Data Exploration: Normal Distribution

This lecture covers strategies for exploring data distributions, including identifying outliers, studying overall data structure, making graphical representations, and calculating numerical summaries. It also delves into modeling data with normal distributions, properties of normal densities, and the standard normal distribution. The instructor demonstrates how to calculate proportions using the normal model and interpret standardized values. Additionally, the lecture discusses tools for checking normality, such as histograms, boxplots, and QQ plots, and provides insights into estimating parameters of a normal distribution from data.

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related lectures (44)

Related concepts (41)

MATH-234(a): Probability and statistics

Ce cours enseigne les notions élémentaires de la théorie de probabilité et de la statistique, tels que l'inférence, les tests et la régression.

Normal-gamma distribution

In probability theory and statistics, the normal-gamma distribution (or Gaussian-gamma distribution) is a bivariate four-parameter family of continuous probability distributions. It is the conjugate prior of a normal distribution with unknown mean and precision. For a pair of random variables, (X,T), suppose that the conditional distribution of X given T is given by meaning that the conditional distribution is a normal distribution with mean and precision — equivalently, with variance Suppose also that the marginal distribution of T is given by where this means that T has a gamma distribution.

Folded normal distribution

The folded normal distribution is a probability distribution related to the normal distribution. Given a normally distributed random variable X with mean μ and variance σ2, the random variable Y = |X| has a folded normal distribution. Such a case may be encountered if only the magnitude of some variable is recorded, but not its sign. The distribution is called "folded" because probability mass to the left of x = 0 is folded over by taking the absolute value.

Normal distribution

In statistics, a normal distribution or Gaussian distribution is a type of continuous probability distribution for a real-valued random variable. The general form of its probability density function is The parameter is the mean or expectation of the distribution (and also its median and mode), while the parameter is its standard deviation. The variance of the distribution is . A random variable with a Gaussian distribution is said to be normally distributed, and is called a normal deviate.

Five-number summary

The five-number summary is a set of descriptive statistics that provides information about a dataset. It consists of the five most important sample percentiles: the sample minimum (smallest observation) the lower quartile or first quartile the median (the middle value) the upper quartile or third quartile the sample maximum (largest observation) In addition to the median of a single set of data there are two related statistics called the upper and lower quartiles.

Multivariate normal distribution

In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem.

Statistical Analysis: Boxplot and Normal Distribution MATH-131: Probability and statistics

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Estimation and Confidence Intervals MATH-234(b): Probability and statistics

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Probability and Statistics: Data Modeling and Analysis MATH-131: Probability and statistics

Explores PDF forms, statistics, boxplots, density curves, and data analysis methods.

Estimators and Confidence Intervals MATH-131: Probability and statistics

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.

Probability Distributions in Environmental Studies ENV-400: Air pollution and climate change

Explores probability distributions for random variables in air pollution and climate change studies, covering descriptive and inferential statistics.