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    1) What is the difference between probability distributions and frequency distributions? Provide an example that demonstrates the difference between the two.  A probability distribution directly corresponds to a frequency distribution‚ except that it is based on theory (probability theory)‚ rather than on what is observed in the real world (empirical data). A frequency distribution is based on actual observations. An example would be observing a coin be flipped twenty times. A probability distribution

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    Rebecca Verian 2577648 Context: π 1: There is no significant difference between the mean heights of the singers. π2: The average of heights of high pitched voices is equal. π3: The average of heights of low pitched voices is equal. Conditions: * There are 39 cases. * Based on the graph gotten the average heights of Soprano are 64.25 inches. * The average heights of the Alto singers are 64.88 inches. * The Tenor singers had an average height of 69.15 inches. * The mean

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    Key Synthesis/Potential Test Questions (PTQs) • What is statistics? Making an inference about a population from a sample. • What is the logic that allows you to be 95% confident that the confidence interval contains the population parameter? We know from the CLT that sample means are normally distributed around the real population mean (). Any time you have a sample mean within E (margin of error) of then the confidence interval will contain . Since 95% of the sample means are within E

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    AP Statistics Semester Review Chapters 1- 16 Name___________________________________ 1) One of the reasons that the Monitoring the Future (MTF) project was started was "to study changes in the beliefs‚ attitudes‚ and behavior of young people in the United States." Data are collected from 8 th‚ 10th‚ and 12th graders each year. To get a representative nationwide sample‚ surveys are given to a randomly selected group of students. In Spring 2004‚ students were asked about alcohol‚ illegal drug‚ and

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    1. STAT10T 7.2.1-2 (Points: 5.0)     Solve the problem. Find the critical value zα/2 that corresponds to a degree of confidence of 91%. a. 1.645 b. 1.75 c. 1.34 d. 1.70 2. STAT10T 7.2.3-2 (Points: 5.0)     Solve the problem. The following confidence interval is obtained for a population proportion‚ p: 0.817 < p < 0.855 Use these confidence interval limits to find the point estimate‚ . a. 0.833 b. 0.817 c. 0.839 d. 0.836 3. STAT10T 7.2.4-3 (Points: 5

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    evidence to infer that the population mean is not equal to 180. c t x s/ n 175 180 60 / 200 1.18‚ p-value = .2400. There is not enough evidence to infer that the population mean is not equal to 180. 269 d. As the s increases‚ the test statistic increases and the p-value increases. 12.12 H0 : H0 : = 50 50 a Rejection region: t t x s/ n 52 50 15 / 25 t / 2‚n 1 t .05‚24 1.711 or t t / 2‚n 1 t .05‚24 1.711 .67‚ p-value = .5113. There is not enough evidence

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    There are several types of bad statistics that can be seen when looking at statistical data. According to the video “Don’t be fooled by bad statistics” (2010)‚ there are three basic types of bad data consisting of poorly collected data‚ leading questions‚ and misuse of center. Poorly collected data can produce misleading results. For example‚ when a publishing company conducted a phone survey of popular magazines but did so during business hours when stay at home moms were most likely to participate

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    45.16 + 2.3263 * 10 = 99% of households spent less than $68.42 NORMINV (.99‚ 45.16) References Levine‚ D.‚ Stephan‚ D.‚ Krehbiel‚ T.‚ & Berenson‚ M. (2008) Statistics for managers using Microsoft Excel w/cd. (5th ed.). Upper Saddle River‚

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    It is known that there are two data types that are utilized to evaluate and draw meaningful conclusions through statistics‚ population and sample data. These two data types are utilized to formulate end conclusions of data that is to be collected and data that is to be reviewed. The description of population data can best be explained‚ as the complete collection of all data that is to be queried/collected and reviewed. Sample data‚ a subset of population data‚ is the partial collection and review

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    How to validate root causes in a lean sigma approach Silvia Pederzolli Milan‚ the 15th of april 2013 attivaRes Define Opportunities Measure Performance Analyze Opportunity Improve Performance Control Performance CCR’S Objective • • • • • Identify problem statement: what is wrong and why. Deviation from what is expected (targeted performance). How much/how often Effects on Customers. Find and validate the root causes that assure the elimination of “real” root causes.

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