MASTER STATISTICS

Basic principles and concepts essential to understanding data analysis

Explore the presentation and summary of data through various methods such as measures of central tendency and dispersion, allowing you to describe datasets

Study of functions that assign numerical values to outcomes in a random experiment, forming the basis for understanding probability and modeling uncertain events

Understand the characteristics and applications of statistical distributions in continuous and discrete datasets. From the precise modeling of discrete events to the representation of continuous phenomena, explore how these distributions are fundamental in statistics

Probability is the quantitative assessment of the likelihood of an event occurring. It is expressed as a number between 0 and 1, where 0 indicates certainty of non-occurrence and 1 indicates certainty of occurrence

Point estimation provides a single estimation for an unknown population parameter, such as the mean or proportion while confidence intervals give a range of values within which the true population parameter is likely to fall at a certain confidence level around the point estimate

Hypothesis testing is a statistical method used to determine if there is enough evidence to support a specific claim or assumption about a population

Sampling is a technique used in statistics to select a subset of individuals or observations from a larger population

Bootstrap method is a resampling technique used to estimate the distribution of a statistic by repeatedly sampling with replacement from the observed data

Machine learning is a field of artificial intelligence focused on developing algorithms that allow systems to learn from data and improve performance over time without explicit programming

Time series are sequences of data points collected or recorded at specific time intervals, typically ordered chronologically

Mathematical optimization in statistics involves finding the best parameters or solutions that maximize or minimize a particular objective function, such as fitting a model to data or minimizing error, to achieve optimal statistical performance or predictions