Correlation is a statistical analysis tool that is used to ascertain the relationships between two linear variables. A correlation can only be effective when two or more variable are compared side by side; whereby, their strengths are compared to determine whether their relationship is correlated or non correlated (Ferguson, 1959, 1). Correlation can be established in two different ways, first using the graphical method; whereby, the two variables are plotted on a graph and a line of best fit is drawn to assess whether there is dispersion from the line or using the spearman’s correlation coefficient method. In the former method, the closer they are to the line, the more correlated they are and the more dispersed from the line, the less correlated they are (Johnston &Yen, 2009, pp. 1-4) and Rummel, 1976, p 1. Correlation will normally range from -1 to 1 and r= is used, which is independent from either the x or y variable to signify the level of relationship (Ferguson, 1959, Johnston p. 1). When correlation is exactly 0, it signifies a lack of linear relationship between the two variable; hence, the given sets of data will be said to be uncorrelated. As you can see in the example below the two variables as seen to be uncorrelated. When the correlation is less than 0, we say the two variable have a negative correlation, since when the variable X increases, the Y variable decreases, showing that r=-1. A negative correlation between two sets of values can be shown by the graphical representation below. Lastly, when the relationship is greater than 0, we say the two variants are positively correlated because as X variable increases, the Y variable also increases too in perfect union (Shaughnessy and Zechmeister, 2009, pp.1-10 and Aggarwal, 1986, pp. 2-14). Months  Rainy days ( X)  Umbrellas sold (Y) 1       5                100                2       4                98                 3       2                50                 4       8                120                5       15               258                6       19               652                7       2                6                  8       8                80                 9       10               250                10      16               600                11      20               822                12      18               584                                                             The data above is plotted in the graph below by assignment the vertical axis (x) the dependent variable, which is the no of umbrellas sold and the horizontal axis (y) which is the independent variable to the rainy days. In the example above we can comfortably say there is a correlation of positive between the two variables. This means the sale of umbrellas is determined by the number rainy days in any single month. The other method used to determine correlation is the Pearson’s C orrelation C oefficient formula method, which uses the formulae below to derive accurate numbers of the correlation of two or more variables ( Shaughnessy, Zechmeister & Zechmeister, 2009, pp. 2-9). In order to use to solve the following equation, additional information is required as computed below. Formular (a)X To get the Pearson’s C orrelation Coefficient, r, we substitute the above values in formular (a) You cannot just by calculating the correlation deduce that on a variable will always react in a certain way when used in conjunction with the other