Individual assignment “Tom Hotel’’
Introduction
It is important for the firms to conduct research and analyze the data to make proper decisions to achieve higher profitability. Concerning, this report focuses on the analysis of the data containing information for Tom Hotel. Tom is one of the leading hotel chains offering accommodations A and B. This hotel chain operates its business across several regions worldwide. A hotel is operated by 1 manager and many employees. Through data analysis, the directors of Tom chain is intended to obtain a general profile of the hotels in the Scandinavian and examine the ways of increasing revenues by focusing on some factors like occupancy, advertising expenditure, size of the hotel, etc.
Research Methodology
This research is based on the analysis of data obtained from a random sample of ‘Tom hotel’ from Finland, Sweden and Norway. In order to carry out this assessment, a questionnaire was distributed to 104 randomly selected managers of ‘Tom hotels’. The manager of each hotel filled the given questionnaire. This information includes number of Tom hotel, hotel location, hotel size, distance of the hotel from the nearest rail link, average spend on advertisement (Per month), number of employees, gender of the manager, years of service, estimated average monthly income of accommodation type A and estimated average monthly income of accommodation type B.
Task 1:
- a) Descriptive statistics:
The below table of descriptive statistic defines the categorical variables:
| Descriptive Statistics | Region | Size | Gender | Year |
| Mean | 1.884615 | 1.798077 | 1.423077 | 1.509615 |
| Standard Error | 0.080034 | 0.080118 | 0.04868 | 0.0611 |
| Median | 2 | 2 | 1 | 1 |
| Mode | 1 | 1 | 1 | 1 |
| Standard Deviation | 0.816192 | 0.817049 | 0.49644 | 0.623102 |
| Sample Variance | 0.666169 | 0.667569 | 0.246453 | 0.388256 |
| Kurtosis | -1.46617 | -1.39491 | -1.93781 | -0.3105 |
| Skewness | 0.216859 | 0.39036 | 0.315975 | 0.82231 |
| Range | 2 | 2 | 1 | 2 |
| Minimum | 1 | 1 | 1 | 1 |
| Maximum | 3 | 3 | 2 | 3 |
| Sum | 196 | 187 | 148 | 157 |
| Count | 104 | 104 | 104 | 104 |
From the given data sample, it can be determined that categorical variables are region of the hotel, size of hotel, gender of the manager and years of service. These variables take on values that are names and labels (Esan et al., 2016). In relation to descriptive statistics, these variables are described as follows:
Region of the hotel- In this descriptive statistics, it is analyzed that the number of hotels are 104 in different regions like Finland, Sweden and Norway. But at the same time, most of the hotels are located at Finland as comparison of Sweden or Norway. It is because the mode of region represents that the value of Finland (1) is most often in the given table of descriptive statistics.
Size of the hotel- Tom hotel is providing mostly small size hotels (47) in the different regions where less than 50 rooms are available for the customers. In this, the normal distribution of the frequency represents that the size of hotel is based on the positive skewed.
Gender of the manager- Furthermore, in concern to the gender of the manager, it is also analyzed that most of the managers are male candidates who manage the hotels in different states of the country. In this, the normal distribution represents that gender of the manager in hotel is based on the positive skewed because it has long right tail (Siegel, 2016).
Years of service- On the basis of the study of this descriptive statistics, it is also analyzed that as a hotel manager 0 to 3 years of service is providing by the group representatives. This statement is defined after the evaluation of the mode of years of service. In this, the normal distribution of frequency represents that the year of service is positive skewed.
b) Chart to graphically display the distribution
In order to present the distribution of the data, Skewness is a significant measure. It explains asymmetry from the normal distribution in a set of data. It can be positive and negative depending on whether the data points are skewed to the left and negative or the right and positive of the data mean. The data set having outliers shows skewness or deviation from the normal bell curve (Berenson et al., 2012). When data is skewed to the right, the mean and median are greater than mode of the data set. When the data is skewed to the left, the mean and median are less than the mode of the data set. The right tail is longer means the mass of the distribution is focused on the left of the figure (Buglear, 2012). It is known as the right-skewed or right tailed. If the left tail is longer means the mass of the distribution is concentrated on the right of the figure. It is known as the left-skewed or left tailed.
Region of hotel:
The frequency distribution presents the right-skewed or low positive skewness.
Size of Hotel:
The frequency distribution presents the right-skewed or low positive skewness.
Gender:
The frequency distribution presents the right-skewed or low positive skewness.
Years of service:
The frequency distribution presents the right-skewed or high positive skewness.
From above charts, it can be stated that there is no normally distribution for any categorical variable.
Task 2:
a) Descriptive statistics
The below table of descriptive statistic defines the numerical variables:
| Descriptive Statistics | Distance | Advertising | Employees | Type A | Type B |
| Mean | 2.425481 | 1215.798 | 19.20192 | 395996.2 | 212168.3 |
| Standard Error | 0.09158 | 28.61087 | 0.600345 | 8824.143 | 717.7173 |
| Median | 2.5 | 1180 | 18 | 384500 | 211750 |
| Mode | 1.5 | 1460 | 15 | 414000 | 208500 |
| Standard Deviation | 0.933934 | 291.7748 | 6.122345 | 89988.96 | 7319.309 |
| Sample Variance | 0.872233 | 85132.51 | 37.4831 | 8.1E+09 | 53572284 |
| Kurtosis | -1.11598 | -1.14171 | -0.65896 | -0.78375 | -0.6749 |
| Skewness | 0.050608 | 0.052651 | 0.682132 | 0.287031 | 0.252459 |
| Range | 3.5 | 1147 | 24 | 390000 | 31200 |
| Minimum | 0.5 | 651 | 10 | 253000 | 198300 |
| Maximum | 4 | 1798 | 34 | 643000 | 229500 |
| Sum | 252.25 | 126443 | 1997 | 41183600 | 22065500 |
| Count | 104 | 104 | 104 | 104 | 104 |
From the above table, it can be stated that there are different numerical data including distance, advertising, number of employees, earnings through accommodation type A and B. values of numerical variables are numbers.
Number of employees: According to this descriptive statistics, it is analyzed that the average employees in Tom hotel are approximately 19 employees. At the same time, number of total employees in 104 hotels is 1997.
Average monthly income from accommodation type A and B: It can be estimated that the average monthly income from accommodation type A would be £395996.2 and the average monthly income from accommodation type B would be £212168.3.
Distance: The most of hotels are located 1.5 miles from the nearest rail link.
Advertising: The firm also spends £1215.8 on average per month on advertising. Total advertising expense for all hotels is £126443.
b) Chart to graphically display the distribution
For displaying distribution of the data in the given sample for the numerical variables, the below charts can be drawn:
Distance:
The frequency distribution presents the right-skewed or very low positive skewness.
Advertising:
The frequency distribution presents the right-skewed or very low positive skewness.
Number of employees:
The frequency distribution presents the right-skewed or high positive skewness.
Average monthly income from Type A:
The frequency distribution presents the right-skewed or low positive skewness.
Average monthly income from Type B:
The frequency distribution presents the right-skewed or low positive skewness.
On the basis of the above charts, it can be stated that there is no data sample for any variable that could present normal distribution. All these data sets have outliers that impact their mean considerably.
Task 3
a) Cross tabulation
| Count of Size | Year | |||
| Size | 1 | 2 | 3 | Grand Total |
| 1 | 28 | 16 | 3 | 47 |
| 2 | 18 | 13 | 31 | |
| 3 | 12 | 10 | 4 | 26 |
| Grand Total | 58 | 39 | 7 | 104 |
On the basis of the above cross tabulation, it can be analyzed that small hotels are more in number having hotel managers with low experience between 0 to 3 years as 28 hotels are small and have manager having low experience. There are only 3 small hotels that have only experienced hotel manager with greater than 7 years experience. In addition, it can also be determined that there are 18 medium size hotels that have managers with low experience from 0 to 3 years. At the same time, 12 large hotels have low experienced managers while 4 have high experienced managers. So, there is no significant association between sizes of hotel on the years of experience of hotel managers. Small, medium and large hotels have hotel managers having different years of experience (Anderson et al., 2011).
b) Chart for cross tabulation
The below chart shows the number of different hotels having different sizes and hotel managers with different experiences:
Task 4
The directors of ‘Tom Hotels’ chain of hotels believes that the Total Average Monthly incomes may have a relationship with the distance of the hotel from the nearest rail link. In order to determine this, regression analysis is conducted as below:
Hypothesis formulation:
µ0: There is no relationship between the Total Average Monthly incomes and the distance of the hotel from the nearest rail link.
µ1: There is relationship between the Total Average Monthly incomes and the distance of the hotel from the nearest rail link.
Regression analysis:
For testing hypotheses, a regression analysis is conducted and its results are presented as below:
| SUMMARY OUTPUT | ||||||
| Regression Statistics | ||||||
| Multiple R | 0.735342475 | |||||
| R Square | 0.540728556 | |||||
| Adjusted R Square | 0.536225894 | |||||
| Standard Error | 65377.92852 | |||||
| Observations | 104 | |||||
| ANOVA | ||||||
| df | SS | MS | F | Significance F | ||
| Regression | 1 | 5.13301E+11 | 5.13E+11 | 120.0908816 | 6.19653E-19 | |
| Residual | 102 | 4.35976E+11 | 4.27E+09 | |||
| Total | 103 | 9.49277E+11 | ||||
| Coefficients | Standard Error | t Stat | P-value | Lower 95% | Upper 95% | |
| Intercept | 791501.0322 | 17916.17737 | 44.17801 | 2.55082E-68 | 755964.3803 | 827037.7 |
| Distance | -75587.73974 | 6897.573587 | -10.9586 | 6.19653E-19 | -89269.04387 | -61906.4 |
Conclusion & Recommendations:
From the above statistics, it can be interpreted that p-value is greater than 0.05 significance value means there is no relationship between both variables. If the p-value is greater than 0.05, we reject the null hypothesis and conclude that a significant difference exists. It means there is no relationship between the Total Average Monthly incomes and the distance of the hotel from the nearest rail link (Siegel, 2016). From this analysis, it can be recommended to Tom Hotel’s management to promote its hotel services the rail links. It is because this analysis indicates that there is no significant relationship between the hotel revenues and the distance of the hotel from the nearest rail link. It means the company lacks of promoting its services to the travellers at railway links. It needs to adopt more advertising to market its services and make the passengers aware about its services. For this, the firm should spend more on average per month on advertising to get the desired results. In addition, the firm needs to provide training to its managers and employees to enhance their knowledge and skills to handle the customer needs effectively and attract them towards its services. The firm also needs to properly analyze the price and duration stay for the revenue management. The management can get full hotel occupancy by managing both the price and duration stay. For this, it needs to adopt dynamic pricing to change the price point of a room in respect to the demand for a certain future date. In relation to this, different factors like competitor pricing, group booking, special events, seasonality and macro market conditions should be considered to decide the pricing strategy and manage the revenues effectively. In addition, the firm should focus on discounts to persuade the buying behaviour of the customers and increase revenues.
References
Anderson, D.R., Sweeney, D.J. and Williams, T.A., 2011. Essentials of modern business statistics with Microsoft Excel. Cengage Learning.
Berenson, M., Levine, D., Szabat, K.A. and Krehbiel, T.C., 2012. Basic business statistics: Concepts and applications. Pearson higher education AU.
Buglear, J., 2012. Stats means business. Taylor & Francis.
Esan, O.T., Akanbi, C.T., Esan, O., Fajobi, O. and Ikenebomeh, P.I., 2016. Application of quantitative techniques in decision making by healthcare managers and administrators in Nigerian public tertiary health institutions. Health Services Management Research, 29(3), pp.50-61.
Siegel, A., 2016. Practical business statistics. Academic Press.
