For example, a scatterplot of one’s science scores and math scores may show us that as science scores increase, math scores also increase i.e. Correlation is only illustrative of how two variables move together. Well setup regressions give us the causality between two variables where we can safely deduce that on average, a change in the independent variable will lead to a certain degree and direction of change in the dependent variable. Unlike regressions, correlation does not indicate causality. Davies (1971) proposed the following descriptions of for different (and absolute) values of the correlation coefficient though usually it is enough to just categorise them into weak, moderate and strong. Typically, absolute values between 0 and 0.3 are considered weak correlations, 0.4-0.5 are considered moderate, while anything between 0.6 and 1 is treated as a strong correlation. The higher the absolute value is of the correlation coefficient, the stronger is this relationship. the magnitude) and direction (positive or negative) of the association/relationship between the two variables. The correlation coefficient tells us the strength (i.e. Correlation is simply a two-way relationship between two variables. These are terms that we use when running regressions where the effect of one or more independent variables is being studied on one dependent variable. When talking about correlations, we do not refer to variables as ‘independent’ or ‘dependent’ variables. For example, an education can have a positive correlation with one’s wages (when one increases, the other also increases, and vice versa) while an increase in exercise may be correlated with a decrease in weight (negative correlation). Correlation is simply defined as the association/relationship between two variables.
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