Analysis & Interpretation of Correlation

• Outliers can have a significant impact on correlation by pulling the line of best fit towards them, potentially strengthening or weakening the correlation between two variables.
• Some limitations of correlation analysis include the inability to determine causation, the sensitivity to outliers, and the assumption of linearity between variables.
• Correlation refers to a relationship between two variables, while causation refers to the idea that one variable directly causes a change in another variable.
• Correlations are calculated using a Spearman's Rho test or Pearson's Product-Moment correlation to get a coefficient between -1 and +1.
• Correlations can be seen when a line of best fit is drawn on a scattergram. The closer the points are to the line of best fit, the stronger the correlation.
• No correlation means there is no relationship between the variables.
• A perfect positive correlation is +1 and a perfect negative correlation is -1.
• A negative correlation is found as one variable increases, the other decreases.
• A positive correlation is found as one variable increases so does the other.
• A correlation coefficient of 0 indicates no linear relationship between the variables.
• A correlation coefficient of +1 indicates a perfect positive linear relationship between two variables.
• A correlation coefficient of -1 indicates a perfect negative linear relationship between two variables.
• When no correlation is seen a coefficient of zero will show.
• Correlations are displayed in scattergrams.
• Correlations can be perfect if the coefficients are either +1 (perfect positive) or -1 (perfect negative).
• Correlations can be weak (closer to 0) or strong (closer to 1).
• A negative correlation has a coefficient between 0 and -1, the closer it is to -1 the stronger the correlation.
• A positive correlation has a coefficient between 0 and +1, the closer it is to +1 the stronger the correlation.
• Correlations calculate coefficients to show the type and strength of the relationship between the variables.