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Nov5-09, 02:01 PM
P: 89
Okay, just think how cool it would be to predict other people! Roommates? Co-workers? How awesome would it be if you could predict whether attractive members of the opposite sex are flirting with you, and even have an equation for it, etc.

I mentioned how in my Multivariate Statistics class we learned about Statisticians making equations when they've been given multiple variables (although not as precise as Physics, you can still get a general range). Maybe I could use this concept of "Multiple Regression Analysis" as a starting point, and then maybe add much Calculus and other concepts into the pot? Then maybe if certain details of existing statistical methods are tweaked, and combined with other ideas that haven't been combined yet, maybe there can be some innovations? On top of that, in the Social Sciences they usually don't post multiple regression equations in their peer-review journals, even if they have the data to create them in many many situations, so doing so may make an impact and be more unique than not including them in the journal (remember Galileo and Newton claimed much of their success was because of moving toward the mathematical equations ball game). What are everyone's thoughts?

Okay as a starting point as food for thought, remember how your high school algebra teacher had you use X to predict Y, and even make a graph with a line moving through the dots. Then if you've taken Calculus you remember how you can give that line a neat curve, to be more accurate. Although the graph below uses cars rather than people, it gets the job done in getting the concept across. Check out the line moving through it, otherwise known as the Regression Line:

You probably noticed that the line moving through the data gives you a general idea of where something may be if you know the other variable, and equations for a "best fit" can be calculated. Well, if you have multiple variables on the X-axis you can lessen a lot of how much variation the dots are from that regression line you see.

In the field of Statistics they call this Multiple Regression Analysis. Of course there's a threshold where adding more variables into the equation doesn't do much, and at the same time where taking away variables limits how well it predicts what will happen. They call this threshold the "adjusted coefficient of determination".

Also keep in mind, in Psychology, they already have experimental-control studies where if you have two groups identical except for an independent variable manipulated by the researcher, then that means the independent variable had a cause-effect impact on the dependent variable, depending on how well you controlled for everything else. Although you can't observe mental processes, you can observe observable behavior. Although you can't prove mental processes, you can generally make them falsifiable through observable behavior. For example, you can randomly assign participants to two groups just like in the Medical Field, manipulating one independent variable, and afterward giving each group a questionnaire about attitudes, or see how they behave differently - to see if the independent variable affected it. (For instance taking a picture and then using a computer to change one independent variable while all the other variables the same and then having experimental and control groups rating the attractiveness of pictures on a scale of 1-10). Sometimes they use physiological methods, for example there are instruments which measure pupils and when people see pictures of things they like their pupils dilate, on the other side of the coin when they see pictures of things they don't like their pupils contract. So what about combining the experimental-control methods with the multiple regression equations, plus maybe add our own spin to it?

What are your impressions of using this as a starting point, or do you think I'm looking in the wrong places?