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.
No.
A very small effect having a greater side effect on a variable or an object may be termed as a strong correlation.
a strong negative correlation* * * * *No it is not. It is a very weak positive correlation.
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.
No, The correlation can not be over 1. An example of a strong correlation would be .99
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
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.
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.
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.
No.
A very small effect having a greater side effect on a variable or an object may be termed as a strong correlation.
A perfect positive correlation would be exactly 1; 1.00 means "0.995 or higher", which is quite strong indeed.
a strong negative correlation* * * * *No it is not. It is a very weak positive correlation.
No, it indicates an extremely strong positive correlation.
A correlation of .12 is considered weak in social sciences. It suggests that there is a very minor relationship between the variables being studied. Strong correlations are typically closer to 1 or -1.
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.