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    Cognitive (COG)‚ Affective (AFFECT) and Behavioral (BEHAVE) should be scale variables. The scale of these variables is changed before the analysis. 4 Perform Initial Data Screening. What did you find regarding missing values‚ univariate outliers‚ multivariate outliers‚ normality? a. What should you consider when you find these kinds of outcomes? HINTS: For missing values‚ see Case Processing Summary Univariate

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    there are some potential outliers. For an item to be considered a potential outlier in this experiment it has to be greater or less than three standard deviations from the mean diameter for each round. In the experimental group‚ the potential outlier for round 3 is 36 mm and those for round 4 are 25 mm‚ 27 mm and 27 mm. In the control groups‚ there is one potential outlier for round 1‚ which is 20 mm‚ one potential outlier for round 3‚ which is 9 mm and two potential outliers for round four‚ which are

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    Bipolar Disorder a. I’m so Distracted! b. Accepting the Bipolar c. Some People with Bipolar Disorder may even Hear voices d. Living with Bipolar Disorder VI. Bipolar Disorder Effects to his/her individual/surrounding A. Three Geniuses with Mental Illnesses B. Recent Studies about Bipolar Disorder a. Serious Mental Illness Tied to Higher Cancer‚ Injury Risk: Studies b. The Natural Law and Human Behavior c. Mental Illness Alone Not Linked to Violence 1. Risk

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    Semi-Supervised K-Means Clustering for Outlier Detection in Mammogram Classification K. Thangavel1‚ A. Kaja Mohideen2 Department of Computer Science‚ Periyar University‚ Salem‚ India 1 drktvelu@yahoo.com‚ 2kaja.akm@gmail.com Abstract— Detection of outliers and relevant features are the most important process before classification. In this paper‚ a novel semi-supervised k-means clustering is proposed for outlier detection in mammogram classification. Initially the shape features are extracted

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    Stella Omusa is another interesting case study. As she pursued early schooling‚ Stella never had any idea of her orientation. Even her parents and peers could not help Stella on the same. Luckily‚ as a bright student‚ it was not difficult to secure admission into a public university. However‚ it is not clear whether Stella pursued a degree in bachelor of commerce‚ accounting option by choice or just as a matter of natural flow of events. Most likely though‚ it was the course she was selected to

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    an example of discrete data is the number of animals. I am using quantitative data which has numerical values rather than qualitative data such as colors. This makes it easier to analyze the data and come to a conclusion. I will also be excluding outliers and anomalies which make my data more representative. The process is to collect data from a population of 264 animals including 19 mammals and 31 amphibians because it is neither large nor small and therefore giving me a clear concise result of the

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    Institut f. Statistik u. Wahrscheinlichkeitstheorie 1040 Wien‚ Wiedner Hauptstr. 8-10/107 AUSTRIA http://www.statistik.tuwien.ac.at Benefits from using continuous rating scales in online survey research H. Treiblmaier and P. Filzmoser Forschungsbericht SM-2009-4 November 2009 Kontakt: P.Filzmoser@tuwien.ac.at Benefits from Using Continuous Rating Scales in Online Survey Research Horst Treiblmaier* Institute for Management Information Systems Vienna University of Economics and Business

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    only rage and violence” (174)‚ said Skipper Dunn‚ one of his ex-roommates. He was aggressive‚ violent‚ and got into trouble with the police several

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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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    CHAPTER 4 – THE BASIS OF STATISTICAL TESTING * samples and populations * population – everyone in a specified target group rather than a specific region * sample – a selection of individuals from the population * sampling * simple random sampling – identify all the people in the target population and then randomly select the number that you need for your research * extremely difficult‚ time-consuming‚ expensive * cluster sampling – identify

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