Refer to the images in the slide "Quantile-Quantile (QQ) Plots (3)" which show data drawn from a student's T distribution (for n = 10,500,100, 1000,5000,10000) compared to the standard normal \, \mathcal{N}(0,1).
Upon examining the QQ plots in that slides, would you conclude that the tails (both left and right) of the student's T distribution are heavier or lighter than those of the standard normal distribution?
Based on the QQ plots in the slide, it can be concluded that the tails of the student's T distribution are heavier or fatter than those of the standard normal distribution. This is seen by the student's T distribution curves deviating from the straight line representing the standard normal distribution in the tails, indicating that there is a higher frequency of extreme values in the student's T distribution compared to the standard normal distribution.
To determine if the tails of the student's T distribution are heavier or lighter than those of the standard normal distribution, we need to analyze the QQ plots in the slide "Quantile-Quantile (QQ) Plots (3)".
QQ plots are used to compare the quantiles of two different datasets. In this case, the student's T distribution is being compared to the standard normal distribution.
If the student's T distribution has heavier tails than the standard normal distribution, we would expect to see points in the QQ plot that deviate from the straight line in the tails of the plot. These points would indicate that the tails of the student's T distribution have more extreme values compared to the standard normal distribution.
Conversely, if the student's T distribution has lighter tails than the standard normal distribution, the QQ plot would show points that deviate from the straight line in the middle of the plot, indicating that the tails of the student's T distribution have fewer extreme values compared to the standard normal distribution.
So, to conclude whether the tails of the student's T distribution are heavier or lighter than those of the standard normal distribution, refer to the QQ plots in the slide and look for the pattern of deviating points in the tails of the plot.