State pedagogical University Faculty of Foreign Languages and Literature English Philology Department Diploma Paper ’’Activities that stimulate writing” Submitted by: the VI d year student …... 2013 Contents: Introduction………………………………………………...……..3 Chapter I. Preliminary Peculiarities of Writing Activities. 1.1 Teaching Writing in ESL Classes……………...………........5 1.2 Speaking and Writing .Written Versus Spoken Text...................6 1.3 The Process of Writing:
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| Public speaking has been taught and studied for thousands of years. | | | | | A) | True | | | | | | B) | False | | | | Feedback: Public speaking has been taught and studied around the world for thousands of years. | | | | 2 | CORRECT | | Because people have different frames of reference‚ a public speaker must take care to adapt her or his message to the particular audience being addressed. | | | | | A) | True | | | | | | B) | False |
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Use K-Means for Clustering 1. Dataset For this tutorial‚ we will work on some unlabeled data from the US Census Bureau. The following introduction to this dataset is for you to learn about its attributes and interpret results: Attributes of the raw data is discretized to have less attribute values‚ which is the data we are seeing now. Attributes description of the raw data attributes is at: http://archive.ics.uci.edu/ml/databases/census1990/USCensus1990raw.attributes.txt Some attributes are kept
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thus it is expected that future studies will develop statistical models which consider both time and clustering effects to calculate the power of trials. Considering other factors - such as the number of randomization steps – which can influence the trial’s outcomes‚ is beneficial to increase the accuracy of the presented inferences. Due to the dependency of the intervention effect on time and clustering‚ proposing a method to extract the intervention effects are highly dependent on the trial’s characteristics
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weakness of the model. Another aspect that makes Hotelling’s concept weak is that it fails to recognize the existence of clustering economies‚ his concept/model sees clustering as a negative thing.
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TECHNICAL WRITING DEFINITION: • Technical writing is your act of communicating‚ through writing‚ whatever you think and feel about your job‚ business‚ industry‚ profession‚ and organization. • “On-the-job writing” • Traditionally‚ technical writing is mainly producing written outputs about these fields of knowledge: Science‚ Technology and Engineering. • Etymology: ▪ technikos (Greek)‚ techne (French) – art‚ craft or skill ▪ teks (Indo-European) – to weave or
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Customer Segmentation and Analysis of a Mobile Telecommunication Company of Pakistan using Two Phase Clustering algorithm Salar Masood‚ Moaz Ali‚ Faryal Arshad‚ Ali Mustafa Qamar‚ Aatif Kamal Department of Computing School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad‚ Pakistan {09bitsmasood‚ 09bitmoaza‚ 09bitfarshad‚ mustafa.qamar‚ aatif.kamal}@seecs.edu.pk Abstract—Pakistan hosts a competitive and fluid telecommunication
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case for this‚ we identified the common routings in the highly varied product mix based on history production data. Thereafter‚ these routings are clustered into routing families by means of the discrete clustering method and the similarity coefficient algorithm. Finally‚ the results of the clustering methods are used to identify the routings that are suitable for cellular manufacturing. Routing mix analysis The first step in converting a work area into a manufacturing cell is to assess the current
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Illustrate Naïve Bayes Model of statistical learning 16. Write short notes on :- a) Naïve Bayes Model b) EM algorithm c) Statistical Learning Model 17. Describe different unsupervised learning 18. What is clustering? Describe K-Mean clustering 19. What is learning with complete data. Give Examples. 20. What is learning by analogy 21. What is Learning with hidden data . Give Examples. 22. Difference between Learning with complete and learning with hidden
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patient population is analysed to perform medical research. We propose a system which allows us to obtain data patterns with the help of clustering algorithms. In this paper we have experimented on data gathered from Community Health Centre hospital which surveys the people from various area of Ponda Goa‚ India. This medical data is then analysed using the clustering algorithms like K-means & CLIQUE. K-means algorithm reveals the percentage of a particular disease in the surveyed areas & also finds
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