SUMMARY

This chapter started with a brief description regarding the need for statistics, followed by some well-known terminology, and then the concept of a random variable, which can be either continuous or discrete.

Then you learned about mean, median, mode, variance, and standard deviation, along with Chebyshev’s inequality, dispersion, and sampling techniques. Next, you saw how to detect normally distributed data, such as D’Agostino’s test, Pearson’s correlation coefficient, and augmented Dickey-Fuller.

You also learned about several continuous probability distributions, such as chi-square, Gaussian, and uniform distribution, followed by some discrete probability distributions, such as Bernoulli, binomial, exponential, and Poisson distribution.

In addition, you saw how to compute metrics for linear regression, including MAE, MSE, RMSE, and MMR, followed by concepts such as the CLT, causality and the contrapositive, and statistical inferences.

In the final...