# Thread: What Kind of Regression Do I Need for This?

1. ## What Kind of Regression Do I Need for This?

I have a data like this. I need to find 7 predictors' coefficients for the best model to predict dependent. It can only be 1 and 0.

Code:
``````A     B     C     D      E      F     G     Dependent
1000  20    -4    150    -567  -83    10    1
-400  35    3     78     341    45    -9    0``````

When "c" stands for coefficient;

Dependent is correctly predicted 1 if cA+cB+cC+cD+cE+cF+cG is positive regardless of the actual number.

Dependent is correctly predicted 0 if cA+cB+cC+cD+cE+cF+cG is negative regardless of the actual number.

I need to get the optimum coefficients for the best model at predicting dependent.

2. ## Re: What Kind of Regression Do I Need for This?

Considering your response is binary, you shall be looking at a logistic regression.

3. ## Re: What Kind of Regression Do I Need for This?

I only worked with linear regressions whether it's penalized or not, weighted or not.

When it comes to logistic regression, I'm completely clueless. What I'm really struggling with is how can I make the regression only care about if cA+cB+cC+cD+cE+cF+cG is positive or not regardless of the value. Response should be 1 for positive 0 for negative. Thus, it should give the optimum coefficients.

4. ## Re: What Kind of Regression Do I Need for This?

So are you saying you're against learning Logistic regression? Because it's much more appropriate when your response is binary.

5. ## Re: What Kind of Regression Do I Need for This?

Originally Posted by Dason
So are you saying you're against learning Logistic regression? Because it's much more appropriate when your response is binary.
Nope. I'm completely open to learning. I just couldn't find how to make it work for my case. If you show me one example that's resembling what I'm trying to achieve, I can get it. For example would you be able to calculate the coefficients for A B C D E F G which I gave in the OP?

6. ## Re: What Kind of Regression Do I Need for This?

I believe I couldn't explain myself clear enough. To make it simpler;

1. I have 7 metrics that predict the outcome of basketball games (Win or Lose)
2. If a given metric's value is positive and the response is 1 (Win) or if that metric's value is negative and the response is 0 (Loss), that metric succeeded at predicting the outcome. Otherwise it did not. Metric values are not important as long as they predict the outcome accurately.
3. I want to make a blend of those 7 metrics that's best at predicting the outcome.

When I worked with score differential, I easily calculated it via any type of linear regression. However, I'm struggling here because the response is dependent on if the metric's value is >0 or <0 and not the value itself.

7. ## Re: What Kind of Regression Do I Need for This?

Well, I'm ashamed to admit I don't really know how it works but simple logistic regression without doing anything else achieved what I wanted to achieve. In R I simply used;

fit <- glm(Dependent~A+B+C+D+E+F+G,data=mydata,family=binomial())
summary(fit)

to get coefficients and the blend (with intercept) eventually improved prediction by 3.2% percent compared to the best metric. Not much but it pretty much confirms it did what I wanted it to did.

Edit: Still I don't understand how the regression figured out the response was dependent on if the independent variables' value are bigger than 0 or not and wasn't related to the actual value itself.

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