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My dependant variable is diagnosis of cancer malignant being 0 and benign being 1. And my covariate is mean radius (of the tumour). I get this: 

Variables in the Equation: 

                     B       S.E.   Wald    df   Sig.   Exp(B)  95% C.I.for EXP(B)  
                                                                Lower   Upper
Step 1a radius    1.020      .104   96.518  1   .000    2.774   2.263   3.401
        Constant -15.039    1.474   104.091 1   .000    .000        

a Variable(s) entered on step 1: radius.

How do I interpret this in words?

gung - Reinstate Monica
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Megan
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    Welcome to the site, @Megan. Can you say more about what exactly you need help with? Is there a specific thing, or do you need a general familiarity with the concepts surrounding logistic regression? If the latter, it may help you to read my answers here: [interpretation of simple predictions to odds ratios in logistic regression](http://stats.stackexchange.com/questions/34636//34638#34638), & here: [difference-between-logit-and-probit-models](http://stats.stackexchange.com/questions/20523//30909#30909). – gung - Reinstate Monica Mar 24 '13 at 21:35
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    Which parts do you need help with? The meaning of the `Step 1a radius` coefficient? What a Wald test is? What a 95% CI is? Logistic regression in general? One could easily fill a book with everything there is to understand on interpreting that output. Could you narrow the scope a little? – Glen_b Mar 25 '13 at 03:32

1 Answers1

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The interpretation of the output is given below:

B - This is the coefficient for the constant (also called the "intercept") in the model.

S.E. - This is the standard error around the coefficient for the constant

Wald and Sig. - This is the Wald chi-square test that tests the null hypothesis that the constant equals 0. This hypothesis is rejected because the p-value (listed in the column called "Sig.") is smaller than the critical p-value of .05 (or .01). Hence, we conclude that the constant is not 0.

df - This is the degrees of freedom for the Wald chi-square test. There is only one degree of freedom because there is only one predictor in the model, namely the constant.

Exp(B) - This is the exponentiation of the B coefficient, which is an odds ratio. This value is given by default because odds ratios can be easier to interpret than the coefficient, which is in log-odds units.

95% C.I.for EXP(B): This is confidence interval (95%) for Exp(B), it can be anywhere between 2.263 and 3.401

Hope this helps !

Learner
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