Introduction to probability and random variables. Discrete random variables. Continuous random variables. The probability distribution function. The probability density function. Examples of popular distributions: Bernoulli, binomial, Poisson, geometric, normal, etc. Conditional probability. Joint distributions. Statistics of random variables. The central limit theorem. Analyzing measurements using statistical techniques. Measures of central tendency (mean, median, mode). Measures of variation (range, interquartile, variance, standard deviation, coefficient of variation, Chebyshev’s rule and empirical rule). Measures of position (Z-score, percentiles and outliers). Graphical data analysis, frequency distributions, standard error, goodness of fit. Linear regression. Confidence intervals and sample size. Counting methods, combinations and permutations. Statistical inference about one and two population parameters. Hypothesis testing. Random processes. Ergodicity and stationarity.