Model rocketry is a hobby similar to building model airplanes‚ where rocket-shaped models are flown vertically and recovered by a variety of means. The rockets may vary greatly in size and complexity.According to the National Association of Rocketry (NAR) safety code‚ model rockets are constructed of paper‚ wood‚ plastic and other lightweight materials. The code also provides guidelines for motor use‚ launch site selection‚ launch methods‚ launcher placement‚ recovery system design and deployment
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.2.3 Time series models Time series is an ordered sequence of values of a variable at equally spaced time intervals. Time series occur frequently when looking at industrial data. The essential difference between modeling data via time series methods and the other methods is that Time series analysis accounts for the fact that data points taken over time may have an internal structure such as autocorrelation‚ trend or seasonal variation that should be accounted for. A Time-series model explains
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social medical models of health. Look at the effects and impact of each model. You will need to give valid examples. In the essay i will contrast the two models‚ looking at their impacts and effects. Also will look at which one is more helpful/effective in society. In relation to social perspective‚ Biomedical and socio-medical are both different from each other in terms of how they view society and the way they deal with health. Biomedical model approach to ill-health is that‚ when someone is sick
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Regression with Time Series Data Week 10 Main features of Time series Data Observations have temporal ordering Variables may have serial correlation‚ trends and seasonality Time series data are not a random sample because the observations in time series are collected from the same objects at different points in time For time series data‚ because MLR2 does not hold‚ the inference tools are valid under a set of strong assumptions (TS1-6) for finite samples While TS3-6 are often too restrictive
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Curve-Fitting Project – Linear Model: Average Sales Prices of new homes sold in the United States between 1964 and 2008 (LR-1) Purpose: To analyze the average sales prices of new homes sold in the United States from 1964 to 2008. Data: The prices were retrieved from http://www.census.gov/const/uspriceann.pdf. I chose to use the prices between 1964 and 2008 as they showed a huge increase (More data was available (see link)). Average sales prices of new homes sold in the US Year Time (seconds) 1964 $20
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As the operations manager of Colsam Company Limited‚ I have been tasked to make a presentation to management on the importance of forecasting. The presentation would be done along the following lines. * THE MEANING OF FORECASTING * STEPS USED TO DEVELOP A FORECASTING SYSTEM * QUALITATIVE FORECASTING * QUANTITATIVE FORECASTING * BENEFITS OF FORECASTING THE MEANING OF FORECASTING A planning tool that helps management in its attempts to cope with the uncertainty of the future
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report on the time-series analysis of continuously compounded returns for Ford and GM for the periods January 2002 till April 2007 using monthly stock prices. This analysis is aimed at estimating the ARIMA model that provides the best forecast for the series. This paper will be divided into 2 sections; the first section showing the Ford analysis and the second the GM analysis. Section 1: Ford Figure 1: Time series plot for raw Ford data. Figure 1 shows a time series plot of the
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Time Series Prediction of Earthquake Input by using Soft Computing Hitoshi FURUTA‚ Yasutoshi NOMURA Department of Informatics‚ Kansai University‚ Takatsuki‚ Osaka569-1095‚ Japan nomura@sc.kutc.kansai-u.ac.jp Abstract Time series analysis is one of important issues in science‚ engineering‚ and so on. Up to the present statistical methods[1] such as AR model[2] and Kalman filter[3] have been successfully applied‚ however‚ those statistical methods may have problems for solving highly nonlinear
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Sensitivity analysis‚ what-if analysis‚ and goal seeking analysis are used for same purpose to analyze complex relationships among millions of data items to discover business patterns‚ trends. All of them are part of Decision support systems (DSS) analysis techniques. It helps managers to explore possible alternatives. What-If Analysis: In what-if analysis‚ an end user makes changes to variables‚ or relationships among variables‚ and observes the resulting changes in the values of
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Relationship between a model and Similitude For a model‚ similitude is achieved when testing conditions are created such that the test results are applicable to the real design. There are some criteria that are required to achieve similitude; 1. Geometric similarity – The model is the same shape as the application (they are usually scaled). 2. Kinematic similarity – Fluid flow of both the model and real application must undergo similar time rates of change motions. (Fluid streamlines are similar)
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