Correlation is a statistical process of establishing the relationship between variables. There are three types of correlation, positive correlation, negative correlation, and no correlation. Positive correlation refers to a relationship where changes in the values of two variables vary directly. Therefore, an increase in the value of one variable predicts an increase in the value of the other variable. Similarly, a decrease in the values of one variable predicts a decrease in the values of the other variable. On the other hand, a negative correlation refers to a relationship where the values of two variables vary inversely (Weaver et al., 2017) . Whereas, no correlation means that the values of the variable are not related. Thus they do not exhibit any linear dependence. Other than the types of correlation, the strengths are also three. The correlation can be strong, weak, or no correlation. Strong correlation means that the values of one variable highly predict the placement of the values of a second variable. As such, when the values of the two variables are plotted on a scatter plot, the plotted points lie close to the best line of fit. Variables can also exhibit weak correlations. A weak correlation means that the values of the two values predict each other. However, the rate of predictability is low. Therefore, when plotted on a scatter plot. The points lie at varying distances along with the scatter plot. Finally, two variables are considered to have no correlation when the values of one variable cannot be used to predict the values of the second variable. Thus, it is impossible to draw the best line of fit for the various data points. In statistics, correlation is expressed using a correlation coefficient. The correlation coefficient is denoted by the letter r. its value ranges from 1 to -1. A correlation of r=1 indicates a strong correlation coefficient. When r = 0, the two variables are not correlated. Whereas, r= -1 indicates a strong negative correlation. For instance when given a correlation coefficient of r = -0.25, one can establish that the variables are negatively correlated. An increase in the values of one variable increase, predicts a decrease in the values of the second variable. Secondly, the closeness of 0.25 to 0 compared to -1 indicates that the correlation is somewhat weak. A correlation was put to taste by performing a survey that sought to establish the prevalence of cancer among US citizens age. The two variables that were under analysis are the prevalence of cancer and age. Thus, the survey involved the analysis of data from 522 USA citizens. The research question sought the respondent’s age and whether they had been diagnosed with cancer. It tested the hypothesis that stated that cancer prevalence increases with age. The ages of the respondents were clustered into groups of five years as shown in table 1.
|
Age Group Midpoints |
Cancer Diagnosis |
|
2.5 |
1 |
|
7.5 |
1 |
|
12.5 |
3 |
|
17.5 |
3 |
|
22.5 |
4 |
|
27.5 |
6 |
|
32.5 |
8 |
|
37.5 |
14 |
|
42.5 |
22 |
|
47.5 |
35 |
|
52.5 |
38 |
|
57.5 |
40 |
|
62.5 |
43 |
|
67.5 |
57 |
|
72.5 |
52 |
|
77.5 |
47 |
|
82.5 |
64 |
|
87.5 |
30 |
|
92.5 |
20 |
|
97.5 |
34 |
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The results accepted the null hypothesis giving r = 0.77. Thus, the survey proved that age and prevalence were positively correlated. The results were expected given that they concurred with the null hypothesis. Furthermore, the results concur with DeSantis et al., (2019) that noted that Americans at young ages are less prevalent to cancer. However, as their age advances, their prevalence of cancer increases. In conclusion, correlation is a process that seeks to establish the relationship between two variables. Correlation is represented using a correlation coefficient (r). the value of r ranges from -1 to 1. -1 indicates a strong negative correlation. Zero indicates no correlation, whereas, 1 indicates a strong positive correlation. When given a correlation coefficient of r = -0.25, it indicates a weak negative correlation. On the other hand, the correlation r= 0.77 indicates a somewhat strong positive correlation between age and the prevalence of cancer.
References
DeSantis, C., Miller, K., Dale, W., Mohile, S., Cohen, H., & Leach, C. et al. (2019). Cancer statistics for adults aged 85 years and older, 2019. CA: A Cancer Journal For Clinicians , 69 (6), 452-467. https://doi.org/10.3322/caac.21577
Weaver, K., Morales, V., Dunn, S., Godde, K., & Weaver, P. (2017). An Introduction to Statistical Analysis in Research: With Applications in the Biological and Life Sciences (pp. 48-127). John Wiley & Sons.
