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A variant-EGT is then (partially) created to resolve the question of moves’ time-wasting status. Comment on the data’s modality (i.e., bimodal,trimodal, etc.). (c) What is the midrange of the data? (d) Can you find (roughly) the first quartile (Q1) and the third quartile (Q3) of the data? (e) Give the five-number summary of the data. (f) Show a boxplot of the data. (g) How is a quantile–quantile plot different from a quantile plot? > DATA2.2 <- c(19, 20, 20, 23, 25, 25, 29, 29, 29, 29, 33, 35, 35, 38, 38, 38, 40, 42, 46, 48, 53, 75, 79, 80) > stat.desc(DATA2.2) > boxplot(DATA2.2, main="DATA TUPLES", ylab="AGE") (a) Calculate the mean, median, and standard deviation of age and %fat. (b) Draw the boxplots for age and %fat. (c) Draw a scatter plot and a q-q plot based on these two variables. > AGE <-c(23,23,27,27,39,41,47,49,50,52, + 54,54,56,57,58,58,60,61) > FAT_PER<-c(9.5,26.5,7.8,17.8,31.4,25.9,27.4,27.2, + 31.2,34.6,42.5,28.8,33.4,30.2,34.1,32.9,41.2,35.7) > plot(AGE, FAT_PER, main="SCATTER PLOT AGE VS FAT%", + xlab="AGE", ylab="FAT PERCENTAGE") # regression line (FAT_PER~AGE) > abline(lm(FAT_PER~AGE), col="red") # lowess line (AGE,FAT_PER) > lines(lowess(FAT_PER~AGE), col="blue") > plot(SORT_AGE, SORT_FAT, main="QQ PLOT AGE VS FAT%", + xlab="AGE", ylab="FAT PERCENTAGE") # regression line (SORT_FAT_PER~SORT_AGE) > abline(lm(SORT_FAT~SORT_AGE), col="red") # lowess line (SORT_AGE,SORT_FAT_PER) > lines(lowess(SORT_FAT~SORT_AGE), col="blue") > dist(rbind(x, y), method = "euclidean") Given two objects represented by the tuples (5, 1,22, 7) and (10, 0, 20, 4): (a) Compute the Euclidean distance between the two objects. (b) Compute the Manhattan distance between the two objects. (c) Compute the Minkowski distance between the two objects, using q D 3. (d) Compute the supremum distance between the two objects. > i <- c(5, 1,22, 7) > j <- c(10, 0, 20, 4) > dist(rbind(i, j), method = "euclidean") > dist(rbind(i, j), method = "manhattan") > dist(rbind(i, j), method = "minkowski") > dist(rbind(i, j), method = "maximum") a.

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Thomas W. Miller

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Premium Solver Pro gives you both Solver dialogs familiar from the Excel Solver, and a new Ribbon and Task Pane user interface, used in all of Frontline’s other Excel products. Some estimates have this total between 150 and 200 billion right now. The US 2009 Teradata User Group Conference & Expo, October 18–22, 2009, at the Gaylord National Resort. This information can directly link the products purchased with an individual. Depending on the medium in which the original data is contained, and how it is to be stored and used later, there can be great differences in input methods.... [tags: data ] Descriptive Statistics: Raw Data - Several things can be done to the raw data in order to see what they can say about the hypotheses (Neuman, 2003).

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Therefore it is imperative for students to enroll (available once logged in in Moodle with their @polytechnique.edu account using the enrollment key specified in the welcoming email they received). Big data centers around data brokers, who buy information that private companies collect about ordinary people, much of which is obtained after customers hit the “I agree” button online or use a store loyalty card that records usage.

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This is accomplished by demonstrating the what-if outcomes of small changes. It has already permeated private day-care centers and preschools; pilot testing in publicly funded preschools and kindergartens is currently taking place. Datasets of crisis-related social media messages. With recent advancements in social measurement techniques, we can now calculate one’s “digital footprint” in the social media world.

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Contains data in USD and tracks original currency. To combat this problem researchers have sought to measure the usefulness or interestingness of rules. ML-Flex uses machine-learning algorithms to derive models from independent variables, with the purpose of predicting the values of a dependent (class) variable. mlpy is a Python module for Machine Learning built on top of NumPy/SciPy and the GNU Scientific Libraries. mlpy provides a wide range of state-of-the-art machine learning methods for supervised and unsupervised problems and it is aimed at finding a reasonable compromise among modularity, maintainability, reproducibility, usability and efficiency.

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It also provides benchmarking information, comparing the client's financial performance with industry peers. Prior to the September 2002 installation of KXEN Analytic Framework, Cox was suing tools such as Oracle�s Discoverer and SQL Navigator. Additionally, the data warehouse contains views that are used to support the data mining scenarios that are described later in this topic. During the use of the LAN, several attacks were performed on it. Robotics engineers develop algorithms that can handle noisy sensor data, learn models of the environment, and make decisions in real time.

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Another application of periodic pattern mining is stock market analysis. Currently, this serves as an interface for the registration of BioProjects and BioSamples and submission of data for WGS and GTR. Optus is an Australian telecommunications carrier that uses analytics to increase customer retention. A growing number of data scientists are using GPUs for big data analytics to make better, real-time business decisions. Also there was one prediction model applied, where as with more tested they could have found that a different model generated better results with less error across the US and within the tested region.

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Hsinchun Chen

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These discovered patterns then can be used to classify other data where the right group designation for the target attribute is unknown (though other attributes may be known). The web is too huge. − The size of the web is very huge and rapidly increasing. Its underlying goal is to help humans make high-level sense of large volumes of low-level data, and share that knowledge with colleagues in related fields. Any new product can be matched to the old simply by being in the same price range.

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It contains customer data for an insurance company. Big data showcases such as Google Flu Trends failed to deliver good predictions in recent years, overstating the flu outbreaks by a factor of two. In fact, big data flows so abundantly that we choose water-themed metaphors to describe it: data lake, data flood, data tsunami, oceans of data, streaming data, and even the CD sea of data. In my free time, I like to run, draw, ski and watch movies (and I also like to make movies).

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Of all the people who bought fishing or hunting licenses the first year, only half came back. In this bit representation, the two leftmost bits represent the attribute A1 and A2, respectively. S. borders — were believed to be private. Most neural networks learn by applying some sort of training rule that adjusts the synaptic connections between neurons in various layers of the network on the basis of presented patterns. With four times the capacity of Premium Solver Pro built in, you can solve nonlinear models 10 times larger than the Excel Solver, and linear models 40 times larger!