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  3. Statistical Thinking in Python (Part 1)
  • 1

    Graphical Exploratory Data Analysis

    Free

    Before diving into sophisticated statistical inference techniques, you should first explore your data by plotting them and computing simple summary statistics. This process, called exploratory data analysis, is a crucial first step in statistical analysis of data.

  • 2

    Quantitative Exploratory Data Analysis

    In this chapter, you will compute useful summary statistics, which serve to concisely describe salient features of a dataset with a few numbers.

  • 3

    Thinking Probabilistically-- Discrete Variables

    Statistical inference rests upon probability. Because we can very rarely say anything meaningful with absolute certainty from data, we use probabilistic language to make quantitative statements about data. In this chapter, you will learn how to think probabilistically about discrete quantities: those that can only take certain values, like integers.

  • 4

    Thinking Probabilistically-- Continuous Variables

    It’s time to move onto continuous variables, such as those that can take on any fractional value. Many of the principles are the same, but there are some subtleties. At the end of this final chapter, you will be speaking the probabilistic language you need to launch into the inference techniques covered in the sequel to this course.

Initializing

Exercise

Generating random numbers using the np.random module

We will be hammering the np.random module for the rest of this course and its sequel. Actually, you will probably call methods of RNG instances more than any other function while wearing your hacker statistician hat. Let's start by taking a simple function, rng.random() for a test spin. The function returns a random number between zero and one. If you call rng.random() a few times, you should see numbers jumping around between zero and one.

In this exercise, we'll generate lots of random numbers between zero and one, and then plot a histogram of the results. If the numbers are truly random, all bars in the histogram should be of (close to) equal height.

You may have noticed that, in the video, Justin generated 4 random numbers by passing the keyword argument size=4 to rng.random(). Such an approach is more efficient than a for loop: in this exercise, however, you will write a for loop to experience hacker statistics as the practice of repeating an experiment over and over again.

Instructions

100 XP
  • Instantiate and seed a random number generator, rng, using the seed 42.
  • Initialize an empty array, random_numbers, of 100,000 entries to store the random numbers. Make sure you use np.empty(100000) to do this.
  • Write a for loop to draw 100,000 random numbers using rng.random(), storing them in the random_numbers array. To do so, loop over range(100000).
  • Plot a histogram of random_numbers. It is not necessary to label the axes in this case because we are just checking the random number generator. Hit submit to show your plot.