of freedom
## (601 observations deleted due to missingness)
## Multiple R-squared: 0.4807, Adjusted R-squared: 0.4741
## F-statistic: 72.58 on 5 and 392 DF, p-value: < 2.2e-16