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.
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The line that connects the dots is relatively straight.
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.
It depends on the statistical test that is being applied. Different tests have different critical values. For the sake of definiteness let's say that you plan to measure the IQs of 30 people using two different paper-and-pencil tests. In advance of your study you decided that the null hypothesis would be that there is no correlation between the two tests and that the alternative hypothesis would be that a positive correlation of the results of test B on those of test A would be accepted at level p=0.01 (or 1%). You proceed with your measurements and you make your correlation calculation. Meanwhile you have consulted the necessary tables to find that the critical value for the correlation statistic based on 30 points is 0.306. This means that you must accept the null hypothesis if the sample correlation coefficient is less than or equal to 0.306. You can accept the alternative if the sample statistic is greater than 0.306.
Biochemical correlation refers to the relationship between the levels or activities of different biochemical substances within an organism. This correlation can help researchers understand how different molecules interact and influence each other within biological systems. By studying these correlations, scientists can gain insights into various physiological processes, disease mechanisms, and potential therapeutic targets.
like for example if a diesel sample is contaminated by paraffin and the flash point is altered is the IBP also altered?