Discussion Response
Read the passage below then explain do you agree or disagree, or can you relate to his or her opinion in the post? ADD any thoughts or additional information that you may have found concerning their topic?
According to Karl Pearson, when someone says that correlation does not imply causation, this means that just because he or she can see a connection or a mutual relationship between two variables. It does not mean that one is caused by the other. By computing the value of the linear correlation coefficient to be 1.200, there are several considerations to conlude that. First, the linear correlation coefficient is generally used to determine the strengh of the linear relationship between two variables in the data set values. Second, it is denoted by the letter “r”.The value of “r” should always be between minus one, and plus one (-1 and +1). Third, the r-value represent the nature of the relationship between two variables.
If the change in one variable accompanied the change in the other variable, then two variables are said to be correlated and there are two variables deviate in the same direction, then it is said to be possitively correlated. If the two variables deviate in the same direction, then it is said to be negative.
Overall, the possible values of the “correlation coefficient” range from minus one to plus one, with minus one (-1) indicating a perfectly linear negative, which means the inverse, the correlation is sloping dwonward, and plus one (+1)indicating a perfectly linear positive correlation which means a sloping upward (Garson, G.D. (2022).
