I'm EViews 8 user. The larger the absolute value of the coefficient, the stronger the relationship between the variables. How to Interpret Pearson’s Correlation Coefficients Pearson’s correlation coefficient is represented by the Greek letter rho ( ρ ) for the population parameter and r for a sample statistic. In the matrix view setting, the columns of the matrix should contain the conditioning information, and the number or rows should match the original matrix. These commands and functions may be divided into roughly four distinct types: (1) utility commands and functions, (2) element functions, (3) matrix algebra functions, and (4) descriptive statistics functions. For the Pearson correlation, an absolute value of 1 indicates a perfect linear relationship. The closer correlation coefficients get to -1.0 or 1.0, the stronger the correlation. Correlation coefficients range from -1.0 (a perfect negative correlation) to positive 1.0 (a perfect positive correlation). It allows missing values to be deleted in a pair-wise or row-wise fashion. A perfect downhill (negative) linear relationship […] (See "How-to-interpret regression output" here for Stata and Excel users). A correlation close to 0 indicates no linear relationship between the variables. When someone speaks of a correlation matrix, they usually mean a matrix of Pearson-type correlations. Direction Correlation Matrix Introduction This program calculates matrices of Pearson product-moment correlations and Spearman-rank correlations. The purpose of this analysis was to determine the relationship between social factors and crime rate. This correlation coefficient is a single number that measures both the strength and direction of the linear relationship between two continuous variables . Interpreting SPSS Correlation Output Correlations estimate the strength of the linear relationship between two (and only two) variables. In statistics, the correlation coefficient r measures the strength and direction of a linear relationship between two variables on a scatterplot. Herein, unemployment rate, GDP per capita, population growth rate, and secondary enrollment rate are the social factors. Hence, the essence of this tutorial is to teach students the significance of these features and how to interpret their results. EViews provides a number of commands and functions that allow you to work with the contents of your matrix objects. I was thinking about a set of commands that would first create an empty table called "corr_tab" and then update the table to make it a correlation matrix. If, for example, z is a leading indicator of y, then you should observe the highest significant correlation at a lag greater than 0. How to read the number is what we'll cover next in this lesson. I will be using EViews analytical package to explain a regression output, but you can practise along using any analytical package of your choice. The value of r is always between +1 and –1. You may specify keywords from one of the four sets (Pearson correlation, Spearman correlation, Kendall’s tau, Uncentered Pearson) corresponding the computational method you wish to employ. To interpret its value, see which of the following values your correlation r is closest to: Exactly –1. By default, cross correlation displays correlograms from 0 to 36 leads-lags for a pair of series (say y and z). Enjoy the videos and music you love, upload original content, and share it all with friends, family, and the world on YouTube. The correlation coefficient can range in value from −1 to +1. Interpreting the Statistic: Direction. Below table represents a sample correlation matrix result. 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