results from single phenotype analyses‚ and performing multivariate analysis for quantitative phenotypes. These methods can be extended for related samples. The authors proposed new approach to jointly evaluate a set of binary phenotypes for related samples because of the following motivations. First‚ joint analysis of multiple phenotypes may provide a gain in power and an increased precision of estimates compared to the univariate analysis. Second‚ unlike a
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correlations among the various assets. Method showed how the variances of individual stock returns and the correlations of those returns can be combined to calculate a value for the variance of a portfolio made up of those stocks. Based on the received data of possible asset allocation we were able to draw an efficient frontier. The efficient frontier is the curve that shows all efficient portfolios in a risk-return framework. The general solution to the shortcomings of the Markowitz method for estimate
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The Brain Behind the Big‚ Bad Burger Section 1: Analysis Most Americans will consume any food regardless of the calories‚ nutritional value and health related consequences. The Brain behind the Big‚ Bad Burger article mentions the importance of using a Business Intelligence System (BIS) which “provides them with insights‚ not just mountains of data” (Levison‚ 2005). Business Intelligence gets its strength from being able to pull data from disparate sources store it for use in a loosely coupled
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Nadine Khoury 201510210 Models for Decision Making DCSN 300 Competing on Analytics by Thomas H. Davenport Analytics is the ability to collect and analyze data through a systematic approach with the objective to make the best decisions in a business. Due to its passed proven capacities and its huge potential to make a difference‚ Analytics has become much more than a tool: it is a “strategic weapon” in today’s Business context. Is Analytics a key element to success in Business today? Analytics is
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forecasting model(s) |Description |Qualitative Approach |Quantitative Approach | |Applicability |Used when situation is vague & little data exist |Used when situation is stable & historical data | | |(e.g.‚ new products and technologies) |exist | | | |(e
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the weather data (in weather.arff). For test options‚ first choose "Use training set"‚ then choose "Percentage Split" using default 66% percentage split. Report model percent error rate. ZeroR (majority class) OneR Naive Bayes Simple J4.8 C. Which of these classifiers are you more likely to trust when determining whether to play? Why? D. What can you say about accuracy when using training set data and when using a separate percentage to train? Assignment 2: Preparing the data and mining it
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Demand Forecasting. [Other Resource] Demand Forecasting System. ￭ Constructing Demand Forecasting System. 1. Determine the information that needs to be forecasted. ․ This includes defining the source of the historical data to be provided and the periods over which the data will be collected. 2. Assign responsibility for the forecast to a person which performance will be measured on the accuracy of actual sales to the forecast. 3. Setup forecast system parameters : ․ Forecast horizon.
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Groupon has built systems for merchants to track deal performance and analytics for demographic data and capacity. Groupon has also been spending to prevent counterfeit coupons by issuing the redeemable coupons with unique identifiers. Also‚ Groupon could use data analytics and business intelligence to collect and analyze the customer data. They also can use the predictive analysis techniques and tool on these data to determine the buying trend and give appropriate discounts to customers. I strongly
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This has been achieved here in 8 combinations of the tolerance level and scaled interval out of the 16 combinations considered. As such‚ one can infer that the variations in the tolerance level and the variations in scaling the data is not having a significant impact on the recognition accuracy when we employ RBF kernel with 3-fold cross validation. Table 6.8 contains the result of experiments when RBF kernel is used with 4-fold cross validation. Table – 6.8: Accuracy obtained
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Business Intelligence & Data Mining Assignment 3 – CELL2CELL Case 1. What are the Business Objective(s) and Data Mining Objective(s) for the case? Business Objectives To develop a proactive retention program (including incentive plans) to reduce the customer churn Data Mining Objectives 1. To predict churn accurately 2. To identify key factors that drive customer churn 2. Based on initial data understanding (Using multiplot/ statexplore node)‚ what are some initial obvious
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