So I am working on this regression and I am doing it for multiple levels of Females. I have done one regression for Females 18-34 and got this output
F18<-read.csv("C:/Users/marissa.ferguson/Desktop/Unrated/F18-34.csv", header = T, sep = ",", na.strings = "?")
female<-na.omit(F18)
set.seed(1000)
train.size<-0.8
train.index<- sample.int(length(subfemale$DemoMedianRtg), round(length(subfemale$DemoMedianRtg)*train.size))
train.sample<-subfemale[train.index,]
test.sample<-subfemale[-train.index,]
> Overall<-lm(DemoMedianRtg~ DP+Subscribers+Tier+SubRange+Male.+Female.+Avg.Age+NewTier+NewSubs+Avg.Income, data=train.sample)
> summary(Overall)
Call:
lm(formula = DemoMedianRtg ~ DP + Subscribers + Tier + SubRange +
Male. + Female. + Avg.Age + NewTier + NewSubs + Avg.Income,
data = train.sample)
Residuals:
Min 1Q Median 3Q Max
-0.038269 -0.014386 0.003568 0.012190 0.029538
Coefficients: (8 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) 2.258e+00 5.495e-01 4.108 0.000375 ***
DPEarly Fringe 8.178e-03 1.129e-02 0.724 0.475665
DPEarly Morning 1.012e-02 1.739e-02 0.582 0.566011
DPLate Fringe 7.304e-02 1.643e-02 4.445 0.000157 ***
DPOvernight 3.327e-02 1.968e-02 1.691 0.103281
DPPrimeTime 1.999e-02 1.188e-02 1.682 0.105005
DPWeekend 3.971e-02 1.054e-02 3.769 0.000895 ***
Subscribers -2.142e-05 5.280e-06 -4.057 0.000428 ***
TierTier2 -9.673e-01 2.090e-01 -4.627 9.79e-05 ***
TierTier3 -1.341e+00 3.010e-01 -4.457 0.000152 ***
SubRange40-50K NA NA NA NA
SubRange60-70K NA NA NA NA
SubRange80-90K -3.722e-01 9.075e-02 -4.101 0.000382 ***
SubRange90-100K -1.958e-01 3.001e-02 -6.524 7.82e-07 ***
Male. 9.721e-02 7.822e-02 1.243 0.225490
Female. NA NA NA NA
Avg.Age NA NA NA NA
NewTierTier2 NA NA NA NA
NewTierTier3 NA NA NA NA
NewSubs NA NA NA NA
Avg.Income NA NA NA NA
As you can see Female. is one level. When I do it for another level of females I get this output
F2554<-read.csv("C:/Users/marissa.ferguson/Desktop/Unrated/F25-54.csv", header = T, sep = ",", na.strings = "?")
female<-na.omit(F2554)
set.seed(1000)
train.size<-0.8
train.index<- sample.int(length(subfemale$DemoMedianRtg), round(length(subfemale$DemoMedianRtg)*train.size))
train.sample<-subfemale[train.index,]
test.sample<-subfemale[-train.index,]
Overall<-lm(DemoMedianRtg~ Qtr+DP+Subscribers+Tier+SubRange+Male.+Female.+Avg.Age+NewTier+NewSubs+Avg.Income, data=train.sample)
Coefficients: (39 not defined because of singularities)
Estimate Std. Error t value Pr(>|t|)
(Intercept) -2.022e+00 7.314e-01 -2.765 0.005911 **
Qtr2015 Q4 3.252e-02 1.602e-02 2.030 0.042898 *
Qtr2016 Q1 1.529e-02 1.721e-02 0.888 0.374835
Qtr2016 Q2 1.265e-02 2.170e-02 0.583 0.560224
DPEarly Fringe 1.724e-02 1.842e-02 0.936 0.349910
DPEarly Morning -2.050e-03 3.006e-02 -0.068 0.945656
DPLate Fringe 4.026e-02 2.070e-02 1.946 0.052270 .
DPOvernight -2.201e-02 2.603e-02 -0.846 0.398193
DPPrimeTime 8.661e-02 1.909e-02 4.538 7.14e-06 ***
DPWeekend 4.809e-02 2.040e-02 2.358 0.018760 *
Subscribers 1.638e-05 6.596e-06 2.483 0.013360 *
TierTier2 -1.314e-01 1.155e-01 -1.138 0.255800
TierTier3 6.884e-01 3.691e-01 1.865 0.062803 .
SubRange110-120K -3.832e-01 1.126e-01 -3.402 0.000722 ***
SubRange30-40K 8.673e-01 2.440e-01 3.555 0.000414 ***
SubRange40-50K 1.229e-01 8.418e-02 1.460 0.144792
SubRange50-60K NA NA NA NA
SubRange60-70K 7.460e-01 2.604e-01 2.865 0.004347 **
SubRange70-80K -9.732e-02 1.917e-01 -0.508 0.611830
SubRange80-90K 2.315e-01 1.211e-01 1.912 0.056398 .
SubRange90-100K 8.464e-02 6.387e-02 1.325 0.185729
Male.0.24 -2.775e-01 6.075e-02 -4.567 6.25e-06 ***
Male.0.25 -5.249e-02 4.436e-02 -1.183 0.237277
Male.0.29 -7.252e-02 6.412e-02 -1.131 0.258537
Male.0.31 1.320e-01 5.029e-02 2.624 0.008956 **
Male.0.33 7.011e-02 1.235e-01 0.568 0.570611
Male.0.34 -1.765e-01 6.584e-02 -2.682 0.007572 **
Male.0.35 1.708e-02 7.568e-02 0.226 0.821518
Male.0.36 -9.714e-02 9.060e-02 -1.072 0.284170
Male.0.37 -2.328e-01 8.924e-02 -2.609 0.009369 **
Male.0.39 NA NA NA NA
Male.0.4 -9.214e-02 8.566e-02 -1.076 0.282642
Male.0.41 -1.629e-01 6.081e-02 -2.679 0.007631 **
Male.0.42 -2.339e-01 5.697e-02 -4.106 4.71e-05 ***
Male.0.44 -2.548e-01 6.245e-02 -4.079 5.26e-05 ***
Male.0.45 -9.951e-02 8.220e-02 -1.211 0.226632
Male.0.46 -7.510e-02 1.079e-01 -0.696 0.486864
Male.0.47 -4.412e-02 5.739e-02 -0.769 0.442357
Male.0.48 -2.330e-01 6.124e-02 -3.805 0.000159 ***
Male.0.5 -2.787e-01 7.532e-02 -3.700 0.000240 ***
Male.0.51 -2.241e-01 6.402e-02 -3.501 0.000506 ***
Male.0.52 -1.690e-01 5.705e-02 -2.962 0.003203 **
Male.0.53 -1.254e-01 8.424e-02 -1.489 0.137125
Male.0.55 -2.398e-01 5.648e-02 -4.246 2.60e-05 ***
Male.0.56 -3.289e-01 9.453e-02 -3.479 0.000548 ***
Male.0.57 -1.017e-01 8.750e-02 -1.162 0.245633
Male.0.58 -3.471e-01 8.574e-02 -4.048 6.00e-05 ***
Male.0.59 -3.855e-01 1.067e-01 -3.612 0.000335 ***
Male.0.6 6.813e-01 1.378e-01 4.943 1.06e-06 ***
Male.0.62 -2.782e-01 7.264e-02 -3.830 0.000144 ***
Male.0.63 4.929e-01 1.042e-01 4.730 2.94e-06 ***
Male.0.64 NA NA NA NA
Male.0.65 4.818e-01 8.043e-02 5.990 4.04e-09 ***
Male.0.66 -4.060e-01 1.459e-01 -2.783 0.005591 **
Male.0.71 -2.394e-03 1.239e-01 -0.019 0.984588
Male.0.76 -1.261e-01 9.176e-02 -1.374 0.169912
Male.n/a -1.728e-01 6.243e-02 -2.768 0.005846 **
Female.0.29 NA NA NA NA
Female.0.34 NA NA NA NA
Female.0.35 NA NA NA NA
Female.0.36 NA NA NA NA
Female.0.37 NA NA NA NA
Female.0.38 NA NA NA NA
Female.0.4 NA NA NA NA
Female.0.41 NA NA NA NA
Female.0.42 NA NA NA NA
Female.0.43 NA NA NA NA
Female.0.44 NA NA NA NA
Female.0.45 NA NA NA NA
Female.0.47 NA NA NA NA
Female.0.48 NA NA NA NA
Female.0.49 NA NA NA NA
Female.0.5 NA NA NA NA
Female.0.52 NA NA NA NA
Female.0.53 NA NA NA NA
Female.0.54 NA NA NA NA
Female.0.55 NA NA NA NA
Female.0.56 NA NA NA NA
Female.0.58 NA NA NA NA
Female.0.59 NA NA NA NA
Female.0.6 NA NA NA NA
Female.0.61 NA NA NA NA
Female.0.63 NA NA NA NA
Female.0.65 NA NA NA NA
Female.0.66 NA NA NA NA
Female.0.67 NA NA NA NA
Female.0.69 NA NA NA NA
Female.0.71 NA NA NA NA
Female.0.75 NA NA NA NA
Female.0.76 NA NA NA NA
Female.0.77 NA NA NA NA
Female.n/a NA NA NA NA
Avg.Age -3.740e-03 1.244e-03 -3.006 0.002785 **
NewTierTier2 2.158e-01 1.048e-01 2.059 0.040012 *
NewTierTier3 4.357e-01 1.294e-01 3.367 0.000819 ***
NewTierTier4 NA NA NA NA
NewSubs 1.056e-05 2.873e-06 3.677 0.000262 ***
Avg.Income -3.272e-06 1.437e-06 -2.277 0.023193 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
I am confused on why Female. and Male. are both different when I have copied and pasted the code and only changed the csv I am using for them. Any help would be appreciated. Is there a way to keep it at one variable? Or are the multiple levels necessary?