sum of squares of error of full model r – no. of variables dropped from full model. 16. Outliers Measure | Potential Outliers | Standardized residual‚ Studentized residual | > 3 (3 sigma level) | Mahalanobis distance | > Critical chi-square value with df = number of explanatory variables(Outliers in independent variable) | Cook’s distance | > 1 implies potential outlier | Leverage values | > 2(k+1)/n‚ then the point is influential (k is the number of independent
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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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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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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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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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Johnnie Cochran: An Outlier By: Ryan Starr Johnnie Cochran was an infamous American lawyer‚ who gained recognition from his highly publicized and controversial cases as a successful defense attorney. Born as an African-American on October 2‚ 1937 in Shreveport‚ Louisiana‚ Cochran grew up facing extreme racial prejudice and learned valuable life experience at a young age (Cochran Biography 1). Turning a deaf ear to discrimination‚ Cochran did well in school and got good grades. His father and
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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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Overview: Chapter 2 Data Mining for Business Intelligence Shmueli‚ Patel & Bruce Core Ideas in Data Mining Classification Prediction Association Rules Data Reduction Data Visualization and exploration Two types of methods: Supervised and Unsupervised learning Supervised Learning Goal: Predict a single “target” or “outcome” variable Training data from which the algorithm “learns” – value of the outcome of interest is known Apply to test data where value is not known and will be predicted
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errors are also likely Outliers and anomalies distort the mean of the data taking it to either of the two extremes. To avoid any Outliers or anomalies affecting the accuracy of this study‚ I will remove them before taking the sample size of around 80-100 students and I will be using stratified sampling so each category categorized by gender‚ age and maths set have a equal proportion in the sample as in the total population so the results are as accurate as possible. Any outliers which I may have missed
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entries) of 2270‚ is only very slightly larger than the median (the data at the middle of the sample)‚ and the mode (the data entry that occurs with the greatest frequency) which are both the same at 2207. This represents a very slight affect by the outliers at the high and low ends of the data sample‚ indicating that the mean presents the most accurate description of the data set. (Larson & Farber‚ 2011. pgs. 66‚ 67‚ 68). [See Exhibit A]. The range of the data set‚ 3138‚ represents the difference
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