Este curso forma parte de Programa especializado: Data Science Foundations: Statistical Inference

4.8
estrellas
25 calificaciones

Jem Corcoran

3093 ya inscrito

Ofrecido Por

Parte del título de grado Master of Science in Electrical Engineering
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Programa especializado: Data Science Foundations: Statistical InferenceUniversidad de Colorado en Boulder

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This course will focus on theory and implementation of hypothesis testing, especially as it relates to applications in data science. Students will learn to use hypothesis tests to make informed decisions from data. Special attention will be given to the general logic of hypothesis testing, error and error rates, power, simulation, and the correct computation and interpretation of p-values. Attention will also be given to the misuse of testing concepts, especially p-values, and the ethical implications of such misuse.
This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder.

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Curso 3 de 3 en

Programa especializado: Data Science Foundations: Statistical Inference

Nivel intermedio

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R

Aprox. 36 horas para completar

Inglés (English)

Qué aprenderás

Define a composite hypothesis and the level of significance for a test with a composite null hypothesis.
Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form of a rejection region.
Perform tests concerning a true population variance.
Compute the sampling distributions for the sample mean and sample minimum of the exponential distribution.

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.

Laboratorios de Coursera

Incluye proyectos de aprendizaje prácticos.

Obtén más información sobre los laboratorios de Coursera

Curso 3 de 3 en

Programa especializado: Data Science Foundations: Statistical Inference

Nivel intermedio

Sequence in calculus up through Calculus II (preferably multivariate calculus) and some programming experience in R

Aprox. 36 horas para completar

Inglés (English)

Instructor

Calificación del instructor

4,87/5 (9 calificaciones)

Jem Corcoran

Ofrecido por

Universidad de Colorado en Boulder

Comienza a trabajar para obtener tu maestría

Este curso es parte de Master of Science in Electrical Engineering completamente en línea de Universidad de Colorado en Boulder. Si te aceptan en el programa completo, tus cursos cuentan para tu título de grado.

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Programa - Qué aprenderás en este curso

Semana1

Semana 1

2 horas para completar

Start Here!

2 horas para completar

3 lecturas

8 horas para completar

Fundamental Concepts of Hypothesis Testing

8 horas para completar

6 videos (Total 70 minutos), 11 lecturas, 2 cuestionarios

6 videos

What is Hypothesis Testing?3mTypes of Hypotheses14mNormal Computations23mErrors in Hypothesis Testing7mTest Statistics and Significance14mA First Test4m

11 lecturas

What is Hypothesis Testing?5mTypes of Hypotheses10mVideo Slides for Types of Hypotheses10mNormal Computations10mVideo Slides for Normal Computations10mErrors in Hypothesis Testing10mVideo Slides for Errors in Hypothesis Testing10mTest Statistics and Significance10mVideo Slides for Test Statistics and Level of Significance10mA First Test10mVideo Slides for A First Test10m

1 ejercicio de práctica

Introduction to Hypothesis Testing30m

Semana2

Semana 2

8 horas para completar

Composite Tests, Power Functions, and P-Values

8 horas para completar

7 videos (Total 125 minutos), 7 lecturas, 2 cuestionarios

7 videos

Composite Hypotheses and Level of Significance16mOne-Tailed Tests20mPower Functions13mHypothesis Testing with P-Values21mTwo Tailed Tests12mCLT: A Brief Review16mHypothesis Tests for Proportions23m

7 lecturas

Video Slides for Composite Hypotheses and Level of Significance10mVideo Slides for One-Tailed Tests10mVideo Slides for Power Functions10mVideo Slides for Hypothesis Testing with P-Values10mVideo Slides for Two-Tailed Tests10mVideo Slides for CLT: A Brief Review10mVideo Slides for Hypothesis Tests for Proportions10m

1 ejercicio de práctica

Constructing Tests30m

Semana3

Semana 3

8 horas para completar

t-Tests and Two-Sample Tests

8 horas para completar

7 videos (Total 140 minutos), 7 lecturas, 2 cuestionarios

7 videos

The t and Chi-Squared Distributions41mThe Sample Variance for the Normal Distribution23mt-Tests18mTwo Sample Tests for Means15mTwo Sample t-Tests for a Difference of Means17mWelch's t-Test and Paired Data13mComparing Population Proportions8m

7 lecturas

Video Slides for the t and Chi-Squared Distributions10mVideo Slides for the Sample Variance and the Normal Distribution10mVideo Slides for t-Tests10mVideo Slides for Two Sample Tests for Means10mVideo Slides for Differences in Population Means10mVideo Slides for Welch's Test and Paired Data10mVideo Slides for Comparing Population Proportions10m

1 ejercicio de práctica

More Hypothesis Tests!30m

Semana4

Semana 4

4 horas para completar

Beyond Normality

In this module, we will consider some problems where the assumption of an underlying normal distribution is not appropriate and will expand our ability to construct hypothesis tests for this case. We will define the concept of a “uniformly most powerful” (UMP) test, whether or not such a test exists for specific problems, and we will revisit some of our earlier tests from Modules 1 and 2 through the UMP lens. We will also introduce the F-distribution and its role in testing whether or not two population variances are equal.

4 horas para completar

6 videos (Total 118 minutos), 6 lecturas, 2 cuestionarios

6 videos

Properties of the Exponential Distribution13mTwo Tests27mBest Tests22mUMP Tests10mA Test for the Variance of the Normal Distribution12mThe F-Distribution and a Ratio of Variances31m

6 lecturas

Video Slides for Properties of the Exponential Distribution10mVideo Slides for Two Hypothesis Tests for the Exponential10mVideo Slides for Best Tests10mVideo Slides for UMP Tests10mVideo Slides for a Normal Variance Test10mVideo Slides for an F-Distribution and a Ratio of Variances10m

2 ejercicios de práctica

Best Tests and Some General Skills30mUniformly Most Powerful Tests and F-Tests30m

Reseñas

4.8

6 reseña

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Principales reseñas sobre STATISTICAL INFERENCE AND HYPOTHESIS TESTING IN DATA SCIENCE APPLICATIONS

por GV27 de jul. de 2022

Loved the material. Content looks quite convincing and well explained!

por RK26 de oct. de 2022

In-depth course on Hypothesis testing. Course instructor is quite engaging.

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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.

This specialization can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics. Learn more about the MS-DS program at https://www.coursera.org/degrees/master-of-science-data-science-boulder. Logo adapted from photo by Christopher Burns on Unsplash.

Data Science Foundations: Statistical Inference

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Statistical Inference and Hypothesis Testing in Data Science Applications

Acerca de este Curso

Qué aprenderás

Define a composite hypothesis and the level of significance for a test with a composite null hypothesis.

Define a test statistic, level of significance, and the rejection region for a hypothesis test. Give the form of a rejection region.

Perform tests concerning a true population variance.

Compute the sampling distributions for the sample mean and sample minimum of the exponential distribution.