Bivariate Correlations: Drinking And Income

Bivariate Correlations Student’s Name Institutional Affiliation Bivariate Correlations Some studies are done to evaluate the relationship between two variables, which are mostly categorized as X and Y. Correlation is useful in determining the relationship between two dissimilar variables (Sommer, 2006). The calculation of correlation coefficient is possible when there are two sets of scores from either matched groups or the same person. The coefficient can be used to interpret direction, positive and negative, and degree of the relation between two variables. For bivariate analysis, two measurements need to be taken for each observation. The strength and direction of the relationship between the variables being observed are normally linear. In this case, research will be conducted to determine the relationship between drinking and income. This would mean that the two variables are the number of days an individual drinks in a month and their income. The measure is useful in determining how individual’s income might affect their habit of drinking alcohol by determining the number of days in a month when they consume alcohol. This information can be helpful to health officers to determine how an individual’s income might affect the number of times they drink alcohol. The overall expectation would be that the lower the income of the individual, the least number of days they are likely to drink due to low purchasing power, whereas, the higher the income of the person the more likely they are to spend on alcohol. Therefore, the relationship between the two variables would be positive since the higher the income the higher the number of drinking days. The statistics considers that employment is the only source of income and therefore, the visits to the bar is only affected by the income earned by the employees. However, in case there are other variables that would affect the linearity of the correlation, then the accuracy of the statistics would be affected as well. The hypothetical research involved selecting six random companies and determining the average income for the individuals as well as the average number of days the individuals went to the bar. Results from the data collection can be represented in the table and scatter plot below. Relationship between Income and Number of Days Spent in The Bar. CompanyAverage Income (PA)Number of Days at the Bar (Monthly)Portland Company Ltd$ 24,00013Mel Distributors$ 22,3109Oak Tree Company Truce Oil Company Pine Distributors Shalom Security Ltd $ 19,720 $ 31,000 $ 13,000 $ 28,60012 16 6 15  EMBED Excel.Chart.8 \s 
 The data taken ...