Probability and Statistics III: A Gentle Introduction to Statistics

This course provides an introduction to basic statistical concepts. We begin by walking through a library of probability distributions – including the normal distribution, which in turn leads to the Central Limit Theorem. We then discuss elementary descriptive statistics and estimation methods.

Inscríbete

Me gustaría recibir correos electrónicos de GTx e informarme sobre otras ofertas relacionadas con Probability and Statistics III: A Gentle Introduction to Statistics.

Learners will be expected to come in knowing a bit of set theory and basic calculus, as well as the material from the first two courses in this series (the Gentle Introduction to Probability and Random Variables courses). The prerequisite material is all available for you to access; and in any event, we will try to make the current course as self-contained as possible. In addition, this course will involve a bit of computer programming, so it would be nice to have at least a little experience in something like Excel and/or the R freeware statistical package.

Sobre este curso

Omitir Sobre este curso

This course provides an introduction to basic statistical concepts.

We begin by walking through a library of probability distributions, where we motivate their uses and go over their fundamental properties.
These distributions include such important folks as the Bernoulli, binomial, geometric, Poisson, uniform, exponential, and normal distributions, just to name a few. Particular attention is paid to the normal distribution, because it leads to the Central Limit Theorem (the most-important mathematical result in the universe, actually), which enables us to make probability calculations for arbitrary averages and sums of random variables.

We then discuss elementary descriptive statistics and estimation methods, including unbiased estimation, maximum likelihood estimation, and the method of moments – you gotta love your MoM! Finally, we describe the t, χ2, and F sampling distributions, which will prove to be useful in upcoming statistical applications.

Lo que aprenderás

Omitir Lo que aprenderás

Upon completion of this course, learners will be able to:

Review a library of discrete and continuous probability distributions
Recognize normal distribution and the Central Limit Theorem, and how they are applied in practice
Recognize elementary methods of descriptive statistics
Describe methods that can be used to estimate the unknown parameters of a distribution
Identify statistical sampling distributions

Plan de estudios

Omitir Plan de estudios

“FCPS” refers to the free text, A First Course in Probability and Statistics

Module 1: Distributions
• Lesson 1: Bernoulli and Binomial Distributions (FCPS §4.1.1)
• Lesson 2: Hypergeometric Distribution (FCPS §4.1.2)
• Lesson 3: Geometric and Negative Binomial Distributions (FCPS §4.1.3)
• Lesson 4: Poisson Distribution (FCPS §4.1.4)
• Lesson 5: Uniform, Exponential, and Friends (FCPS §4.2.1–4.2.2)
• Lesson 6: Other Continuous Distributions (FCPS §4.2.3)
• Lesson 7: Normal Distribution: Basics (FCPS §4.3.1)
Module 1 (cont’d): Distributions
• Lesson 8: Standard Normal Distribution (FCPS §4.3.2)
• Lesson 9: Sample Mean of Normals (FCPS §4.3.3)
• Lesson 10: The Central Limit Theorem + OPTIONAL Proof (FCPS §4.3.4)
• Lesson 11: Central Limit Theorem Examples (FCPS §4.3.5)
• Lesson 12 [OPTIONAL]: Extensions – Multivariate Normal Distribution (FCPS §4.4.1)
• Lesson 13 [OPTIONAL]: Extensions – Lognormal Distribution (FCPS §4.4.2)
• Lesson 14: Computer Stuff, including OPTIONAL Box-Muller Proof (FCPS §4.5)
Module 2: Getting Started with Statistics
• Lesson 1: Introduction to Descriptive Statistics (FCPS §5.1.1)
• Lesson 2: Summarizing Data (FCPS §5.1.2)
• Lesson 3: Candidate Distributions (FCPS §5.1.3)
• Lesson 4: Introduction to Estimation (FCPS §5.2.1)
• Lesson 5: Unbiased Estimation (FCPS §5.2.2)
• Lesson 6: Mean Squared Error (FCPS §5.2.3)
Module 2 (cont’d): Getting Started with Statistics
• Lesson 7: Maximum Likelihood Estimation (FCPS §5.2.4)
• Lesson 8: Trickier MLE Examples (FCPS §5.2.4)
• Lesson 9: Invariance Property of MLEs (FCPS §5.2.4)
• Lesson 10: Method of Moments Estimation (FCPS §5.2.5)
• Lesson 11: Sampling Distributions (FCPS §5.3)

Conoce a tus instructores

The Georgia Institute of Technology

David Goldsman

Professor of Industrial and Systems Engineering

The Georgia Institute of Technology

Obtén un Certificado Verificado para destacar los conocimientos y las habilidades que adquieras
$199 USD

Ver un modelo de certificado de edX en PDF

Oficial y verificado
Obtén un certificado con la firma del instructor y el logotipo de la institución para demostrar tus logros y aumentar las posibilidades de conseguir trabajo
Fácil de compartir
Agrega el certificado a tu currículum o publícalo directamente en LinkedIn
Incentivo comprobado
El certificado te da un motivo más para completar el curso
Apoya nuestra labor
edX, una organización sin fines de lucro, se sustenta con los certificados verificados para financiar la educación gratuita para todo el mundo

Preguntas frecuentes

*What if I don’t remember much basic probability and statistics?

Answer: You can take the companion courses, “A Gentle Introduction to Probability” and “Random Variables – Great Expectations to Bell Curves”, which will get you up to speed in a jiff!

*Will there be much programming?

Answer: A little, but you’ll be handle that in Excel or Matlab or whatever your favorite programming application is. We also go over a couple of R demos, but those will be self-contained

Comparte este curso

Prerrequisitos

Sobre este curso

Lo que aprenderás

Plan de estudios

Conoce a tus instructores

Obtén un Certificado Verificado para destacar los conocimientos y las habilidades que adquieras$199 USD

Oficial y verificado

Fácil de compartir

Incentivo comprobado

Apoya nuestra labor

Preguntas frecuentes

Obtén un Certificado Verificado para destacar los conocimientos y las habilidades que adquieras
$199 USD