STATISTICS Statistics Questions Word Count: 250 page) Question What is correlation? What is an example of two variables that are likely to be correlated because they are both changing over time? (125 words) Correlation mean how much two elements are most alike. According to Reinard (2006), In simplest terms, a correlation is the extent to which two or more things are related…to one another (pp. 88). An example of two variables that are likely to be correlated because they are both changing over time are air quality and soil quality. If we were to measure over a specific period of time, we would see that these two variables could be measured in the environment and to have sustained about an equal amount of pollution over time, considering that the amount of pollution to each variable would be constant. These variables would then be said to be correlational. In that sense, one sees that these variables are good examples. Question 2: ?If a researcher were studying the effects of a teaching method on patient learning outcomes, how must he or she word the research question (different from the hypothesis) to use a t-test to test for statistical differences? What type of data must he or she collect ( interval, ratio, ordinal, nominal)? Why? *Hint: An example of a research question is: Will drug B do a better job of curing acne then drug A? (250 words) In order to do any kind of experiment, one must have a control subject. In order to keep this particular study viable for the control part of the experiment, a t-test must be conducted. According to Allen (2004), Under a set of assumptions that are usually referred to as the Gauss-Markov conditions, the t test can be used to test the significance of a regression… (pp. 66). The type of data which must be collected has to be ordinal data, which basically implies the preference of one value over another, usually (but not always) on a scale of 1 to 10, with 10 being the strongest indicator and 1 being the weakest. Ordinal data will definitely be used instead of interval, ratio, or nominal data. REFERENCES Allen, M.P. (2004). Understanding regression analysis. US: Springer Science Business. Reinard, J.C. (2006). Communication research statistics. US: Sage Publishing.