Assingment

Order Description Answer these three questions in" rel="nofollow">in one page and these questions in" rel="nofollow">in statistics area: 1-For the followin" rel="nofollow">ing hypothesis determin" rel="nofollow">ine the followin" rel="nofollow">ing: 1- Identify the measured variables. 2- The scale of measurement. 3- What the type I and Type II error would be. 4- Any potential covariates. 5- Are there any latent variables present, if so what might be a good measure? Hypothesis: The market will have a negative reaction to merger announcements ................................................................................................................................................ 2-After a long couple of months of collectin" rel="nofollow">ing and cleanin" rel="nofollow">ing your data, you have fin" rel="nofollow">inally gotten to the poin" rel="nofollow">int of conductin" rel="nofollow">ing your statistical tests. You set alpha at .05, and your results are in" rel="nofollow">insignificant. All of that time wasted but wait� you thin" rel="nofollow">ink to yourself �what happens if I set alpha at .10�. Your results are now significant, so you can contin" rel="nofollow">inue on with your manuscript. Is there anythin" rel="nofollow">ing you can so to try to get a significant result when alpha equals .05? What effect does changin" rel="nofollow">ing the alpha effect have on your error? Are there any ethical considerations? Will this have any effect on your ability to be published? ................................................................................................................................................................................................................ 3- Effective Research Design has been identified as one of the important aspect of a manuscript that has to be well written. How do you carry out an effective research design in" rel="nofollow">in practice? What is your understandin" rel="nofollow">ing of data cleanin" rel="nofollow">ing and screenin" rel="nofollow">ing? What are the differences and similarities? Of what relevance is data screenin" rel="nofollow">ing and cleanin" rel="nofollow">ing in" rel="nofollow">in research design? Do you agree with the notion that it slows down progress of work and constitutes a sheer waste of time?