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Using Excel to Construct Confidence Intervals
Using Excel to Construct Confidence Intervals

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Introduction to Statistical Inference

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... If the variances are unequal, the denominator in the t-statistic is harder to compute mathematically. But not with R. The only difference is that you don’t have to specify var.equal=TRUE (so it is actually easier with R). If we continue the same example we would get the following > t.test(x,y,alt="l ...
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Inferences for the difference between two means based on two

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Math 2200 Chapter 13 Power Point

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Unit 6 – confidence intervals

... In order to determine the number of points a new high blood pressure medicine reduces a patient’s systolic measurement . A sample of 6 patients were given the medicine and their blood pressure was measured after the prescribe period of time. The number of points the blood pressure was reduce is list ...
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... • Weight trimming: weights of the outliers are changed to 1 • Number of outliers: avg. 2% of the cases • Change in the estimates: • Mean: -15% (in avarage) ...
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STATS 245.3 - Mathematics and Statistics

... The branch of statistics referred to as experimental design deals specifically with the collection of data. The branch of statistics referred to as descriptive statistics deals with summarizing the data after its been collected. The branch of statistics referred to as inferential statistics deals wi ...
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105 Descriptive statistics in ICT education: A practical and real

< 1 ... 191 192 193 194 195 196 197 198 199 ... 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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