Bivariate Analysis
Bivariate analysis
Contingency table
In this case, we use contingency table to analyze the relationship between 2 qualitative variables. And this test works by comparing expected and observed frequencies with x2 distribution.
Correlation coefficient
When we need to test the relationship between 2 quantitative variables, we use correlation coefficient and it measured by standardized covariance measure and investigates linear dependence. Before doing this, it is better to first make a scatterplot to check the outliers and linearity then get the idea about the nature and strength relationship.
Next, we should calculate the r for correlation coefficient of variation, which measures the degree of linear dependence. r is always in between -1 and +1. If r < 0 or >0, then it is negative or positive linear dependence. if r equal to 0, then no linear dependence. if r equal to 1 or -1, then it is perfect linear dependence.
Rating- Price
This scatterplot does not show any clear relationship between the two variables. By using SPSS, we get the Pearson’s correlation coefficient to be r= 0.491, Pvalue=0.00. The Tobs=22.40, which is higher than the critical value of 2.32. In this case, we can reject H0 at 1% significance level.
Therefore, we can conclude that there is a strong positive relationship between rating and price, which means that the more rating hotel gets, the higher price will become.
Stars- Price
Again, the scatterplot does not show any clear relationship between two variables. And we get the Pearson’s correlation coefficient to be r= 0.592, Pvalue=0.00. The Tobs=29.57, which is higher than the critical value of 2.32.
This is the same as what we expected before, there is a strong positive relationship between stars and price.
Position- Price
The scatterplot does not show linear relationship between position and price.
And we get Pearson’s correlation coefficient to be r= -004, Pvalue=0.880. The Tobs= -0.161, which is higher than the critical
Contingency table
In this case, we use contingency table to analyze the relationship between 2 qualitative variables. And this test works by comparing expected and observed frequencies with x2 distribution.
Correlation coefficient
When we need to test the relationship between 2 quantitative variables, we use correlation coefficient and it measured by standardized covariance measure and investigates linear dependence. Before doing this, it is better to first make a scatterplot to check the outliers and linearity then get the idea about the nature and strength relationship.
Next, we should calculate the r for correlation coefficient of variation, which measures the degree of linear dependence. r is always in between -1 and +1. If r < 0 or >0, then it is negative or positive linear dependence. if r equal to 0, then no linear dependence. if r equal to 1 or -1, then it is perfect linear dependence.
Rating- Price
This scatterplot does not show any clear relationship between the two variables. By using SPSS, we get the Pearson’s correlation coefficient to be r= 0.491, Pvalue=0.00. The Tobs=22.40, which is higher than the critical value of 2.32. In this case, we can reject H0 at 1% significance level.
Therefore, we can conclude that there is a strong positive relationship between rating and price, which means that the more rating hotel gets, the higher price will become.
Stars- Price
Again, the scatterplot does not show any clear relationship between two variables. And we get the Pearson’s correlation coefficient to be r= 0.592, Pvalue=0.00. The Tobs=29.57, which is higher than the critical value of 2.32.
This is the same as what we expected before, there is a strong positive relationship between stars and price.
Position- Price
The scatterplot does not show linear relationship between position and price.
And we get Pearson’s correlation coefficient to be r= -004, Pvalue=0.880. The Tobs= -0.161, which is higher than the critical