QNT ( CORERLATION, REGRESSION)
1. We are all familiar with the concept of correlation. It is a powerful tool to help us see how strongly two variables are related to each other. But there are some issues best seen in the fact that research has shown in New York City that there is a direct and strong correlation between ice cream sales and street crime. What are the issues this example tells us are inherent in correlation analysis?
2. When you think about it putting a regression model together is 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 chose 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?
3. As mentioned in Topic #2 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, heterscedasticity and colinearity. What are these issues and how do they affect the reliability of of our regression model? And how can we spot them if they are there?
1. We are all familiar with the concept of correlation. It is a powerful tool to help us see how strongly two variables are related to each other. But there are some issues best seen in the fact that research has shown in New York City that there is a direct and strong correlation between ice cream sales and street crime. What are the issues this example tells us are inherent in correlation analysis?
2. When you think about it putting a regression model together is 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 chose 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?
3. As mentioned in Topic #2 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, heterscedasticity and colinearity. What are these issues and how do they affect the reliability of of our regression model? And how can we spot them if they are there?
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