Doctor, I can't stress this enough, you've saved my academic hide. Thank you very, very much! Most of my classes give so much waffle in explaining what to do, but you made it simple and to the point while also explaining why you do it.
Thank you so much Dr. Grande! This video is on point for me. I have watched several videos on correlation and testing assumptions and by far, none compare to opening my understanding. Thank you again and God bless you!
This was great! Our book did not go into this level of detail on testing assumptions. I would have missed some of these points if I had only read the assignment. Thank you for the clarifications.
Does it mean that the kurtosis and skewness falling outside the limits mean that the 'satisfaction' data is not normally distributed? If so, isn't the assumption then violated and you will need to transform DV and/or IV and coax the residuals to normality?
So basically if all the points together for the linearity assumption run in a straight line then the assumption is met. How about if some of the points run in a straight line but some of the other points run differently, that would indicate a curve which would mean no linear relationship so the assumption was not met? Also when testing for the multicollinearity assumption, if there is multicollinearity then the assumption was met but if there is none then the assumption was not met or is it the other way around?
just a quick question in this case the correlation is 0.88. these is note in my spss saying correlation is signinificant at the 0.01 level. So what does 0.88 indicate?? thanks
Hello, thank you for your video. I am trying to find the relationship between Happiness (Ordinal variable with more than 5 categories) and Income (continuous variable). In this case, I am having trouble deciding if I should run Pearson correlation (by treating the happiness variable as continuous since it has more than 5 categories) or whether run a spearman's correlation? Also, while I tried to check the assumption for Pearson correlation (treating happiness as continuous), I found that Income is not normally distributed. And when I generated a scatterplot for these two variables, the result is not clear and somehow visually indicates no linear relationship. What should I Do???