Reference no: EM132843437

This week as we look at regression analysis - one of the most widely used tools in statistics.

• When you think about it, putting a regression model together is really not that difficult. After all, I assume if you are doing that you would have a feel for the process you are modeling and the variables to choose for the model. But the real question is "is the model any good"? Here is where measures of validity and reliability come into play. How do we measure validity in a regression model? Let's save the reliability issues for the next topic. 
• As mentioned in Topic#1 for this week, the issues of validity and reliability are critical when evaluating your regression model. For reliability the most significant issues are serial correlation, heteroscedasticity and collinearity. What are these issues and how do they affect the reliability of our regression model? And how can we spot them if they are there?
• One of the interesting things about regression is the ability to model many different patterns of data yet still remain in a linear model mode. What are some of the non linear models that can be modeled linearly? How do we set up such models to be used by our linear regression tool?

While you can continue to make a single long response, I suggest you separate your responses to cover regression in several posts rather than a single long post.