Tim,
NaN looks like a numerical error. I'm curious, how may levels does the
variable have and how many dummy variables are you using?
If the original variable has K levels, you should have K-1 dummy
variables. For example, if your variable were location (1=rural,
2=suburban, 3=urban) then you would pick one level to be the reference
and create two dummy variables, perhaps:
recode location (1=1) (else=0) into dum1.
recode location (2=1) (else=0) into dum2.
Then the coefficients of dum1 and dum2 tell you how living in a rural
(dum1) or suburban (dum2) area compares to living in an urban area.
The model won't be defined if you use K variables for K levels.
I notice that both of the zeros are for xxx_1 variables, so that
suggested possibly not coding the categorical variable correctly. But I
don't know if that's what you are seeing. You could also get zeros if
there were no instances of that dummy code, but you shouldn't see NaN
values. It could also be another problem, or a bug. In fact, I think
it's probably a bug to see NaN's...
-Alan
On 12/20/23 3:46 AM, tim.goodsp...@btinternet.com wrote:
A basic stat’s question and a specific PSPP query, please. Any help
gratefully received. I can’t see this in the archives anywhere
(searching for ‘categorical’ and ‘dummy’).
For a linear regression, some variables are categorical and so
included using dummy coding (Coding Systems for Categorical Variables
in Regression Analysis (ucla.edu)
<https://stats.oarc.ucla.edu/spss/faq/coding-systems-for-categorical-variables-in-regression-analysis-2/#:~:text=Categorical%20variables%20require%20special%20attention,entered%20into%20the%20regression%20model.>).
*basic stat’s question: *This results in a zero coefficient and zero
standard error for some variables, as shown in the example below. Is
this correct? There is little or no linear relationship to be found?
*specific PSPP query: *if there is little relationship/the coefficient
is very small, is there a way to tell PSPP to show the very small
value instead of zero?**
Thanks in advance
Table: Model Summary (adjRA1SR1)
R
R Square
Adjusted R Square
Std. Error of the Estimate
0.55723
0.310505
0.302797
0.8359
Table: ANOVA (adjRA1SR1)
Sum of Squares
df
Mean Square
F
Sig.
Regression
619.25791
22
28.148087
40.284698
0
Residual
1375.0987
1968
0.698729
Total
1994.3566
1990
Table: Coefficients (adjRA1SR1)
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
95% Confidence Interval for B
B
Std. Error
Beta
Lower Bound
Upper Bound
(Constant)
8.163407
0.310014
0
26.332394
0
7.555417
8.771397
lnSTINC
-0.036745
0.011677
-0.088107
-3.146888
0.002
-0.059645
-0.013845
RA1PKHSIZ
-0.011834
0.016218
-0.020561
-0.729708
0.466
-0.043639
0.019971
RA1PRAGE
-0.039326
0.011175
-0.550388
-3.519082
0
-0.061242
-0.01741
sqPRAGE
0.000464
0.000109
0.666977
4.258349
0
0.00025
0.000678
RA1PRSEX
0.13709
0.03935
0.068446
3.483888
0.001
0.059918
0.214261
RA1PB19_1
0
0
0
NaN
NaN
0
0
RA1PB19_2
-0.485628
0.170694
-0.054029
-2.845015
0.004
-0.820389
-0.150867
RA1PB19_3
-0.324574
0.058981
-0.109094
-5.503011
0
-0.440246
-0.208902
RA1PB19_4
-0.333625
0.089807
-0.074169
-3.714896
0
-0.509752
-0.157497
RA1PB1
-0.002888
0.008407
-0.007002
-0.343559
0.731
-0.019376
0.0136
RA1SG17A_1
0
0
0
NaN
NaN
0
0
RA1SG17A_2
-0.061221
0.053837
-0.021822
-1.137147
0.256
-0.166804
0.044363
RA1PA1
-0.15082
0.022182
-0.160102
-6.7991
0
-0.194324
-0.107317
RA1PA2
-0.248882
0.024367
-0.243609
-10.214077
0
-0.29667
-0.201095
RA1SC1
-0.328042
0.073134
-0.08782
-4.485512
0
-0.471469
-0.184614
RA1PF3bin
0.003064
0.041159
0.001422
0.074435
0.941
-0.077655
0.083783
RA1PF7A_2
0.009538
0.086914
0.002111
0.109735
0.913
-0.160917
0.179992
RA1PF7A_3
0.14177
0.166844
0.016081
0.849712
0.396
-0.18544
0.468979
RA1PF7A_4
-0.104009
0.155971
-0.01266
-0.666848
0.505
-0.409894
0.201877
RA1PF7A_5
0.173309
0.59246
0.005486
0.292525
0.77
-0.988606
1.335224
RA1PF7A_6
0.064264
0.080864
0.01504
0.794712
0.427
-0.094325
0.222853
RA1PG2
-0.350528
0.030049
-0.233421
-11.66509
0
-0.40946
-0.291597
--
Alan D. Mead, Ph.D.
President, Talent Algorithms Inc.
science + technology = better workers
https://talalg.com
Linus' Law: Given enough eyeballs, all bugs are shallow.
