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Analysing continuous outcomes: what data can I use?
Analysing continuous outcomes: what data can I use?

Chapter 10 – Sampling Distributions
Chapter 10 – Sampling Distributions

Location of Packet 1
Location of Packet 1

... variability - thus, improving the precision of inference Moving from descriptive statistics to making inference: Margin of Error (ME). ME allows statement about the range of plausible values for the population parameter. ME measures sampling variability you’d expect in repeated samples. Mathematical ...
**A sample size of 900 is not large enough to conclude that the
**A sample size of 900 is not large enough to conclude that the

Results & Data Analysis
Results & Data Analysis

... three of more independent groups (F statistic) ...
Chapter 5
Chapter 5

population
population

Chapter 6
Chapter 6

Confidence Intervals Suppose we observe data X1,...,Xn and
Confidence Intervals Suppose we observe data X1,...,Xn and

Estimating with Confidence
Estimating with Confidence

... The last chapter provided practice finding confidence intervals and carrying out tests of significance in a somewhat unrealistic setting. We needed the population σ. In reality σ and μ are rarely known. The conditions for inference about a mean are as before: SRS and a normal distribution. For these ...
10-04 lecture
10-04 lecture

... • Are CU undergrads smarter than population? – Sample size n = 100, sample mean M = 103 ...
Lecture Powerpoint presentation
Lecture Powerpoint presentation

Chapter 9 Day 2
Chapter 9 Day 2

Chapter 8 review answers File
Chapter 8 review answers File

Review Hypothesis testing for population mean
Review Hypothesis testing for population mean

Notation summary
Notation summary

Common Definitions from Statistics 210
Common Definitions from Statistics 210

Estimating Means and Proportions
Estimating Means and Proportions

... same as the mean of the population from which the sample is drawn. • Variance: The variance of this distribution is equal to the population variance divided by the sample size (n). • Justification: Central Limit Theorem. • Implications: If we draw a random sample of sufficient size we can estimate t ...
Homework #1
Homework #1

STA 2023 - FAU Math
STA 2023 - FAU Math

View/Open - Pan Africa Christian University
View/Open - Pan Africa Christian University

... The following data represent scores of 50 students in a calculus test. ...
List of topics
List of topics

Refreshing Your Skills 11
Refreshing Your Skills 11

... Recall that deviations are the signed differences between the data values and the mean. The standard deviation, s, is the sum of the squares of the deviations divided by one less than the number of data values. ...
population - Penn State Department of Statistics
population - Penn State Department of Statistics

Suppose that as a military psychologist you know that the population
Suppose that as a military psychologist you know that the population

< 1 ... 372 373 374 375 376 377 378 379 380 382 >

Bootstrapping (statistics)



In statistics, bootstrapping can refer to any test or metric that relies on random sampling with replacement. Bootstrapping allows assigning measures of accuracy (defined in terms of bias, variance, confidence intervals, prediction error or some other such measure) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. Generally, it falls in the broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution function of the observed data. In the case where a set of observations can be assumed to be from an independent and identically distributed population, this can be implemented by constructing a number of resamples with replacement, of the observed dataset (and of equal size to the observed dataset).It may also be used for constructing hypothesis tests. It is often used as an alternative to statistical inference based on the assumption of a parametric model when that assumption is in doubt, or where parametric inference is impossible or requires complicated formulas for the calculation of standard errors.
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