1. ## Regression

I wonder if you can help me with the following. I am not a statistics student but need to interpret some tables. I asked for some help a few days ago but as I did not get a reply I got some books and I am trying to understand what is going on...

The table below is supposed to be a regression model showing what happens in a choice between males and females (gender male=1; female = 0) and (ice = 1; no ice = 0)

From what I can read when the choice is ice there is a negative coefficient implying that females prefer ice (cream) just slightly more than males.

Then, and this is the difficult bit, the p-value being -1.39 says that this is not a significant statistic. But that goes against earlier tables that agree with these results.

Can yo help me? Am I totally off the mark?

Thank you

qim

. logit con gender

Iteration 0: log likelihood = -1595.2709
Iteration 1: log likelihood = -1594.3081
Iteration 2: log likelihood = -1594.3079

Logistic regression Number of obs = 2756
LR chi2(1) = 1.93
Prob > chi2 = 0.1652
Log likelihood = -1594.3079 Pseudo R2 = 0.0006

ice Coef. Std. Err. z P>z [95% Conf. Interval]

gender -.1204865 .0869281 -1.39 0.166 -.2908625 .0498895
_cons -.9632477 .0576305 -16.71 0.000 -1.076201 -.850294

2. Then, and this is the difficult bit, the p-value being -1.39 says that this is not a significant statistic. But that goes against earlier tables that agree with these results.

Can yo help me? Am I totally off the mark?
the p-value is a probability, and as such must lie between 0 and 1. so it can't be negative, and even if it were, its absolute value couldn't be greater than 1.

the p-value associated with the gender variable looks to me to be .166, so it still looks as if the gender variable is not particularly significant in this model.

3. Thank you jahred

Of course, the p-value is 0.166...

Thanks

4. iirc the LR chi^2(1) value refers to the test statistic -2 log L, where L is the appropriate likelihood ratio (LR) for a likelihood ratio test.

for a 2x2 contingency table (i.e. male/female ice/no ice), -2 log L has a chi-square distribution with one degree of freedom, hence the chi^2(1).

i believe here it's used for the test

Ho: gender & ice cream preference are not associated
Ha: gender & ice cream preference are associated

so it is equivalent to testing the coefficient for gender in your logistic regression.

notice that the p-values are the same; P(-2 log L > 1.93) = 0.166. so for the likelihood ratio test above, there is insufficient evidence to reject Ho.

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