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I need help with hypothesis testing and excel
I need help with hypothesis testing and excel

N.2 Understanding Numerical Data
N.2 Understanding Numerical Data

... • Let’s say that you want to know the lipid content of a typical corn grain. • You could analyze one grain, but how would you know that you’d picked a “typical” grain? • You’d get a better estimate of “typical” if you increased you sample size to a few hundred grain, or even to 10,000. Or to 1,000,0 ...
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... significantly > scree plot. Tendency to take too much; 2) Enough PC’s should be included to explain >= 90% of the total variance. Tendency to take too few; 3) PC omitted if its variance < average of all PC’s or less than 1 when the correlation matrix is used; ...
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7. Point Estimation and Confidence Intervals for Means

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Section 18: Inferences about Means (σ unknown, sample “small

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Chapter Seven: Confidence Intervals and Sample Size A point

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Sampling from a population of “0”s and “1”s

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Power and Sample Size + Principles of Simulation

... Mean different from 0 hypotheses: ◦ ho (null hypothesis) is μ=0 ◦ ha (alternative hypothesis) is μ ≠ 0 ...
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1 Estimating the uncertainty attached to a sample mean: s vs. σ

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Estimation: Point and Interval

Econ173_sp03MidtermAnswers
Econ173_sp03MidtermAnswers

... machine is filling jars within 0.2 oz of the mean. Assume that previous studies suggest that the population is normally distributed with population standard deviation of 0.9. How big of a sample do you need to conduct this test? (z0.025=1.96, z0.05=1.645, z0.1=1.28) a. 40 b. 9 c. 34 d. 77 e. 78 Use ...
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Statistics for the Social Science

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Clicker_chapter18 - ROHAN Academic Computing

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Algebra 1B Assignments Data, Statistics, and Probability

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Basic Statistical Models - CIS @ Temple University

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6 Random Sampling and Data Description

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Inference for one sample

< 1 ... 155 156 157 158 159 160 161 162 163 ... 285 >

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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