Can you split the name of this up somehow when you resubmit your question, so that an answerer can attempt to use the search facility on Photobucket.com?
Positive correlation = the slope of the scattered dots will rise from left to right (positive slope) Negative correlation = the slope of the scattered dots will fall from left to right (negative slope) No correlation = no real visible slope, the dots are too scattered to tell.
An example of a positive correlation is: the number of cars on the road and the number of greenhouse gas emissions there are. As one of those rises, so does the other. A negative correlation is when one statistic rises causing the other to drop. Look at a few scatter plots and you will easily be able to see positive and negative correlations
you graph the points going downwards
To accurately describe the type of relationship shown by a scatter plot, I would need to see the plot itself. Generally, scatter plots can depict various relationships such as positive, negative, or no correlation. A positive relationship indicates that as one variable increases, the other also increases, while a negative relationship shows that as one variable increases, the other decreases. If the points are randomly scattered without any discernible pattern, it suggests no correlation.
It depends on the range of ages, but a moderate positive correlation.
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.
Positive correlation = the slope of the scattered dots will rise from left to right (positive slope) Negative correlation = the slope of the scattered dots will fall from left to right (negative slope) No correlation = no real visible slope, the dots are too scattered to tell.
None.
If Y increases as X increases, you are referring to a positive correlation. However, if Y falls as X increses, you have a negative correlation.
An example of a positive correlation is: the number of cars on the road and the number of greenhouse gas emissions there are. As one of those rises, so does the other. A negative correlation is when one statistic rises causing the other to drop. Look at a few scatter plots and you will easily be able to see positive and negative correlations
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
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.
When you have a scatter graph and you want to find the correlation of it, you draw a line from one corner to the other of the grid.Also, if the categories are to do with the same thing, then it's a positive correlation.
"If y tends to increase as x increases, then the data have a positive correlation. If y tends to decrease as x increases, then the data have a negative correlation. If the points show no correlation, then the data have approximately no correlation."
It depends on the range of ages, but a moderate positive correlation.
Yes. * A positive correlation is when the dependant variable increases as the independent one does. * A negative correlation is when the dependant variable decreases as the independent one increases. * Perfect correlation is when all the points lie along a straight line; no correlation is when the points lie all over the place. In calculating the correlation coefficient it can have a value between -1 and 1, with 0 indication no correlation and values between 0 and ±1 showing a greater correlation until ±1 which is perfect correlation. Moderate correlation would be one of these intermediate values, eg ±0.5, which shows the points are moderately related.