Methodology and data analysis

This is because they are paying for any service they get at the hospital as well as their stay. If they are not comfortable with the services that they get from one place they give negative feedback. These feedbacks at customers who have visited Hilton have enabled us to gather it as our data and use it in the analysis of this section. The analysis can help the hotel management to know what is wrong and make corrections based on the recommendation that we give them. In most cases the staff is an important entity in contributing towards customer satisfaction. These feedbacks are from the customers who gave their reviews about their stay at the Hilton Hotel. The primary method of data collection was based on the data that was collected from our various respondents.

First the Questionnaires were prepared and administered to a 35 people. This was done in conjunction with the teacher who verified and approved our survey questions to be used. It involved fine tuning the questions on few details so that the questions were of the required standard. Descriptive Statistics Under descriptive statistics, summary of the findings was done. Descriptive statistics consists of n, which is the number of elements that were observed. Others include the mean which is the average, the median which is the central value of the data if it is sorted and arranged in ascending or descending order, the mode which is the common entry or the one that appears most in the data, the standard deviation which is the dispersion of the data, the variance which is found by standard deviation squared.

Others are minimum, maximum and range which is the difference between the maximum and minimum. Then the final part is the histogram. There were 11(36. 7%) male respondents and 19 (63. 3%) female respondents. This shows that in all the returned questionnaires, the respondents gave their gender correctly. The analysis of age composition is given below. 3% of the total population of the respondents. The general trend is that as we go up the age bracket, the number of the respondents decreases. At the end there are no people in the higher age bracket. This trend is represented in the histogram below. From the histogram, it is clear that higher frequencies are observed towards the left hand side of the Histogram. The Question here becomes: is there significance statistical correlation between gender and the rating of the respondents? H0: Hotel rating is not associated with the gender of the respondents.

H1: Hotel rating is associated with gender of the respondents. The table of output is presented below. The Chi-Square statistics was found to be 8. 567 as shown in the table. H0: U1 = U2 H1: U1 is not equal to U2 In this case we shall take the rating as independent variable and returning back as dependent variable. For this test to hold we shall assume that Dependent variable is continuous, the responses were independent, the dependent variable is approximately normal and lastly there is no outlier in dependent variable. The results were as below Group Statistics Consider coming back N Mean Std. Deviation Std. Error Mean rating of Hilton Yes 27 1. Its value which is 0. 972 will not be used to reject or not rejecting the null hypothesis.

The value will is used in Anova. The test statistics is -2. 641 and the degree of freedom is 28. The correlation is compared to the two extremes of correlation co-efficient which are +1 to -1. A positive correlation is where an increase in one variable leads to a corresponding increase in another variable. This also applies to a decrease. In the case where an increase in one variable leads to a decrease in the other variable of vice versa, that is called negative correlation (Sparks et al, 1987). We shall look at one aspect of assurance and how it relates to customer satisfaction. 01 level (2-tailed). From the table, the correlation co-efficient is 0. This is a positive correlation. It means that an increase or a decrease in Assurance (courtesy) leads to a corresponding increase or decrease in customer satisfaction (valuable stay).

The value of the correlation co-efficient is almost one. Since this value is almost a perfect correlation, most of the points are likely to lie close to the line of the best fit. This is because of the perfect correlation that is almost close to 1. iv) Multiple regression In multiple variations, we look at how one variable is affected by more than one independent variables (Preacher et al, 2006). In this case we shall see how tangibles and reliability affects customer satisfaction. It is multiple because we are various variables in this case reliability and tangibles. This means that we shall observe a predictable loss of 55. 3% if we apply it to another set of data. From the above table, we have the partial coefficients.

It refers to specific correlation between the variables. This means that how one variable that is the model can explain the dependent variable when the other independent variable is held constant. It is the unique contribution that each independent variable can make to the variance of the dependent variable (Preacher et al, 2006). For example in the above table if we square 0. 214 that comes from tangibles, we get 0. 046 which is 4. 6% the contribution that tangibles make to the variance uniquely. The results reveal that tangibles influence the customer satisfaction more than reliability at Hilton Hotel. The bigger part of the model is being controlled by the tangible more than reliability. This forms the basis of recommendation to the management on which aspect of SERVEQUAL to improve on between the two.