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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alternative hypothesis should state µ1 − µ2 > 0 ANSWER: c 2. A Type I error is committed when a. a true alternative hypothesis is not accepted b. a true null hypothesis is rejected c. the critical value is greater than the value of the test statistic d. sample data contradict the null hypothesis ANSWER: b In determining an interval estimate of a population mean when σ is unknown‚ we use a t distribution with a. n − 1 degrees of freedom 3. b. c. d. ANSWER: 4. n degrees of freedom n −
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Investigating Bottling Company Case Study T.P University Statistics Mat 300 Mr. Thevar December 01‚ 2013 Investigating Bottling Company Case Study The case study that is being investigated is for a bottling company producing less soda than what is advertised. Customers have complained that the sodas in the bottles contain less than the advertised sixteen ounces. The employees at the company have measured the amount of soda contained in each bottle. There are thirty bottles that have
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Executive Summary The purpose of an executive summary is to summarize a report. Executive summaries are written for executives who most likely do not have time to read the complete document. Therefore‚ the executive summary must cover the major points and be detailed enough to mirror the content yet concise enough for an executive to understand the substance without reading the entire report. An executive summary differs from an abstract. Readers use an abstract to decide whether to read the complete
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chapter 1: STATS – STATISTICS DATA AND STATISTICAL THINKING 1.1 The science of statistics * Statistics - is the science of data. It involves collecting‚ classifying‚ summarising‚ organising‚ analysing‚ and interpreting numerical information. 1.2 types of statistical applications in business * Descriptive Statistics - describe collected data. Utilizes numerical and graphical methods to look for patterns in data‚ summarize the information in the data and to present the information in a
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