Complete the following steps to interpret a correlation analysis. Key output includes the Pearson correlation coefficient, the Spearman correlation coefficient, and the p-value. Show
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Step 1: Examine the relationships between variables on a matrix plotUse the matrix plot to examine the relationships between two continuous variables. Also, look for outliers in the relationships. Outliers can heavily influence the results for the Pearson correlation coefficient. Determine whether the relationships are linear, monotonic, or neither. The following are examples of the types of forms that the correlation coefficients describe. The Pearson correlation coefficient is appropriate for linear forms. Spearman's correlation coefficient is appropriate for monotonic forms. No relationshipThe points fall randomly on the plot, which indicates that there is no linear relationship between the variables. Moderate positive relationshipSome points are close to the line but other points are far from it, which indicates only a moderate linear relationship between the variables. The points fall close to the line, which indicates that there is a strong linear relationship between the variables. The relationship is positive because as one variable increases, the other variable also increases. Large negative relationshipThe points fall close to the line, which indicates that there is a strong negative relationship between the variables. The relationship is negative because, as one variable increases, the other variable decreases. MonotonicIn a monotonic relationship, the variables tend to move in the same relative direction, but not necessarily at a constant rate. In a linear relationship, the variables move in the same direction at a constant rate. This plot shows both variables increasing concurrently, but not at the same rate. This relationship is monotonic, but not linear. The Pearson correlation coefficient for these data is 0.843, but the Spearman correlation is higher, 0.948. Curved quadraticThis example shows a curved relationship. Even though the relationship between the variables is strong, the correlation coefficient would be close to zero. The relationship is neither linear nor monotonic. Key Result: Matrix Plot
In these results, you can see positive linear relationships, negative linear relationships, possible curved relationships, and a few outliers.
Step 2: Examine the correlation coefficients between variablesUse the Pearson correlation coefficient to examine the strength and direction of the linear relationship between two continuous variables. StrengthThe correlation coefficient can range in value from −1 to +1. The larger the absolute value of the coefficient, the stronger the relationship between the variables. For the Pearson correlation, an absolute value of 1 indicates a perfect linear relationship. A correlation close to 0 indicates no linear relationship between the variables.DirectionThe sign of the coefficient indicates the direction of the relationship. If both variables tend to increase or decrease together, the coefficient is positive, and the line that represents the correlation slopes upward. If one variable tends to increase as the other decreases, the coefficient is negative, and the line that represents the correlation slopes downward. Consider the following points when you interpret the correlation coefficient:
Correlation: Age, Residence, Employ, Savings, Debt, Credit cardsMethod Correlation type Pearson Rows used 30 Correlations Age Residence Employ Savings Debt Residence 0.838 Employ 0.848 0.952 Savings 0.552 0.570 0.539 Debt 0.032 0.186 0.247 -0.393 Credit cards -0.130 0.053 0.023 -0.410 0.474 Key Result: Pearson correlationA positive linear relationship exists between Residence and Age, Employ and Age, and Employ and Residence. The Pearson correlation coefficients for these pairs are:
These values indicate that there is a moderate positive relationship between the variables. A negative linear relationship exists for the following pairs, with negative Pearson correlation coefficients:
The relationship between these variables is negative, which indicates that, as debt increases, education and savings decrease, and as the number of credit cards increases, the savings decrease, as well. How do you test if a correlation is statistically significant?Compare r to the appropriate critical value in the table. If r is not between the positive and negative critical values, then the correlation coefficient is significant. If r is significant, then you may want to use the line for prediction.
What is the appropriate test statistic to use to determine the correlation coefficient?To determine if a correlation coefficient is statistically significant you can perform a t-test, which involves calculating a t-score and a corresponding p-value.
Which test would you use to determine if there is a correlation?Spearman rank correlation: Spearman rank correlation is a non-parametric test that is used to measure the degree of association between two variables.
What is the primary purpose of Pearson and Spearman correlation coefficient?Pearson correlation: Pearson correlation evaluates the linear relationship between two continuous variables. Spearman correlation: Spearman correlation evaluates the monotonic relationship. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data.
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