2011 8th International Conference on the European Energy Market (EEM) • 25-27 May 2011 • Zagreb‚ Croatia Electricity price forecasting – ARIMA model approach Tina Jakaša #1‚ Ivan Andročec #2‚ Petar Sprčić *3 Hrvatska elektroprivreda Ulica grada Vukovara 37‚ Zagreb‚ Croatia 2 # tina.jakasa@hep.hr ivan.androcec@hep.hr 1 * HEP Trade Ulica grada Vukovara 37‚ Zagreb‚ Croatia 2 petar.sprcic@hep.hr Abstract— Electricity price forecasting is becoming more important in everyday business
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Dear Dr. Ducking I am still not able to afford a book at this time. Also I did not make time to use the book in the reading room and the library only had the 3rd edition of the book. So I wrote a summary of the class notes that you had put up on blackboard. Chapter 3 Summaries: Chapter 3 talks about descriptive statistics with numerical measures. These measures consist of location and variability. The measures of locations are mean‚ median‚ mode‚ weighted mean‚ geometric mean
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tried to get across to us in his book Outliers and articles such as “Most Likely To Succeed” and “Late Bloomers”. The main point that the book and articles revolved around was; what is success‚ and how to define the successful ones. With the spaces between his words you would start to realize that at certain times‚ Gladwell contradicts himself‚ and at the end of the paragraph he would leave you with a question to think about. As I continue to read Outliers and the two articles it made me question
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Film is a large multi-billion dollar industry in the USA and worldwide overall. Compared to other industries it has a very unique characteristic. For example when it comes to the real estate‚ fashion‚ cars consumer cost will always reflect the cost of manufacturing. If Nissan spends less on manufacturing a car‚ it will charge less than what Rolls Royce would. This rule doesn’t hold in the movie industry. When a person goes to a movie theater they have a wide variety of choices and the price for
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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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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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Overall‚ I thoroughly enjoyed both‚ The Grapes of Wrath‚ as well as‚ Their Eyes Were Watching God. While The Grapes of Wrath focuses more on the suffering of people in America during the Great Depression‚ Their Eyes Were Watching God fixates further upon the struggles of specific people. Even so‚ despite obvious differences in the plots and the writing‚ I was able to find several similarities amongst the two stories. Similarities such as‚ parallels between the main characters‚ the appearances of
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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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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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