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3. Descriptive Statistics: It involves organizing, summarizing, and describing the main features of a set of data. This
includes measures of central tendency (mean, median, and mode), measures of variability (range, variance, and
standard deviation), and measures of shape (skewness and kurtosis).
Probability: It involves calculating the likelihood of events occurring. Basic concepts include independent and
dependent events, mutually exclusive events, and conditional probability.
Inferential Statistics: It involves making inferences about a population based on a sample of data. This includes
estimation, hypothesis testing, and regression analysis.
Hypothesis Testing: It involves using statistical methods to test a claim about a population parameter. This includes the
selection of appropriate statistical tests, interpreting test results, and making conclusions.
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4. Regression Analysis: It involves modeling the relationship between two or more variables. This includes simple linear
regression, multiple linear regression, and logistic regression.
Non-Parametric Statistics: It involves statistical methods that do not assume a specific distribution for the data. This
includes the Wilcoxon rank-sum test, the Kruskal-Wallis test, and the Mann-Whitney test.
Bayesian Statistics: It involves updating probabilities based on new information. This includes Bayes' theorem and
Markov Chain Monte Carlo (MCMC) methods.
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