UK Room Price Analysis: A Statistical Report on Room Types

This report provides a statistical analysis of room prices in the UK, utilizing tools such as regression, correlation, and ANOVA to explore the relationships between different room types and their corresponding prices. The analysis includes descriptive statistics to compute the average room price, which is found to be $105.73 per night, and ANOVA to determine significant price differences among room types like entire homes/apartments, hotel rooms, private rooms, and shared rooms. Regression analysis is used to assess the influence of room type on the minimum number of nights booked, revealing a limited impact. The report concludes by emphasizing the importance of statistical tools in decision-making processes, including planning, data collection, analysis, and research reporting. Desklib provides access to this solved assignment and other study materials to aid students in their academic endeavors.

Individual Report

INTRODUCTION
Correlation is the statistical tool that shows the relationship between
the two different variables. The relationship between such two
variables can be positive, negative and in further such relationship can
be higher, moderate and lower. It is considered to be the common tool
for describing the common relationship without making a statement
about cause and its effect. Regression is the statistical method used in
finance and investment made in various sectors, that will help in
determining the importance and character of the relationship between

Compute the average price of the cost of rooms per
night.
The average price which is charged for per room will be $ 105.73 for per
night per room. This average price is gathered and evaluated amongst the
various and multiple types of data that have been collected from the various
hotels in the UK city.
Descriptive Statistics
N Mean
price 1625 105.73
Valid N (listwise) 1625

Find the significant difference in the prices of different room types.
Descriptives
Price
N Mean Std. Deviation Std. Error 95% Confidence Interval for Mean Minimum Maximum
Lower Bound Upper Bound
Enitre home/apt 997 137.17 104.451 3.308 130.68 143.66 24 956
Hotel room 30 98.43 31.835 5.812 86.55 110.32 53 222
Private room 594 53.54 56.902 2.335 48.95 58.12 17 839
Shared room 3 62.67 32.192 18.586 -17.30 142.64 35 98
Total 1624 105.73 97.501 2.419 100.98 110.47 17 956
ANOVA
Price
Sum of Squares Df Mean Square F Sig.
Between Groups 2610840.818 3 870280.273 109.990 .000
Within Groups 12817973.970 1620 7912.330
Total 15428814.788 1623

Use regression for room types and nights.
It shows that there is very less influence of one variable on another of 4 %
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .066a .004 .004 30.80027
a. Predictors: (Constant), room type
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 6704.050 1 6704.050 7.067 .008b
Residual 1545361.808 1629 948.657
Total 1552065.858 1630
a. Dependent Variable: minimum nights
b. Predictors: (Constant), room type

CONCLUSION
The statistical tools are used to analyse the various decision making policies
of an entity which includes planning, designing, the collection of data,
analysing and interpretation of various forms of results, reporting of various
research findings and so on. In this report various statistical techniques have
been carried out using the data which has been collected from the primary
or secondary research that conducted on the sets of respondents. The
techniques used in this report are regression, correlation, ANOVA, one
sample t test, independent sample t test and so on.

REFERENCES
 SUGIHAR and et.al., 2021. STATISTIK MULTIVARIAT UNTUK
EKONOMI DAN BISNIS: Menggunakan Software SPSS. Airlangga
University Press.
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