0
No, it's a small enough value that it doesn't suggest any correlation at all. There's no hard-and-fast rule for interpreting the correlation coefficient: a very strong correlation in one discipline might be considered weak in others, and the correlation coefficient might be misleading in some cases. But most of the time, you want r to be at least plus or minus 0.9 before even thinking about any relation between the data.
Still curious? Ask our experts.
Chat with our AI personalities
Professor
Blake
CoachAdd your answer:
Earn +20 pts
How do you find out what correlation a scatter plot has?
It is easy to find the correlation. First you see how far apart the dots are. if they are going UP like this / <---- it means its a positive correlation. if its like this \ <---- its a negative correlation. if its everywhere its a neutral (although they almost never do them in tests). To find out the strength is your opinion. If alot are grouped together almost making a line its a Strong correlation. Then you decide if its a Strong or Weak correlation depending on how close together the dots are. So put them together in a 1 mark question like::::it is a Strong Positive Correlation
What correlation is stronger -1.00 or plus 1.00?
they are the same. +1.00 and -1.00 are the strongest correlations. If you have +.92 and -.92 then that's a strong correlation but if you have -.15 and +.15 then that would be a weak correlation. There for + 1 or - 1 makes no difference
What is correlation What are the different types of correlation Why is it important to determine correlation What does it mean when it is said that two variables have no correlation?
A correlation is the relationship between two or more variables. Correlations are described as either weak or strong, and positive or negative. There can be a perfect correlation between variables, or no correlation between variables. It is important to determine the correlation between variables in order to know if and how closely changes in one variable are reflected by changes in another variable. This is done by determining the coefficient of correlation (r), which describes the strength of the relationship between variables and the direction. -1 ≤ r ≤ +1 if r= +1 or -1, there is a perfect correlation if r= 0 there is no correlation between the variables. a value closer to + or - 1 demonstrates a strong correlation, while a value closer to 0 demonstrates a weak correlation. a + value demonstrates that when one variable increases the other variable increases, while a - value demonstrates that when one variable increases the other variable decreases. However, it is very important to understand that correlation is not the same as relationship. Consider the two variables, x and y such that y = x2 where x lies between -a and +a. There is a clear and well-defined relationship between x and y, but the correlation coefficient r is 0. This is true of any pair of variables whose graph is symmetric about one axis. Conversely, a high correlation coefficient does not mean a strong relationship - at least, not a strong causal relationship. There is pretty strong correlation between my age and [the log of] the number of television sets in the world. That is not because TV makes me grow old nor that my ageing produces TVs. The reason is that both variables are related to the passage of time.
What is the correlation between two quantities?
It is a measure of the extent to which a linear change in one quantity is accompanied by a linear change in the other quantity. Note that only linear changes are measured and that there is no causality.
What is Multiple Correlation?
Let me assume that you are familiar with the (Pearson) correlation coefficient. If you estimate how one variable might be a linear function of another (using least-squares) then the measure of how strong the association is is known as that with which you are correlation coefficient. If you generalise by estimating what linear function one variable is of two or more other variables then the measure of how strong the relationship is is the multiple correlation.For mathematical reasons which may or may not interest you, and which I won't go into here, if we now go backwards we find that the multiple correlation for the situation where one variable is regressed against one other variable is just the square of the Pearson correlation coefficient.As you probably know, the Pearson ranges from -1 to +1. Because the multiple correlation is the squared value it ranges only from 0 to 1 and can indicate only degree of association, not the sense of direction.
Is 1.10 a strong correlation?
No, The correlation can not be over 1. An example of a strong correlation would be .99
What is the maximum and minimum value of the correlation coefficient?
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
Is the Correlation Coefficient of 35 strong?
The correlation coefficient must lie between -1 and +1 and so a correlation coefficient of 35 is a strong indication of a calculation error. If you meant 0.35, then it is a weak correlation.
What does a correlation coefficient of zero indicates?
A coefficient of zero means there is no correlation between two variables. A coefficient of -1 indicates strong negative correlation, while +1 suggests strong positive correlation.
What is considered a strong correlation?
"Strong" is very much a subjective term. Not only that, but it depends on expectations. In economics I would consider 70% to be a strong correlation, but for physics I would want more than 95% before I called the correlation strong!
Is 0.17 the strong correlation coefficients?
No.
What is strong correlation?
A very small effect having a greater side effect on a variable or an object may be termed as a strong correlation.
Is 1.00 a strong correlation?
A perfect positive correlation would be exactly 1; 1.00 means "0.995 or higher", which is quite strong indeed.
Interpret an r value of 0.11?
a strong negative correlation* * * * *No it is not. It is a very weak positive correlation.
When are is close to -1 does it indicate a weak negative correlation?
No, it indicates an extremely strong positive correlation.
What would a strong association but weak correlation look like?
If the form is nonlinear (like if the data is in the shape of a parabola) then there could be a strong association and weak correlation.
What does a strong negative correlation coefficient mean?
The graph follows a very strong downward trend. Would have helped if you specified which correlation coefficient; there are different types.
