Correlation Between Variables

The dataset used for this analysis is named as “Bears”

Quantitative variables under investigation include age in months and weight.

The R-value between the two variables was 0.749013. 

Correlation seeks to establish the relationship between two quantitative variables using the Pearson’s correlation coefficient (r). In the interpretation of R-value in correlation, the closer to 1.0, the stronger the relationship between the two variables under consideration. Where the R-value has a positive sign, it implies that the two variables are directly related where as one increases the other one increases. Where the sign is negative, it implies that the variables are inversely related where as one increases the other one decreases. This implies that the covariance between two variables may either be positive or negative. The magnitude of this association is dependent on the units of measurement. 

With an r-value of positive 0.749013, it implies that there is a strong relationship between age in months and weight. The positive sign on the r-value indicates an uphill line which indicates a direct relationship between the two variables where an increase age corresponds to an increase in weight. 

The purpose of the scatterplot graph is to provide a general illustration of the relationship between the two variables (Asuero, Sayago, Gonzalez, 2006). The scatterplot has a linear appearance which implies that the variables have a strong relationship. From the scatterplot, the uphill direction of the datasets can be identified, which denotes a positive relationship between the variables, where an increase in age is coupled with an increase in weight. At the lower values, the data sets are closer to general trends but as the values of the variables increase, more outliers in the data set are identified. 

ReferencesAsuero, A. G., Sayago, A., & Gonzalez, A. G. (2006). The correlation coefficient: An overview. Critical reviews in analytical chemistry, 36(1), 41-59.