a correlation statement is a sentence that says whether the points on a scatterplot have a positive, negative or no correlation.
ex. This graph shows a negative correlation, as the number of cows increases (x axis data) the profitability decreases (y axis data).
Event B has something in common with Event A.
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No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
No.
No. The units of the two variables in a correlation will not change the value of the correlation coefficient.
The statement "correlation does not imply causation" means that just because two variables are correlated—meaning they change together—it does not necessarily mean that one variable causes the change in the other. Correlation can arise from various factors, including coincidence, confounding variables, or reverse causation. Therefore, establishing a cause-and-effect relationship requires further investigation beyond mere correlation.
Event B has something in common with Event A.
A positive correlation, nothing more.
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There aren't. This is an untrue statement. There is no correlation between sexual orientation and physical disabilities.
Auto correlation is the correlation of one signal with itself. Cross correlation is the correlation of one signal with a different signal.
The statement about the correlation between the number of hours a person studies and their exam score is true; generally, more study hours lead to higher scores, indicating a positive correlation. Similarly, there is often a positive correlation between a child's age and their height, as children typically grow taller as they get older. As for the speed of a vehicle, it would depend on the context; if you're referring to speed increasing with time or distance, that could also represent a positive correlation.
positive correlation-negative correlation and no correlation
No. The strongest correlation coefficient is +1 (positive correlation) and -1 (negative correlation).
The correlation can be anything between +1 (strong positive correlation), passing through zero (no correlation), to -1 (strong negative correlation).
The sentence "Research studies have consistently shown a strong correlation between smoking and an increased risk of developing lung cancer" supports the thesis statement that smoking is a major risk factor for lung cancer.
If measurements are taken for two (or more) variable for a sample , then the correlation between the variables are the sample correlation. If the sample is representative then the sample correlation will be a good estimate of the true population correlation.