re: basic question on why my predictor is not significant?

#1
I am puzzle by why my predictor was not significant so I would like to seek your expertise. My regression model does not provide significant on the predictor value. Would you suggest that because I have too many levels on my predictor value? I have 500 levels on my predictor values meaning predictor contains code of "A1" through "A500". My model just have two variables. One outcome variable naming y, and one predictor value naming x. Outcome variable is alternate between 0 and 1, and the predictor value is contain all values of A1 through A500. Furthermore , I have 1000 observations in my data. My question is that why my predictor variable is not significant when I ran the glm analysis? My guess is that because my predictor contains so many levels meaning 500 levels. Is this the problem why it would not be significant? Please help, and also if I try to reduce the number of levels of my predictor, then would it be significant? Thanks for all of your help in advance.

regression
 

hlsmith

Omega Contributor
#2
What program are you using? Are you getting any warning messages? Is it running at all? Can you run the model with fewer levels to see if it will run? Can the levels be turned into a continous values? Lots of questions, but 500 levels seems like quite a few.
 
#3
Hello, I am using R, and none of my predictor value was significant. No, I can not turn the predictor level to continous.
Here is my sample output:
> summary (reg)

Call:
glm(formula = x ~ y, family = binomial("logit"), data = df)

Deviance Residuals:
Min 1Q Median 3Q Max
-1.66511 -0.75853 -0.00013 0.86325 1.66511

Coefficients:
Estimate Std. Error z value Pr(>|z|)
(Intercept) -1.857e+01 3.261e+03 -0.006 0.995
yA100 1.747e+01 3.261e+03 0.005 0.996
yA101 1.857e+01 3.261e+03 0.006 0.995
yA102 1.747e+01 3.261e+03 0.005 0.996
yA103 1.857e+01 3.261e+03 0.006 0.995
yA104 1.747e+01 3.261e+03 0.005 0.996
yA105 1.966e+01 3.261e+03 0.006 0.995
yA106 2.271e-07 4.612e+03 0.000 1.000
yA107 1.857e+01 3.261e+03 0.006 0.995
yA108 1.747e+01 3.261e+03 0.005 0.996
yA109 1.857e+01 3.261e+03 0.006 0.995
yA11 1.857e+01 3.261e+03 0.006 0.995
yA110 1.747e+01 3.261e+03 0.005 0.996
yA111 1.747e+01 3.261e+03 0.005 0.996
yA112 1.747e+01 3.261e+03 0.005 0.996
yA113 1.857e+01 3.261e+03 0.006 0.995
yA114 1.747e+01 3.261e+03 0.005 0.996
yA115 1.747e+01 3.261e+03 0.005 0.996
yA116 1.966e+01 3.261e+03 0.006 0.995
yA117 2.267e-07 4.612e+03 0.000 1.000
yA118 1.747e+01 3.261e+03 0.005 0.996
yA119 2.273e-07 4.612e+03 0.000 1.000
yA12 1.857e+01 3.261e+03 0.006 0.995
yA120 1.966e+01 3.261e+03 0.006 0.995
yA121 2.273e-07 4.612e+03 0.000 1.000
yA122 1.747e+01 3.261e+03 0.005 0.996
yA123 1.747e+01 3.261e+03 0.005 0.996
yA124 1.747e+01 3.261e+03 0.005 0.996
yA125 2.262e-07 4.612e+03 0.000 1.000
yA126 1.966e+01 3.261e+03 0.006 0.995
yA127 1.747e+01 3.261e+03 0.005 0.996
yA128 2.280e-07 4.612e+03 0.000 1.000
yA129 1.857e+01 3.261e+03 0.006 0.995
yA13 1.857e+01 3.261e+03 0.006 0.995
yA130 1.857e+01 3.261e+03 0.006 0.995
yA131 1.747e+01 3.261e+03 0.005 0.996
yA132 2.267e-07 4.612e+03 0.000 1.000
yA133 1.966e+01 3.261e+03 0.006 0.995
yA134 2.265e-07 4.612e+03 0.000 1.000
yA135 1.747e+01 3.261e+03 0.005 0.996
yA136 1.857e+01 3.261e+03 0.006 0.995
yA137 1.747e+01 3.261e+03 0.005 0.996
yA138 2.267e-07 4.612e+03 0.000 1.000
yA139 1.857e+01 3.261e+03 0.006 0.995
yA14 2.277e-07 4.612e+03 0.000 1.000
yA140 2.273e-07 4.612e+03 0.000 1.000
yA141 1.747e+01 3.261e+03 0.005 0.996
yA142 1.747e+01 3.261e+03 0.005 0.996
yA143 2.252e-07 4.612e+03 0.000 1.000
yA144 1.747e+01 3.261e+03 0.005 0.996
yA145 1.747e+01 3.261e+03 0.005 0.996
yA146 2.262e-07 4.612e+03 0.000 1.000
yA147 1.747e+01 3.261e+03 0.005 0.996
yA148 2.267e-07 4.612e+03 0.000 1.000
yA149 1.747e+01 3.261e+03 0.005 0.996
yA15 1.857e+01 3.261e+03 0.006 0.995
yA150 1.857e+01 3.261e+03 0.006 0.995
yA151 1.857e+01 3.261e+03 0.006 0.995
yA152 1.747e+01 3.261e+03 0.005 0.996
yA153 1.747e+01 3.261e+03 0.005 0.996
yA154 1.857e+01 3.261e+03 0.006 0.995
yA155 1.747e+01 3.261e+03 0.005 0.996
yA156 2.255e-07 4.612e+03 0.000 1.000
yA157 1.857e+01 3.261e+03 0.006 0.995
yA158 1.747e+01 3.261e+03 0.005 0.996
yA159 2.262e-07 4.612e+03 0.000 1.000
yA16 2.273e-07 4.612e+03 0.000 1.000
yA160 2.267e-07 4.612e+03 0.000 1.000
yA161 1.747e+01 3.261e+03 0.005 0.996
yA162 2.272e-07 4.612e+03 0.000 1.000
yA163 1.857e+01 3.261e+03 0.006 0.995
yA164 1.747e+01 3.261e+03 0.005 0.996
yA165 2.272e-07 4.612e+03 0.000 1.000
 

kiton

New Member
#4
First of all, as far as I understood, your DV (y) is binary, that is has values of either 0 or 1. In this case the proper type of analysis would be logistic regression. Secondly, could you please post the descriptive statistics for your IV (mean, SD, min, max).