# Thread: Logistic regression : negative coefficients, categorical variables

1. ## Logistic regression : negative coefficients, categorical variables

Hi
I am trying to build attrition model and have these variables :- 10 categorical variables with sub levels which created into dummy variables and 4 continuous variables in a trained data set. My Y dependent variable is binary variable. After running log regress, the results are some of the dummy variables are found to have collinearity error and some of them are insignificant. Tried running penalized log reg, same results obtained. After omitting the dummy variables, I run a new log regress, there are negative coefficients in the 3 continuous variables which are found significant. But, they are in a very small numbers e.g. -0.003,-0.005 etc which did not make senses. Now I am in a fix.

Did i go wrong somewhere as all the categorical variables are not significant?
Or I should not be using logistic regress?

2. ## Re: Logistic regression : negative coefficients, categorical variables

Do you have a huge dataset?

3. ## Re: Logistic regression : negative coefficients, categorical variables

In total I have 107 rows. Not big actually.

4. ## Re: Logistic regression : negative coefficients, categorical variables

hi,
without knowing the details, you could look ar two aspects: the odds ratio will depend on the exponential of the coefficient, and it will give you the effect of increasing the continuous variable by 1 unit. If the unit is small, the odds ratio will likely be very close to 1, and you would need to calculate the odds ratio for some practically significant change in the continuous variable.

E.g. odds of heart attack vs. weight. If you measure the weight in grams, the odds will not greately change by a weight change of 1 gram, though they may change a lot by an increase of say, 5 kg.

regards

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gianmarco (04-22-2017)

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