Quantile-quantile plots are a useful tool for determining whether a measure is normally distributed. From Wikipedia:
Q–Q (quantile-quantile) plot is a probability plot, which is a graphical method for comparing two probability distributions by plotting their quantiles against each other. First, the set of intervals for the quantiles is chosen. A point (x, y) on the plot corresponds to one of the quantiles of the second distribution (y-coordinate) plotted against the same quantile of the first distribution (x-coordinate).

To explore how R generates random numbers, we will use the rnorm function. This function draws a random number from a normal distribution with a mean = 0 and standard deviation = 1 (though these can be changed with the mean and sd parameters). With n = 1 we will get one random number.
rnorm(n = 1) ## [1] 0.463405 rnorm(n = 1) ## [1] 1.87497 Each time you run the command you will get a different number.

To get started with the labs, you will need to install R and other supporting applications. Links to download all the software is on the Software page which is also available on the Course Overview menu. Once you are in RStudio, you will need to install a number of R packages using the following two commands:

Welcome to DATA606! My name is Dr. Jason Bryer and I will be your instructor for this semester. I am an Assistant Professor in the Data Science and Information Systems department at CUNY SPS. Couple of important notes as you get started:
The course syllabus located here: https://spring2021.data606.net/course-overview I will post all course materials there. Blackboard will be used primarily for submitting assignments. Please read the syllabus carefully!