Econ 299 Chapter 06
Econ 299 Chapter 06

Module 6: The Sampling Distributions
Module 6: The Sampling Distributions

Chapter 8: Introduction to Statistical Inference
Chapter 8: Introduction to Statistical Inference

Chapter 12 - Inference About A Population
Chapter 12 - Inference About A Population

Chapter 12 - Inference About A Population
Chapter 12 - Inference About A Population

Estimates of Population Parameters
Estimates of Population Parameters

Completely Randomized Design
Completely Randomized Design

Chapter 7
Chapter 7

MDST242 C2 - The Open University
MDST242 C2 - The Open University

10.2
10.2

Interpreting Confidence Intervals
Interpreting Confidence Intervals

sample
sample

Document
Document

Chapter 9 Correlation and Regression
Chapter 9 Correlation and Regression

II. Finite Sample Properties of Estimators
II. Finite Sample Properties of Estimators

Introducing Hypothesis Tests
Introducing Hypothesis Tests

Problem set for PA551
Problem set for PA551

Significance Testing
Significance Testing

Confidence intervals
Confidence intervals

BraseUStat10_10_01 (compilation)
BraseUStat10_10_01 (compilation)

Point Estimation
Point Estimation

Chapter18 [Repaired]
Chapter18 [Repaired]

chapter18
chapter18

CHAPTER 9
CHAPTER 9

Chapter 7 - Practice Problems 1
Chapter 7 - Practice Problems 1

Resampling (statistics)

In statistics, resampling is any of a variety of methods for doing one of the following: Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping) Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests, randomization tests, or re-randomization tests) Validating models by using random subsets (bootstrapping, cross validation)Common resampling techniques include bootstrapping, jackknifing and permutation tests.