Estimation theory | Parametric statistics
In statistical inference, the concept of a confidence distribution (CD) has often been loosely referred to as a distribution function on the parameter space that can represent confidence intervals of all levels for a parameter of interest. Historically, it has typically been constructed by inverting the upper limits of lower sided confidence intervals of all levels, and it was also commonly associated with a fiducial interpretation (fiducial distribution), although it is a purely frequentist concept. A confidence distribution is NOT a probability distribution function of the parameter of interest, but may still be a function useful for making inferences. In recent years, there has been a surge of renewed interest in confidence distributions. In the more recent developments, the concept of confidence distribution has emerged as a purely frequentist concept, without any fiducial interpretation or reasoning. Conceptually, a confidence distribution is no different from a point estimator or an interval estimator (confidence interval), but it uses a sample-dependent distribution function on the parameter space (instead of a point or an interval) to estimate the parameter of interest. A simple example of a confidence distribution, that has been broadly used in statistical practice, is a bootstrap distribution. The development and interpretation of a bootstrap distribution does not involve any fiducial reasoning; the same is true for the concept of a confidence distribution. But the notion of confidence distribution is much broader than that of a bootstrap distribution. In particular, recent research suggests that it encompasses and unifies a wide range of examples, from regular parametric cases (including most examples of the classical development of Fisher's fiducial distribution) to bootstrap distributions, p-value functions, normalized likelihood functions and, in some cases, Bayesian priors and Bayesian posteriors. Just as a Bayesian posterior distribution contains a wealth of information for any type of Bayesian inference, a confidence distribution contains a wealth of information for constructing almost all types of frequentist inferences, including point estimates, confidence intervals, critical values, statistical power and p-values, among others. Some recent developments have highlighted the promising potentials of the CD concept, as an effective inferential tool. (Wikipedia).
Lesson: Calculate a Confidence Interval for a Population Proportion
This lesson explains how to calculator a confidence interval for a population proportion.
From playlist Confidence Intervals
07 Data Analytics: Confidence Intervals
Lecture on confidence intervals. What are they? How to calculate them? How we can impact business decisions.
From playlist Data Analytics and Geostatistics
Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
https://www.patreon.com/ProfessorLeonard Statistics Lecture 7.2: Finding Confidence Intervals for the Population Proportion
From playlist Statistics (Full Length Videos)
Statistics 5_1 Confidence Intervals
In this lecture explain the meaning of a confidence interval and look at the equation to calculate it.
From playlist Medical Statistics
Calculate a Confidence Interval for a Population Proportion (Basic)
This example explains how to calculator a confidence interval for a population proportion.
From playlist Confidence Intervals
Excel 2010 Preview #7: Confidence Intervals for T-Distribution CONFIDENCE.T function
Download Excel file: https://people.highline.edu/mgirvin/YouTubeExcelIsFun/Excel2010NewAwesomeThings1-8.xlsx The new Excel 2010 Statistics function CONFIDENCE.T will calculate the Margin of Error for a T Distribution Confidence Interval much more efficiently that in earlier version!!! Tot
From playlist Excel 2010 Videos
Calculate a Confidence Interval for a Population Proportion (Plus Four Method)
This lesson explains how to calculator a confidence interval for a population proportion using the Plus Four Method.
From playlist Confidence Intervals
How to find a confidence interval with a z distribution
Easy steps to calculating a confidence interval using a z score.
From playlist Hypothesis Tests and Critical Values
Finding The Confidence Interval of a Population Proportion Using The Normal Distribution
This statistics video tutorial explains how to find the confidence interval of a population proportion using the normal distribution. It also explains how to calculate the margin of error also known as the error bound for the true proportion. it discusses how to calculate the sample size
From playlist Statistics
0:15 - Review 2:29 - Learning objectives 2:48 - 1. Construct and interpret sampling distributions using StatKey 3:36 - StatKey 10:42 - Review of terms 12:12 - 2. Explain the general form of a confidence interval 16:59 - 3. Interpret a confidence interval 23:47 - 4. Explain the
From playlist STAT 200 Video Lectures
From playlist STAT 200 Video Lectures
Python for Data Analysis: Confidence Intervals
This video covers the basics of making point estimates and creating confidence intervals in Python. Subscribe: ► https://www.youtube.com/c/DataDaft?sub_confirmation=1 This is lesson 23 of a 30-part introduction to the Python programming language for data analysis and predictive modeling.
From playlist Python for Data Analysis
Introduction to R: Confidence Intervals
This is lesson 23 of a 30-part introduction to the R programming language for data analysis and predictive modeling. Link to the code notebook below: Intro to R: Confidence Intervals https://www.kaggle.com/hamelg/intro-to-r-part-23-confidence-intervals This lesson covers point estimates
From playlist Introduction to R
Bootstrap and Confidence Intervals
In this video, I explain how to make a confidence interval with bootstrapping. Here, we go over how to make a confidence interval with the true population, how to apply bootstrap to get the confidence interval and finally, I walk you through what happens to the confidence interval as the s
From playlist Introduction to Data Science - Foundations
Confidence Intervals: Crash Course Statistics #20
Today we’re going to talk about confidence intervals. Confidence intervals allow us to quantify our uncertainty, by allowing us to define a range of values for our predictions and assigning a likelihood that something falls within that range. And confidence intervals come up a lot like whe
From playlist Statistics
Table of Contents: 00:00 - Introduction 01:05 - Review: Inferential Statistics 01:47 - Introduction to Confidence Intervals 07:10 - Interpreting Confidence Intervals 09:21 - Applying Confidence Intervals 11:22 - Bootstrap Sampling Distributions 12:44 - StatKey: Bootstrap Sampling
From playlist STAT 200 Lectures (OER)
Table of Contents: 00:50 - Lecture structure Two Proportions 01:11 - Checking assumptions 02:50 - Computing the standard error by hand 03:59 - Example: Computing the standard error for a confidence interval 06:22 - Example: Computing the standard error for a hypothesis test 08
From playlist STAT 200 Video Lectures
The Normal Distribution (1 of 3: Introductory definition)
More resources available at www.misterwootube.com
From playlist The Normal Distribution
Lec 6 | MIT 2.830J Control of Manufacturing Processes, S08
Lecture 6: Sampling distributions and statistical hypotheses Instructor: Duane Boning, David Hardt View the complete course at: http://ocw.mit.edu/2-830JS08 License: Creative Commons BY-NC-SA More information at http://ocw.mit.edu/terms More courses at http://ocw.mit.edu
From playlist MIT 2.830J, Control of Manufacturing Processes S08