I know that the P value is high, so given that, what is my next step? Does it just mean my variables are not significant and I should choose another set of variables? Is there any type of "troubleshooting" I can do?

also, I do not know how to interperpret the rest of the results, so any comments would be appreciated.

thanks!

Call:

glm(formula = Y~ ., family = binomial(link = "logit"), data = data)

Deviance Residuals:

Min 1Q Median 3Q Max

-1.4045 -0.6166 -0.4228 -0.1674 2.0933

Coefficients:

Estimate Std. Error z value Pr(>|z|)

(Int) -2.40466 1.37109 -1.754 0.0795 .

v1 0.10440 0.48008 0.217 0.8279

v2 -0.31214 0.55934 -0.558 0.5768

v3 0.04656 0.18316 0.254 0.7993

v4 0.48907 0.23262 2.102 0.0355 *

v5 -0.82624 0.74024 -1.116 0.2643

v6 0.20443 0.18118 1.128 0.2592

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for binomial family taken to be 1)

Null deviance: 50.482 on 50 degrees of freedom

Residual deviance: 40.822 on 44 degrees of freedom

AIC: 54.822

Number of Fisher Scoring iterations: 5 ]]>

My question is how do I calculate weights for Y=W1X1*W2X2*WNXN where N is the number of independent variables.

I could use logarithms to convert this to an additive model, but each of the independent variables are binary/dummy variables. Here is some background.

Assume two independent variables (more in practice). Really this is two sets of dummy variables. The predictive airline market share model looks something like this:

ShareIndex=

[W(ns)D(ns)+W(1s)D(1s)+W(2s)D(2s)] *

[W(wb)D(wb)+W(nb)D(nb)+W(rj)D(rj)+W(tp)D(tp)]

W for weights, D for binary dummy variables

ITINERARY CONNECTIVITY (ns=nonstop, 1s=connection, 2s=doubleconnection)

AIRCRAFT TYPE (wb=widebody, nb=narrowbody, rj=regionaljet, tp=turboprop)

For each record, the there is one AIRCRAFTTYPE dummy variable with a value of 1; all others 0.

For each record, the there is one ITINERARY dummy variable with a value of 1; all others 0.

So basically the model is ShareIndex=W(aircrafttype)*W(itinerarytype)

The dependent variable in the actual data is SHARE (market share). However, the model might produce a ShareIndex where each record's SHAREINDEX / (sum SHAREINDEXES all records that market)=market share

For example if a market has 10 records each with 10% market share. MS(record1)=W(AircraftType)*W(ItineraryType)/

How do I calculate the coefficients/weights?

Thanks!!! ]]>