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Study Guide
Study Guide

Bootstrapping: described and illustrated Comparing
Bootstrapping: described and illustrated Comparing

... etc.). Our goal is to make inferences about ‘parent population’ parameters using confidence intervals that have in turn been constructed using the information from a reasonably large number of computer generated bootstrap samples. (Take note: we will NOT introduce any mathematical theory here; all t ...
Statistics
Statistics

Educator Materials Click and Learn Sampling and Normal Distribution
Educator Materials Click and Learn Sampling and Normal Distribution

... 2. Instruct students to complete the Pre-assessment Question (which could be collected on note cards as a formative assessment). Then instruct students to access the Click and Learn at http://www.hhmi.org/biointeractive/sampling-and-normal-distribution and complete items 2 through 5. It is import ...
MAT 210 Class Notes
MAT 210 Class Notes

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PDF Format

Word Format
Word Format

Math109 homework 1soln
Math109 homework 1soln

11.1
11.1

... contract … they each have some precision (standard deviation) … are their precisions significantly different ...
Chapter 10 Comparisons Involving Means Part A
Chapter 10 Comparisons Involving Means Part A

... 6. Determine whether to reject H0. z = 6.49 > z.01 = 2.33, so we reject H0. At the .01 level of significance, the sample evidence indicates the mean driving distance of Par, Inc. golf balls is greater than the mean driving distance of Rap, Ltd. golf balls. ...
Measures of Variability or Dispersion
Measures of Variability or Dispersion

Homework Solutions – Statistics
Homework Solutions – Statistics

Chapter 5: Regression Analysis: fitting equations to data 5.0
Chapter 5: Regression Analysis: fitting equations to data 5.0

Project 2c - emilyyleak
Project 2c - emilyyleak

Statistics – Theory
Statistics – Theory

6.6 The Central Limit Theorem 6.6.1 State the Central Limit Theorem
6.6 The Central Limit Theorem 6.6.1 State the Central Limit Theorem

Inference for one Population Mean
Inference for one Population Mean

Hypothesis testing for means
Hypothesis testing for means

... As we shall see, in any of the contexts we consider, we could work out a confidence interval, or we could carry out a hypothesis test. This raises obvious questions of principle and practice. Should we prefer one or the other? Are the two connected somehow, as intuition suggest they must be? If so, ...
Inference for one Population Mean
Inference for one Population Mean

σ 2
σ 2

... A traffic engineer is concerned about the delays at an intersection near a local school. The intersection is equipped with a fully actuated (“demand”) traffic light and there have been complaints that traffic on the main street is subject to unacceptable delays. To develop a benchmark, the traffic e ...
[R] esize
[R] esize

... effect sizes. Do not confuse the by() option with the by prefix; you can specify both. cohensd specifies that Cohen’s d (1988) be reported. hedgesg specifies that Hedges’s g (1981) be reported. glassdelta specifies that Glass’s ∆ (Smith and Glass 1977) be reported. pbcorr specifies that the point-bi ...
STAT-UB.0103    Sample Final Exam Problems
STAT-UB.0103 Sample Final Exam Problems

... B2. An auditing investigator is suspicious about the office supply bills submitted by a large division of Psytron, Inc. In particular, he suspects that these bills have been overstating the quantities of merchandise actually received. This problem can be resolved by doing interviews with randomly-se ...
chapter 8 introduction to hypothesis testing
chapter 8 introduction to hypothesis testing

spract4
spract4

... standard deviation 0.12 pounds. A random sample of sixteen bricks from today's output had a mean weight of 4.07 pounds. (a) Find a 99% confidence interval for the mean weight of all bricks produced today. A confidence interval consists of three elements: a measure of central tendency, a number of st ...
Measures of Variability or Dispersion
Measures of Variability or Dispersion

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Misuse of statistics

Statistics are supposed to make something easier to understand but when used in a misleading fashion can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. In others, it is purposeful and for the gain of the perpetrator. When the statistical reason involved is false or misapplied, this constitutes a statistical fallacy.The false statistics trap can be quite damaging to the quest for knowledge. For example, in medical science, correcting a falsehood may take decades and cost lives.Misuses can be easy to fall into. Professional scientists, even mathematicians and professional statisticians, can be fooled by even some simple methods, even if they are careful to check everything. Scientists have been known to fool themselves with statistics due to lack of knowledge of probability theory and lack of standardization of their tests.
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