Honeymoon Destinations

Honeymoon Destinations

Honeymoon Destinations Case

Introduction

The study is related to the honeymoon destinations which particularly focus on various factors that include favorite destinations of the people, characteristics of resorts interest them and resorts they want to see on a video.

Hypothesis

Ho 1: Bobzien and Rose believe that people with higher incomes would be more likely both to be interested in a video about potential honeymoon destinations and they are willing to pay more for honeymoon destinations videos.

Ho 3: What characteristics of resorts interest people most.

Ho 2: Which destinations interest people most in context of higher income group.

Results and Analyses

Ho 1: Bobzien and Rose believe that people with higher incomes would be more likely both to be interested in a video about potential honeymoon destinations and they are willing to pay more for honeymoon destinations videos.

Descriptive Statistics

Mean

Std. Deviation

N

VAR00054

6.0231E4

22651.50322

91

VAR00053

16.4945

11.11093

91

The result of the frequency table is showing the mean of the all the variables that are used in analyzing the data. The most important values of the above table that is means and the standard deviation of the variables are important to study as these vales are providing the accuracy of the data which ahs been gathered from the participants. From the above table, it is observed that the standard deviation of the income of the people is high as compared to the people willing to pay for the video.

Correlations

VAR00054

VAR00053

Pearson Correlation

VAR00054

1.000

.949

VAR00053

.949

1.000

Sig. (1-tailed)

VAR00054

.

.000

VAR00053

.000

.

N

VAR00054

91

91

VAR00053

91

91

The above result for the data shows that there is a correlation person willing to pay for the video and the income of the people. In addition to this, the value of the Pearson correlation coefficient shows that the value of Pearson correlation coefficient is significant that is it is less than 0.05 which shows that the value is significant. In this context, the correlation is a statistical relationship between two or more random variables (or variables that you can with some degree of accuracy consider acceptable as such). At the same time changing the values of one or more of these quantities are accompanied by systematic changes in the values of one or several other variables. The mathematical measure of the correlation of two random variables is the correlation ratio, or the correlation coefficient. In the event that a change in one of the random variable does not lead to a natural change in the other random variable, but it leads to a change in the other statistical characteristics of the random variable, then such a relationship is not considered a correlation, although a statistical. The objective of regression testing is to eliminate the ripple effect, ie verify that changes on a component of an information system do not introduce unwanted behavior or additional errors in other components unchanged.

Regression testing should be done every time you make a change in the system, both to correct an error to make an improvement. It is not sufficient to prove only the changed or added components, or functions that they perform, it is also necessary to check that the changes do not impact ...