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.
The line that connects the dots is relatively straight.
A scatter plot.A scatter plot.A scatter plot.A scatter plot.
The points are increasing from left to right. They are scattered but they are increasing non the less.
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You can describe if there's any obvious correlation (like a positive or negative correlation), apparent outliers, and the corrlation coefficient, which is the "r" on your calculator when you do a regression model. The closer "r" is to either -1 or 1, the stronger that correlation is.
The line that connects the dots is relatively straight.
A scatter plot.A scatter plot.A scatter plot.A scatter plot.
A correlation exists in a scatter plot if there is a general trend in the outputs as inputs increase. If the outputs generally increase in value, then there is a positive correlation. If the outputs generally decrease in value, then there is a negative correlation.
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The points are increasing from left to right. They are scattered but they are increasing non the less.
A scatterplot with no correlation means that there is no relation between the two categories, a negative correlation means that the two categories have a relationship that as one gets greater the other gets smaller
you graph the points going downwards
It tells you how strong and what type of correlations two random variables or data values have. The coefficient is between -1 and 1. The value of 0 means no correlation, while -1 is a strong negative correlation and 1 is a strong positive correlation. Often a scatter plot is used to visualize this.
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.
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It depends on the range of ages, but a moderate positive correlation.