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The World Health Organization has declared the coronavirus a worldwide pandemic and individuals around the world are left wondering what they can do to help slow or stop the spread of this novel virus. Washing your hands, covering your sneezes/coughs, keeping social distances, and not going to work when you are sick are some of the measures being used to slow the spread. The emphasis on slowing the spread is two-fold. First, the overall number of infected cases would be vastly reduced which means less deaths around the world.

 Second, any healthcare system has a limited capacity to deal with infected individuals. A sharp reduction in the spread would allow healthcare providers such as hospitals and doctors to provide sufficient care to those infected. If the number of infected cases passes the capacity of a healthcare system, the spread of the virus may be accelerated. The term ‘flattening the curve’ shows the effect of an initial lower spread of infections.

 In simple terms, flattening the curve produces a lower number of overall infections by having an initial slower and eventual lower rate of infections compared to exponential growth that would occur if no measures are taken. The graph below, produced in Statgraphics 18, shows a simplified version of a flattened curve where the amount of infections was blunted. The graph below was not created using real data but rather artificial data to show the projected results of such a theory. However, there is no denying that taking measures to slow the spread of the virus will result in fewer deaths from this pandemic. 

 Flattening curve 18

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Statgraphics 18's Monte Carlo simulation is used to estimate the distribution of variables when it is impossible or impractical to determine that distribution theoretically in the real world. Professionals in wide array of fields such as engineering, finance, medical research to DFSS (Design for Six Sigma) use Monte Carlo Simulation to run a multitude of experiments without having to collect real data. Data scientists can also make significant changes to the simulation instantly and immediately discover new results. More information about Monte Carlo simulation is available on our website:

matrix 1 V18


Stream Now: Distribution Fitting for Arbitrarily Censored Data

Data collection in the real world can be challenging and very often data scientists will have incomplete or partial data sets. Our YouTube Channel holds dozens of free instructional videos including several hour-long webinars. The video below discusses the Statgraphics procedure for fitting data that have been censored and shows how users can still perform statistical tests and fits with limited or missing data sets. In fact, any of 27 probability distributions may be fit to data which include combinations of uncensored, left-censored, right-censored and interval censored values. The procedure calculates summary statistics, fits distributions, creates graphs, and calculates a nonparametric estimate of the survival function. Examples in this video are included in the areas of medical and environmental data analysis. Please visit our entire channel for more videos (Statgraphics TV).



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