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Sample Deviation
Sample Deviation

Chapter 13
Chapter 13

Table: Chi-Square Probabilities - Fisher College of Business
Table: Chi-Square Probabilities - Fisher College of Business

Statistical Significance and Bivariate Tests
Statistical Significance and Bivariate Tests

... – Typically take advantage of central limit theorem (imposes requirements on probability distributions) – Appropriate only for interval and ratio data. – More powerful than nonparametric methods. • Nonparametric statistics: – Do not require assumptions concerning the probability distribution for the ...
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Estimation and Hypothesis Testing with Large Sample of Data: Part II

Introduction to the t statistic OVERVIEW 1. A sample mean (X)
Introduction to the t statistic OVERVIEW 1. A sample mean (X)

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... zc = critical value for confidence level c based on the standard normal distribution. Example 3: Julia enjoys jogging. She has been jogging over a period of several years during which time her physical condition has remained constantly good. Usually, she jogs 2 miles per day. The standard deviation ...
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14.0 Hypothesis Testing

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is used when we have categorical (nominal) rather than interval

Chapter 11/12 Review 1. The mean weight of loaves of bread
Chapter 11/12 Review 1. The mean weight of loaves of bread

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Chapter 3 Bootstrap

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Confidence intervals

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Slides for week 11 lecture 1

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Summary of calculator commands

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mean weight and parasite infection (25 nest)
mean weight and parasite infection (25 nest)

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Chapter 5 Slides

... • Often used to model times: survival of components, to complete tasks, between customer arrivals at a checkout line, etc. Density is highly skewed: Sample means of size 10 (m=1, s=1/100.5=0.32) ...
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... scale are equal  has ordinal properties  has an absolute zero (a value below which others have no meaning)  (e.g. degrees K, all weights and measures) ...
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b.sc maths stat 2 qp bank 2016 - E

AMS 315/576 Lecture Notes Chapter 5
AMS 315/576 Lecture Notes Chapter 5

< 1 ... 172 173 174 175 176 177 178 179 180 ... 229 >

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.
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