It looks like a straight line increasing as the x-values increase. So basically a line that goes from the bottom left area to the top right area on a graph.
You can look at the r value and tell from there. Also you can try to see if there is a linear assocation and if its tightly centered or loosely centered.
Strength and direction of linear relation. Closer to 1 is positive linear association, closer to -1 is positive negative association and closer to 0 means no linear relation. Remember that 0 does not mean that there is no relation - just no linear relation.
The closer the correlation is to 1 or -1, the more linear the data is
You cannot. Or rather, you should not. You do not know if the relationship is linear or something else. A scatter graph is the best way to establish the nature of the relationship. For example, the correlation between x and y, when y = x2 between, say, -4 and +4 is zero (because of symmetry). That would lead you to conclude that there was no relationship. You could not be more incorrect!
Correlation study is restricted to linear relationships between the variable(s) being studied.
The product-moment correlation coefficient or PMCC should have a value between -1 and 1. A positive value shows a positive linear correlation, and a negative value shows a negative linear correlation. At zero, there is no linear correlation, and the correlation becomes stronger as the value moves further from 0.
If the two variables increase together and decrease together AND in a linear fashion, the correlation is positive. If one increases when the other decreases, again, in a linear fashion, the correlation is negative.
men and penises
Correlation is an estimate of a linear relationship between two variables and takes no account of non-linear relationship. If the relationship is quadratic and the domain is symmetric about some point, the correlation will be zero. It is, thus possible for the points on the scatter plot to lie exactly on a parabola while the calculated correlation is zero. In such a case, it is easy to make a prediction despite no correlation.
Scatter-plot shows correlation between two different variables (one on the y-axis, the other on x-axis). If there is linear correlation, the scatter-points form a straight line from zero (origo) to some direction. The more cloud-like distribution the scatter-plot does have, the less those variables in question have correlation or dependence with each other.
If the data have a positive or negative correlation, it means the data have a linear relationship in the form of an equation of a line; or Y = mX + b.
False.
No. If the correlation coefficient is close to 1 or -1, then the two variables have a high degree of statistical linear correlation. See the related link, particularly the graphs which illustrate correlation.
You can look at the r value and tell from there. Also you can try to see if there is a linear assocation and if its tightly centered or loosely centered.
Strength and direction of linear relation. Closer to 1 is positive linear association, closer to -1 is positive negative association and closer to 0 means no linear relation. Remember that 0 does not mean that there is no relation - just no linear relation.
The correlation coefficient takes on values ranging between +1 and -1. The following points are the accepted guidelines for interpreting the correlation coefficient:0 indicates no linear relationship.+1 indicates a perfect positive linear relationship: as one variable increases in its values, the other variable also increases in its values via an exact linear rule.-1 indicates a perfect negative linear relationship: as one variable increases in its values, the other variable decreases in its values via an exact linear rule.Values between 0 and 0.3 (0 and -0.3) indicate a weak positive (negative) linear relationship via a shaky linear rule.Values between 0.3 and 0.7 (0.3 and -0.7) indicate a moderate positive (negative) linear relationship via a fuzzy-firm linear rule.Values between 0.7 and 1.0 (-0.7 and -1.0) indicate a strong positive (negative) linear relationship via a firm linear rule.The value of r squared is typically taken as "the percent of variation in one variable explained by the other variable," or "the percent of variation shared between the two variables."Linearity Assumption. The correlation coefficient requires that the underlying relationship between the two variables under consideration is linear. If the relationship is known to be linear, or the observed pattern between the two variables appears to be linear, then the correlation coefficient provides a reliable measure of the strength of the linear relationship. If the relationship is known to be nonlinear, or the observed pattern appears to be nonlinear, then the correlation coefficient is not useful, or at least questionable.
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