Respuesta :
If your data consists of scores from variables that are correlated or those that you already correlated with, then the most reasonable data statistical analysis would be a linear regression. This is used usually in order to determine if one variable predicts the other.
Answer:
C. a set of four data pairs with a correlation coefficient r = –0.8
Step-by-step explanation:
The options for this answer are
A. a set of nine data pairs with a correlation coefficient r = –0.4
B. a set of five data pairs with a correlation coefficient r = 0.3
C. a set of four data pairs with a correlation coefficient r = –0.8
D. a set of six data pairs with a correlation coefficient r = 0.6
The correlation coefficient is a statistical measure about the relationship between two variables. Specifically, it tells if there's a positive or negative correlation, and how strong or weak it is.
The interval of a correlation coefficient is from -1 to 1, and the nearer the value gets to zero, the less correlation exist between variables. If the coefficient is near 1, that means there is a strong positive correlation, and if the coefficient is near -1, that means there's a strong negative correlation.
So, in this case, option C shows a correlation coefficient of -0.8, which is the "highest" coefficient among options, this means that it represents the strongest correlation among options.
Therefore, the best data set that fits a linear correlation is C.
