This course introduces statistical inference, sampling distributions, and confidence intervals. Students will learn how to define and construct good estimators, method of moments estimation, maximum likelihood estimation, and methods of constructing confidence intervals that will extend to more general settings.
Este curso forma parte de Programa especializado: Data Science Foundations: Statistical Inference
25 calificaciones
2406 ya inscrito
ofrecido por
Programa especializado: Data Science Foundations: Statistical InferenceUniversidad de Colorado en BoulderAcerca de este Curso
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Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Aprox. 26 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
Qué aprenderás
Identify characteristics of “good” estimators and be able to compare competing estimators.
Construct sound estimators using the techniques of maximum likelihood and method of moments estimation.
Construct and interpret confidence intervals for one and two population means, one and two population proportions, and a population variance.
Fechas límite flexibles
Restablece las fechas límite en función de tus horarios.
Certificado para compartir
Obtén un certificado al finalizar
100 % en línea
Comienza de inmediato y aprende a tu propio ritmo.
Nivel intermedio
Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R.
Aprox. 26 horas para completar
Inglés (English)
Subtítulos: Inglés (English)
ofrecido por
Universidad de Colorado en Boulder
CU-Boulder is a dynamic community of scholars and learners on one of the most spectacular college campuses in the country. As one of 34 U.S. public institutions in the prestigious Association of American Universities (AAU), we have a proud tradition of academic excellence, with five Nobel laureates and more than 50 members of prestigious academic academies.
Comienza a trabajar para obtener tu maestría
Este curso es parte del Master of Science in Data Science completamente en línea de Universidad de Colorado en Boulder.
Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.
Programa - Qué aprenderás en este curso
7 horas para completar
Point Estimation
In this module you will learn how to estimate parameters from a large population based only on information from a small sample. You will learn about desirable properties that can be used to help you to differentiate between good and bad estimators. We will review the concepts of expectation, variance, and covariance, and you will be introduced to a formal, yet intuitive, method of estimation known as the "method of moments".
7 horas para completar
10 videos (Total 142 minutos), 11 lecturas, 4 cuestionarios
3 horas para completar
Maximum Likelihood Estimation
3 horas para completar
5 videos (Total 67 minutos), 5 lecturas, 2 cuestionarios
5 horas para completar
Large Sample Properties of Maximum Likelihood Estimators
In this module we will explore large sample properties of maximum likelihood estimators including asymptotic unbiasedness and asymptotic normality. We will learn how to compute the “Cramér–Rao lower bound” which gives us a benchmark for the smallest possible variance for an unbiased estimator.
5 horas para completar
5 videos (Total 100 minutos), 5 lecturas, 2 cuestionarios
7 horas para completar
Confidence Intervals Involving the Normal Distribution
7 horas para completar
5 videos (Total 99 minutos), 5 lecturas, 3 cuestionarios
Acerca de Programa especializado: Data Science Foundations: Statistical Inference
This program is designed to provide the learner with a solid foundation in probability theory to prepare for the broader study of statistics. It will also introduce the learner to the fundamentals of statistics and statistical theory and will equip the learner with the skills required to perform fundamental statistical analysis of a data set in the R programming language.
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