Hotel Rate in Indian Market Final
1/31/2018
Project Title: Hotel Room Rates in the Indian Market
NAME:Kashish Yogesh Mukheja
EMAIL:kash.mkj18@gmail.com
COLLEGE:SRM INSTITUTE OF ENGINEERING AND TECHNOLOGY,KTR(B.TECH CSE)
1.INTRODUCTION:-
Tourism has now become a significant industry in India. It is a sunrise industry, an employment generator, a significant source of foreign exchange for the country. Tourism in India is the third largest foreign exchange earner in the country. The hotel industry in India thrives largely due to the growth in tourism and travel. Due to the increase in tourism with rising foreign and domestic tourists, hotel sector is bound to grow. There are different categories of hotels in India which include Heritage hotels, Luxury hotels, Budget hotels, and Resorts.There is an emergence of budget hotels in India to cater to the majority of the population who seek an affordable stay. Hotels provide the facilities for recreation and entertainment, meeting conferences and business transmission.Besides these, the tourism and hotel industry has been a great source of foreign exchange earnings and helps in the diversification of the economy.The suitably pricing hotel rooms and related services are significant issues.This is because a hotel’s price reflects an estimate of the value that tourists see and their compliance to pay for the hotel’s rooms and facility services.
2.OVERVIEW OF THE STUDY:-
The particular objective of this Study was to analyze the pricing strategy of hotels in the Indian hotel industry.Our aim was to compare the room rents of hotels for 8 days in 42 cities with the description provided in the dataset.Based on them we did some T-tests and correlation test to identify the significance of different variables and that which variable(s) acted as driving force to any changes in the same.We tried to explore the differences in price based on weekends(if the date lies on Saturday or Sunday), holidays(New Year Eve), Metro city, distance from the airport, the presence of swimming pools, Free wifi, free breakfast and so on.The tests helped us determine that which factor(s) related the price of hotel rooms positively and which affect negatively. Based on the tests, we also fitted in a regression model with respect to the variables which may be used to predict the room rent of the hotels or in short, the more significant variables.
3.DATA SOURCES AND DESCRIPTION:-
The data has been collected from https://in.hotels.com. The dataset contains the description of hotels in 42 cities across India.The dataset consists of records for 8 days.The description captures some internal and external factors which can be ued to predict the rates of hotel rooms.The internal factors include Hotel’s Name, StarRating,Address, Pincode, Description, its distance from nearest major Airport,HotelCapacity, presence of SwimmingPool, acces to FreeWifi and FreeBreakfast or not.The external factors are CityName,Population of the city, CityRank,IsMetroCity,IsTouristDestination and Date, IsWeekend and IsNewYearEve.Some of mentioned factors act like dummy or indicator variables like IsWeekend,IsNewYearEve, IsMetroCity, IsTouristDestination, HasSwimmingPool, FreeBreakfast.These indicator variables are used to indicate the presense or absense of the respective fields that may be expected to shift the outcome.
4.MODEL:-
Hypothesis H1: The average prices of hotel rooms depend on various internal and external factors. \[ \begin{aligned} RoomRent= \alpha_0 + \alpha_1 HasSwimmingPool + \alpha_2 IsNewYearEve + \alpha_3IsMetroCity + \alpha_4 IsTouristDestination + \alpha_5 StarRating +\\ \alpha_6Airport + \alpha_7HotelCapacity +\alpha_8FreeWifi+\alpha_9FreeBreakfast + \epsilon \end{aligned}\]
#Reading the data
setwd("~/Desktop/5 SRM Kashish Mukheja/Downoad content")
hot1<-read.csv(paste("Cities42.csv",sep=""))
# OLS Model
fit1<- lm(RoomRent ~HasSwimmingPool+IsNewYearEve+IsTouristDestination+IsMetroCity+StarRating+Airport+HotelCapacity+FreeWifi+FreeBreakfast, data = hot1)
summary(fit1)##
## Call:
## lm(formula = RoomRent ~ HasSwimmingPool + IsNewYearEve + IsTouristDestination +
## IsMetroCity + StarRating + Airport + HotelCapacity + FreeWifi +
## FreeBreakfast, data = hot1)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11696 -2375 -701 1063 309539
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8906.418 405.396 -21.970 < 2e-16 ***
## HasSwimmingPool 2227.069 159.327 13.978 < 2e-16 ***
## IsNewYearEve 844.123 174.046 4.850 1.25e-06 ***
## IsTouristDestination 2113.725 134.336 15.735 < 2e-16 ***
## IsMetroCity -1548.328 138.527 -11.177 < 2e-16 ***
## StarRating 3564.570 110.489 32.262 < 2e-16 ***
## Airport 11.265 2.710 4.157 3.24e-05 ***
## HotelCapacity -10.990 1.026 -10.714 < 2e-16 ***
## FreeWifi 485.597 224.134 2.167 0.0303 *
## FreeBreakfast 182.992 123.296 1.484 0.1378
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6607 on 13222 degrees of freedom
## Multiple R-squared: 0.1887, Adjusted R-squared: 0.1881
## F-statistic: 341.7 on 9 and 13222 DF, p-value: < 2.2e-16We ascertained the effect of several internal and external factors on the price of a hotel room with the simplest model. We regressed RoomRent on HasSwimmingPool,IsNewYearEve,IsMetroCity,IsTouristDestination,StarRating,Airport,HotelCapacity,FreeWifi,FreeBreakfast.We estimated model, using linear least squares The T-tests have also been implemented in appendix 3
5.CONCLUSIONS:-
Location of the hotel matters.Hotels in the Tourist Destinations, have higher room rents.
The facility of SwimmingPool and the starRating will positively affect the hotel prices which means the presence of pool increases the room rent as well as higher the star rating of
On the New Year’s Eve, prices go higher than the normal evenings.
We may also infer that the facilities like FreeWifi and FreeBreakfast do not intervene much in the hotel room’s rents.
6.REFERENCES:-
1.https://www.equitymaster.com/research-it/sector-info/hotels/Hotels-Sector-Analysis-Report.asp
2.https://in.hotels.com
3.https://www.slideshare.net/sushmasahupgdthm/importance-of-hotels-and-tourism-in-india
4.http://www.indianmirror.com/indian-industries/hotel.html
Appendix 1
Reading the csv file and visualizing the dimension of your dataset:-
setwd("~/Desktop/5 SRM Kashish Mukheja/Downoad content")
hot1<-read.csv(paste("Cities42.csv",sep=""))
View(hot1)
colnames(hot1)## [1] "CityName" "Population" "CityRank"
## [4] "IsMetroCity" "IsTouristDestination" "IsWeekend"
## [7] "IsNewYearEve" "Date" "HotelName"
## [10] "RoomRent" "StarRating" "Airport"
## [13] "HotelAddress" "HotelPincode" "HotelDescription"
## [16] "FreeWifi" "FreeBreakfast" "HotelCapacity"
## [19] "HasSwimmingPool"dim(hot1)## [1] 13232 19Descriptive statistics (min, max, median etc) of each variable:-
library(psych)
describe(hot1)## vars n mean sd median trimmed
## CityName* 1 13232 18.07 11.72 16 17.29
## Population 2 13232 4416836.87 4258386.00 3046163 4040816.22
## CityRank 3 13232 14.83 13.51 9 13.30
## IsMetroCity 4 13232 0.28 0.45 0 0.23
## IsTouristDestination 5 13232 0.70 0.46 1 0.75
## IsWeekend 6 13232 0.62 0.48 1 0.65
## IsNewYearEve 7 13232 0.12 0.33 0 0.03
## Date* 8 13232 14.30 2.69 14 14.39
## HotelName* 9 13232 841.84 488.14 834 842.05
## RoomRent 10 13232 5473.99 7333.12 4000 4383.33
## StarRating 11 13232 3.46 0.76 3 3.40
## Airport 12 13232 21.16 22.76 15 16.39
## HotelAddress* 13 13232 1202.69 581.98 1261 1233.43
## HotelPincode 14 13232 397430.26 259837.50 395003 388540.47
## HotelDescription* 15 13224 581.40 363.01 570 575.79
## FreeWifi 16 13232 0.93 0.26 1 1.00
## FreeBreakfast 17 13232 0.65 0.48 1 0.69
## HotelCapacity 18 13232 62.51 76.66 34 46.03
## HasSwimmingPool 19 13232 0.36 0.48 0 0.32
## mad min max range skew
## CityName* 11.86 1.0 42 41.0 0.48
## Population 3846498.95 8096.0 12442373 12434277.0 0.68
## CityRank 11.86 0.0 44 44.0 0.69
## IsMetroCity 0.00 0.0 1 1.0 0.96
## IsTouristDestination 0.00 0.0 1 1.0 -0.86
## IsWeekend 0.00 0.0 1 1.0 -0.51
## IsNewYearEve 0.00 0.0 1 1.0 2.28
## Date* 2.97 1.0 20 19.0 -0.77
## HotelName* 644.93 1.0 1670 1669.0 0.00
## RoomRent 2653.85 299.0 322500 322201.0 16.75
## StarRating 0.74 0.0 5 5.0 0.48
## Airport 11.12 0.2 124 123.8 2.73
## HotelAddress* 668.65 1.0 2108 2107.0 -0.37
## HotelPincode 257975.37 100025.0 7000157 6900132.0 9.99
## HotelDescription* 465.54 1.0 1226 1225.0 0.10
## FreeWifi 0.00 0.0 1 1.0 -3.25
## FreeBreakfast 0.00 0.0 1 1.0 -0.62
## HotelCapacity 28.17 0.0 600 600.0 2.95
## HasSwimmingPool 0.00 0.0 1 1.0 0.60
## kurtosis se
## CityName* -0.88 0.10
## Population -1.08 37019.65
## CityRank -0.76 0.12
## IsMetroCity -1.08 0.00
## IsTouristDestination -1.26 0.00
## IsWeekend -1.74 0.00
## IsNewYearEve 3.18 0.00
## Date* 1.92 0.02
## HotelName* -1.26 4.24
## RoomRent 582.06 63.75
## StarRating 0.25 0.01
## Airport 7.89 0.20
## HotelAddress* -0.88 5.06
## HotelPincode 249.76 2258.86
## HotelDescription* -1.25 3.16
## FreeWifi 8.57 0.00
## FreeBreakfast -1.61 0.00
## HotelCapacity 11.39 0.67
## HasSwimmingPool -1.64 0.00Appendix 2
one-way contingency tables for the categorical variables in your dataset.:-
mytable1<-with(hot1,table(IsMetroCity))
View(mytable1)
round(prop.table(mytable1)*100,2)## IsMetroCity
## 0 1
## 71.58 28.42mytable2<-with(hot1,table(IsTouristDestination))
View(mytable2)
round(prop.table(mytable2)*100,2)## IsTouristDestination
## 0 1
## 30.28 69.72mytable3<-with(hot1,table(IsWeekend))
View(mytable3)
round(prop.table(mytable3)*100,2)## IsWeekend
## 0 1
## 37.72 62.28mytable4<-with(hot1,table(IsNewYearEve))
View(mytable4)
round(prop.table(mytable4)*100,2)## IsNewYearEve
## 0 1
## 87.56 12.44mytable5<-with(hot1,table(StarRating))
View(mytable5)
round(prop.table(mytable5)*100,2)## StarRating
## 0 1 2 2.5 3 3.2 3.3 3.4 3.5 3.6 3.7 3.8
## 0.12 0.06 3.33 4.78 44.99 0.06 0.12 0.06 13.24 0.06 0.18 0.12
## 3.9 4 4.1 4.3 4.4 4.5 4.7 4.8 5
## 0.24 18.61 0.18 0.12 0.06 2.84 0.06 0.12 10.64mytable6<-with(hot1,table(FreeWifi))
View(mytable6)
round(prop.table(mytable6)*100,2)## FreeWifi
## 0 1
## 7.41 92.59mytable7<-with(hot1,table(FreeBreakfast))
View(mytable7)
round(prop.table(mytable7)*100,2)## FreeBreakfast
## 0 1
## 35.09 64.91mytable8<-with(hot1,table(HasSwimmingPool))
View(mytable8)
round(prop.table(mytable8)*100,2)## HasSwimmingPool
## 0 1
## 64.42 35.58mytable9<-with(hot1,table(CityName))
View(mytable9)
round(prop.table(mytable9)*100,2)## CityName
## Agra Ahmedabad Amritsar Bangalore
## 3.26 3.20 1.03 4.96
## Bhubaneswar Chandigarh Chennai Darjeeling
## 0.91 2.54 3.14 1.03
## Delhi Gangtok Goa Guwahati
## 15.48 0.97 4.72 0.36
## Haridwar Hyderabad Indore Jaipur
## 0.36 4.05 1.21 5.80
## Jaisalmer Jodhpur Kanpur Kochi
## 2.00 1.69 0.12 4.59
## Kolkata Lucknow Madurai Manali
## 3.87 0.97 0.85 2.18
## Mangalore Mumbai Munnar Mysore
## 0.79 5.38 2.48 1.21
## Nainital Ooty Panchkula Pune
## 1.09 1.03 0.48 4.53
## Puri Rajkot Rishikesh Shimla
## 0.42 0.97 0.67 2.12
## Srinagar Surat Thiruvanthipuram Thrissur
## 0.30 0.60 2.96 0.24
## Udaipur Varanasi
## 3.45 2.00Two-way contingency tables for the categorical variables in your dataset:-
library(gmodels)
CrossTable(hot1$IsMetroCity,hot1$IsTouristDestination)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 13232
##
##
## | hot1$IsTouristDestination
## hot1$IsMetroCity | 0 | 1 | Row Total |
## -----------------|-----------|-----------|-----------|
## 0 | 3352 | 6120 | 9472 |
## | 81.543 | 35.419 | |
## | 0.354 | 0.646 | 0.716 |
## | 0.837 | 0.663 | |
## | 0.253 | 0.463 | |
## -----------------|-----------|-----------|-----------|
## 1 | 655 | 3105 | 3760 |
## | 205.419 | 89.226 | |
## | 0.174 | 0.826 | 0.284 |
## | 0.163 | 0.337 | |
## | 0.050 | 0.235 | |
## -----------------|-----------|-----------|-----------|
## Column Total | 4007 | 9225 | 13232 |
## | 0.303 | 0.697 | |
## -----------------|-----------|-----------|-----------|
##
## CrossTable(hot1$IsWeekend,hot1$IsNewYearEve)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 13232
##
##
## | hot1$IsNewYearEve
## hot1$IsWeekend | 0 | 1 | Row Total |
## ---------------|-----------|-----------|-----------|
## 0 | 4989 | 2 | 4991 |
## | 87.637 | 616.864 | |
## | 1.000 | 0.000 | 0.377 |
## | 0.431 | 0.001 | |
## | 0.377 | 0.000 | |
## ---------------|-----------|-----------|-----------|
## 1 | 6597 | 1644 | 8241 |
## | 53.075 | 373.592 | |
## | 0.801 | 0.199 | 0.623 |
## | 0.569 | 0.999 | |
## | 0.499 | 0.124 | |
## ---------------|-----------|-----------|-----------|
## Column Total | 11586 | 1646 | 13232 |
## | 0.876 | 0.124 | |
## ---------------|-----------|-----------|-----------|
##
## CrossTable(hot1$FreeWifi,hot1$FreeBreakfast)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 13232
##
##
## | hot1$FreeBreakfast
## hot1$FreeWifi | 0 | 1 | Row Total |
## --------------|-----------|-----------|-----------|
## 0 | 606 | 375 | 981 |
## | 199.074 | 107.614 | |
## | 0.618 | 0.382 | 0.074 |
## | 0.131 | 0.044 | |
## | 0.046 | 0.028 | |
## --------------|-----------|-----------|-----------|
## 1 | 4037 | 8214 | 12251 |
## | 15.941 | 8.617 | |
## | 0.330 | 0.670 | 0.926 |
## | 0.869 | 0.956 | |
## | 0.305 | 0.621 | |
## --------------|-----------|-----------|-----------|
## Column Total | 4643 | 8589 | 13232 |
## | 0.351 | 0.649 | |
## --------------|-----------|-----------|-----------|
##
## CrossTable(hot1$FreeWifi,hot1$HasSwimmingPool)##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 13232
##
##
## | hot1$HasSwimmingPool
## hot1$FreeWifi | 0 | 1 | Row Total |
## --------------|-----------|-----------|-----------|
## 0 | 592 | 389 | 981 |
## | 2.526 | 4.574 | |
## | 0.603 | 0.397 | 0.074 |
## | 0.069 | 0.083 | |
## | 0.045 | 0.029 | |
## --------------|-----------|-----------|-----------|
## 1 | 7932 | 4319 | 12251 |
## | 0.202 | 0.366 | |
## | 0.647 | 0.353 | 0.926 |
## | 0.931 | 0.917 | |
## | 0.599 | 0.326 | |
## --------------|-----------|-----------|-----------|
## Column Total | 8524 | 4708 | 13232 |
## | 0.644 | 0.356 | |
## --------------|-----------|-----------|-----------|
##
## Boxplot of the variables:-
boxplot(hot1$RoomRent,
horizontal = TRUE,
xlab="Room Rent of the hotel",
main="Box plot of Room Rent Rating of hotel")
boxplot(hot1$StarRating,
xlab="Star Rating of the hotel",
main="Box plot of Star Rating of hotel",
horizontal = TRUE)
boxplot(hot1$Airport,
xlab="Distance between Hotel and closest major Airport(in km)",
main="Box plot of Airport Distance of hotel",
horizontal = TRUE)
boxplot(hot1$HotelCapacity,
xlab="Hotel Capacity",
main="Box plot of Hotel Capacity",
horizontal = TRUE)
##Histograms:-
library(lattice)
histogram(~StarRating,
data=hot1,
type="count",
nint=7,
xlab="Star Rating", main="Distrubtion of Star Ratings of hotels")
histogram(~HotelCapacity,
data=hot1,
type="count",
nint=12,
xlab="Hotel Capacity", main="Distrubtion of capacity of Hotels")
histogram(~Airport,
data=hot1,
type="count",
nint=12,
xlab="Distance from Airport", main="Distrubtion of distance to the nearest major airport")
par(mfrow= c(1,2))
plot(hot1$RoomRent ~ hot1$Airport, ylim= c(100 , 75000) , xlim=c(1,50) , main="RoomRent vs Airport")
plot(hot1$RoomRent ~ hot1$StarRating , main= " RoomRent vs Starrating" ,ylab= "Room Rent" , xlab="StarRating")
Corrgram
library(corrgram)
library(ellipse)##
## Attaching package: 'ellipse'## The following object is masked from 'package:graphics':
##
## pairscorrgram(hot1, order = FALSE, lower.panel = panel.shade, upper.panel = panel.pie, text.panel = panel.txt,main = "Corrgram of Hotel Data")
##ScatterPlot Matrix
pairs(formula = ~ RoomRent + IsWeekend + IsNewYearEve, data = hot1, pch = 16)
Cor Test
Cor test between Airport and RoomRent
cor.test(hot1$RoomRent,hot1$Airport)##
## Pearson's product-moment correlation
##
## data: hot1$RoomRent and hot1$Airport
## t = 5.7183, df = 13230, p-value = 1.099e-08
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03264192 0.06663581
## sample estimates:
## cor
## 0.04965324Cor test between StarRating and RoomRent
cor.test(hot1$RoomRent,hot1$StarRating)##
## Pearson's product-moment correlation
##
## data: hot1$RoomRent and hot1$StarRating
## t = 45.719, df = 13230, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3545660 0.3839956
## sample estimates:
## cor
## 0.3693734Cor test between HotelCapacity and RoomRent
cor.test(hot1$RoomRent,hot1$HotelCapacity)##
## Pearson's product-moment correlation
##
## data: hot1$RoomRent and hot1$HotelCapacity
## t = 18.389, df = 13230, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.1412142 0.1744430
## sample estimates:
## cor
## 0.1578733Appendix 2
T-test
T- test between RoomRent and HasSwimmingPool
2 Tail test
Ho:-There is no significant difference between the Room Rent of Hotels with swimmin pool and hotels without swimmin pool
H1:-There is a significant difference between the Room Rent of Hotels with swimmin pool and hotels without swimmin pool
t.test(hot1$RoomRent[hot1$HasSwimmingPool==0],hot1$RoomRent[hot1$HasSwimmingPool==1])##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$HasSwimmingPool == 0] and hot1$RoomRent[hot1$HasSwimmingPool == 1]
## t = -29.013, df = 5011.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -5096.030 -4450.942
## sample estimates:
## mean of x mean of y
## 3775.566 8549.052Inference:-Since p-vale<0.05, we accept H1,hence, there is a significant difference between the Room Rent of Hotels with swimming pool and hotels without swimming pool. ####1 Tail T-Test Ho:-There is no significant difference between the Room Rent of Hotels with swimming pool and hotels without swimmin pool
H1:-The Room Rent of Hotels with swimming pool is greater than the room rent of hotels without swimming pool
t.test(hot1$RoomRent[hot1$HasSwimmingPool==0],hot1$RoomRent[hot1$HasSwimmingPool==1],alternative = "less")##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$HasSwimmingPool == 0] and hot1$RoomRent[hot1$HasSwimmingPool == 1]
## t = -29.013, df = 5011.3, p-value < 2.2e-16
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -4502.814
## sample estimates:
## mean of x mean of y
## 3775.566 8549.052Inference:-Since p-vale<0.05, we accept H1,hence the Room Rent of Hotels with swimming pool is greater than the room rent of hotels without swimming pool.
T-test between RoomRent and FreeWifi
Ho:-There is no significant difference between the Room Rent of Hotels providing free wifi and those which do not
H1:-There is a significant difference between the Room Rent of Hotels providing free wifi and those which do not
t.test(hot1$RoomRent[hot1$FreeWifi==0],hot1$RoomRent[hot1$FreeWifi==1])##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$FreeWifi == 0] and hot1$RoomRent[hot1$FreeWifi == 1]
## t = -0.76847, df = 1804.7, p-value = 0.4423
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -360.5977 157.5701
## sample estimates:
## mean of x mean of y
## 5380.004 5481.518Inference:-Since p-vale>0.05, we accept Ho,hence there is no significant difference between the Room Rent of Hotels providing free wifi and those which do not.
T-test between RoomRent and FreeBreakfast
Ho: There is no significant difference between the Room Rent of Hotels with free breakfast and hotels without free breakfast.
H1: There is a significant difference between the Room Rent of Hotels with free breakfast and hotels without free breakfast
t.test(hot1$RoomRent[hot1$FreeBreakfast==0],hot1$RoomRent[hot1$FreeBreakfast==1])##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$FreeBreakfast == 0] and hot1$RoomRent[hot1$FreeBreakfast == 1]
## t = 0.98095, df = 6212.3, p-value = 0.3267
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -153.5017 460.9935
## sample estimates:
## mean of x mean of y
## 5573.790 5420.044Inference: Since p-value>0.05, we accept H0,hence, There is no significant difference between the Room Rent of Hotels with free breakfast and hotels without free breakfast.
T-test between RoomRent and IsNewYearEve
Ho: There is no significant difference between the Room Rent of Hotels on normal Eve and New Year’s Eve.
H1: The Room Rents of Hotels on normal Eve are cheaper than that on New Year’s Eve
t.test(hot1$RoomRent[hot1$IsNewYearEve==0],hot1$RoomRent[hot1$IsNewYearEve==1],alternative = "less")##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$IsNewYearEve == 0] and hot1$RoomRent[hot1$IsNewYearEve == 1]
## t = -4.1793, df = 2065, p-value = 1.523e-05
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -518.4763
## sample estimates:
## mean of x mean of y
## 5367.606 6222.826Inference: Since p-value<0.05, we accept H1,hence,the Room Rents of Hotels on normal Eve are cheaper than that on New Year’s Eve.
T-test between RoomRent and IsWeekend
Ho:-There is no significant difference between the Room Rent of Hotels on weekdays and weekends.
H1:-There is a significant difference between the Room Rent of Hotels on weekdays and weekends.
t.test(hot1$RoomRent[hot1$IsWeekend==0],hot1$RoomRent[hot1$IsWeekend==1])##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$IsWeekend == 0] and hot1$RoomRent[hot1$IsWeekend == 1]
## t = -0.51853, df = 9999.4, p-value = 0.6041
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -331.2427 192.6559
## sample estimates:
## mean of x mean of y
## 5430.835 5500.129Inference:-Since p-vale>0.05, we accept Ho,hence there is no significant difference between the Room Rent of Hotels on weekdays and weekends.
T-test between RoomRent and IsTouristDestination
Ho: There is no significant difference between the Room Rent of Hotels in Tourist destinations and non tourist destinations.
H1: The Room Rents of Hotels in Tourist destinations are greater than that in non tourist destinations
t.test(hot1$RoomRent[hot1$IsTouristDestination==0],hot1$RoomRent[hot1$IsTouristDestination==1],alternative = "less")##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$IsTouristDestination == 0] and hot1$RoomRent[hot1$IsTouristDestination == 1]
## t = -19.449, df = 12888, p-value < 2.2e-16
## alternative hypothesis: true difference in means is less than 0
## 95 percent confidence interval:
## -Inf -1789.665
## sample estimates:
## mean of x mean of y
## 4111.003 6066.024Inference: Since p-value<0.05, we accept H1,hence,the Room.Rents of Hotels in Tourist destinations are greater than that in non tourist destinations
T-test between RoomRent and IsMetroCity
Ho: There is no significant difference between the Room Rent of Hotels in non-metro cities and metro cities.
H1: The Room Rent of hotels in non-metro cities are more expensive than that in metro cities.
t.test(hot1$RoomRent[hot1$IsMetroCity==0],hot1$RoomRent[hot1$IsMetroCity==1],alternative = "greater")##
## Welch Two Sample t-test
##
## data: hot1$RoomRent[hot1$IsMetroCity == 0] and hot1$RoomRent[hot1$IsMetroCity == 1]
## t = 10.721, df = 13224, p-value < 2.2e-16
## alternative hypothesis: true difference in means is greater than 0
## 95 percent confidence interval:
## 919.9785 Inf
## sample estimates:
## mean of x mean of y
## 5782.794 4696.073Inference: Since p-value<0.05, we accept H1,hence,the Room Rents of Hotels in non-metro cities is more than that of metro cities.
Inferences:-From the above t-test,we may infer that the followinf variables are significant
1.HasSwimmingPool
2.IsNewYearEve
3.IsTouristDestination
4.IsMetroCity