The desired code to understand and learn concepts Question 1: What is the purpose of np.random.seed() function?

The np.random.seed() function sets the random seed for NumPy's random number generator. This ensures that the same sequence of random numbers is generated every time the code is run, which is useful for repeatability and testing.

Question 2: What is the difference between np.random.rand() and np.random.randn()?

: np.random.rand() generates random numbers from a uniform distribution between 0 and 1, while np.random.randn() generates random numbers from a normal distribution with mean 0 and standard deviation 1.

Question 3: What is the purpose of sns.heatmap() function?

The sns.heatmap() function creates a heatmap visualization of a two-dimensional array or matrix, where the color of each cell represents the value of the corresponding element in the array.

Question 4: What is the purpose of axs[1].scatter() function?

The axs[1].scatter() function creates a scatter plot of random points in 2D space, where each point is represented by a marker in the plot.

Question 5: What is the purpose of np.polyfit() function?

The np.polyfit() function fits a polynomial curve to a set of data points, in this case, a linear regression line to random points.

Question 6: What is the purpose of axs[1].plot() function?

The axs[1].plot() function plots the regression line on the scatter plot.

Question 7: What is the purpose of np.mean(), np.std() and np.corrcoef() functions?

These functions calculate the mean, standard deviation and correlation coefficient of random points respectively.

Question 8: What is the purpose of the print() function?

The print() function outputs the calculated statistics to the console.

Overall, this code shows how to generate random data, calculate statistics, and visualize data using Python's NumPy, Pandas, and Matplotlib libraries. It also provides a model for understanding and learning.