Statistics and Probability
Descriptive Statistics involves summarizing and describing the features of a data set, including measures of central tendency and measures of variability.
Inferential Statistics focuses on using data from sample populations to make inferences and hypothesis testing about larger populations.
Probability Theory is the mathematical framework for quantifying uncertain phenomena, providing the foundations for statistical reasoning.
Bayesian Statistics is an approach that utilizes Bayes' Theorem to update the probability of a hypothesis as more information becomes available.
Time Series Analysis involves statistical techniques for analyzing data points collected or sequenced over time to identify trends and patterns.
Multivariate Analysis includes methods for analyzing data involving multiple variables to understand relationships and patterns within the data.
Regression Analysis is a powerful statistical method for estimating relationships among variables, often used for prediction and forecasting.
Non-parametric Statistics refers to methods that do not rely on data belonging to any particular parameterized family of probability distributions.
Survival Analysis deals with the analysis of time-to-event data, commonly used in medical research and reliability engineering.
Statistical Simulation is the use of computer models to imitate random processes and estimate probabilities of different outcomes through virtual experiments.
Experimental Design is the planning of experiments to ensure that appropriate data will be gathered for analysis and conclusions.
Biostatistics applies statistical methods to biological and medical research, essential for understanding public health and clinical trials.
Econometrics combines economics, mathematics, and statistical methods to analyze economic data and test hypotheses.
Psychometrics focuses on the theory and techniques for psychological measurement, including the development and refinement of tests and scales.
Statistical Quality Control utilizes statistical methods for monitoring and controlling quality during the manufacturing process.
Machine Learning and Data Mining involve statistical methods for making sense of large datasets, uncovering patterns, and making predictions.