You might remember that at the beginning of the chapter, we noticed in the stacked bar plot that in our sample of 1,000 roulette spins, the zero was drawn about twice as often as we would expect. We just mentioned it but didn't really have a point of comparison. We now have proportions from 100 samples and thus can examine this a little further. The proportion of zeros can be obtained from the data we have as we simply have to subtract from 1, the sum of proportions of red and black numbers for each of the samples. So let's do this, and add the attribute to the data frame, and get the mean value of this proportion:
samples$isZero = 1-(samples$isRed+samples$isBlack) Mean = mean(samples$isZero) Mean
The mean value is 0.0277. We can compute the value we would expect is 1/37, which is 0.0270. The average value of the proportion of zeros in all our 100 samples is therefore almost identical to the expected value. This in no way means that there are no outliers.
There...
