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Scoring Guidelines - AP Central
Scoring Guidelines - AP Central

chapters. - Project MOSAIC
chapters. - Project MOSAIC

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ppt8

... s Known There is a 1 -  probability that the value of a sample mean will provide a margin of error of z /2 s x or less. ...
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... the observational units are the students in your class. Both of these are numbers that provide summary information about the class as a whole. They do not vary from student to student. g. If you record the average student height or percentage of student cell phone usage by class taught at your schoo ...
Elementary Statistics on the TI-83 and TI-84
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... separate from the boxplot (See Figure 4.2(b) on the next page). This is because 25 lies outside the interval (−3.75, 14.25). The value of 9 is the largest that lies inside this interval. So we have identified 25 as an outlier. ...
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... treated differently during statistical analysis. If you don’t assign codes for missing values, even nonsensical values are accepted. A value of -1 for age would be considered a real age. The missing-value codes that you assign to a variable are called usermissing values. System-missing values are as ...
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... EDF option, and they include Kolmogorov-Smirnov statistics and Cramer-von Mises statistics. In the preceding example, the CLASS variable Dose has five levels, and the analyses examines possible differences among these five levels, or samples. The following statements invoke the NPAR1WAY procedure to ...
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... heterogeneous rescaling of coordinate variables. This can be of practical concern in applications involving coordinates with differing measurement scales. As pointed out by Chakraborty (2001, p. 391), we would like the outlyingness measure of a data point not to depend on the choice of coordinate sy ...
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Chapter 1 Descriptive Statistics for Financial Data

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Summarizing data - Creighton University

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... find patterns otherwise masked by perceptual error. With the advent of more powerful hardware and software, statistical graphics is certain to see rapid growth in the near future. One factor driving this will be the incredible increase in the amount of information available as a result of scientific ...
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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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