Sigma Estimates Download UPD
Erinn Hickel
From my understanding, Poisson would limit sigma to between 0 and 1 but there can still be variation within that. However, its not unlikely that im incorrect as im quite new to mixed models and brms in general
- Total economic losses from disaster events in 2014 reached USD 113 billion worldwide
- Insured losses from natural catastrophes and man-made disasters totalled USD 34 billion in 2014, below recent annual averages
- Around 11 000 people lost their lives in natural catastrophe and man-made disaster events in 2014
According to preliminary sigma estimates, total economic losses from natural catastrophes and man-made disasters were USD 113 billion in 2014, down from USD 135 billion in 2013. Out of the total economic losses, insurers covered USD 34 billion in 2014, down 24% from USD 45 billion in 2013. This year disaster events have claimed around 11 000 lives.
Of the estimated total economic losses of USD 113 billion in 2014, natural catastrophes caused USD 106 billion, down from USD 126 billion in 2013. The outcome is well below the average annual USD 188 billion loss figure of the previous 10 years. The total loss of life of 11 000 from natural catastrophe and man-made disaster events this year is down from the more than 27 000 fatalities in 2013.
Insured losses for 2014 are estimated to be USD 34 billion, of which USD 29 billion were triggered by natural catastrophe events compared with USD 37 billion in 2013. Man-made disasters generated the additional USD 5 billion in insurance losses in 2014.
This year started with extreme winter conditions in the US and Japan and, as the year drew to a close, the Northeast US was once again gripped by very low temperatures and heavy snow. The storms in the US at the beginning of 2014 alone caused insured losses of USD 1.7 billion. This is above the average full-year winter storm loss number of USD 1.1 billion of the previous 10 years. In mid-May, a spate of strong storms with large hail stones hit many parts of the US over a five-day period, resulting in insured losses of USD 2.9 billion, the highest of the year.
The North Atlantic hurricane season was relatively mild again in 2014. No major hurricane made landfall in the US, the ninth year running that this has happened. However, Mexico was impacted by Hurricane Odile from the East Pacific in September. Strong winds and heavy rains resulted in insured losses of USD 1.6 billion, as Odile hit the Cabo San Lucas and other tourist resort areas in which there are a number of hotels and where commercial insurance penetration is relatively high. This made Hurriance Odile the second most costly catastrophe event in Mexico ever after Hurricane Wilma in 2005.
On the other side of the Pacific, the Philippines was once again hit by a typhoon at the beginning of December. Early loss estimates for Typhoon Hagupit indicate less damage than Typhoon Haiyan caused in 2013. Also, evacuation procedures based on lessons learned from the Haiyan experience have meant less loss of life than otherwise may have been.
In Europe, a series of small loss-inducing weather events hit different countries at the beginning of the year. One major event was wind and hail storm Ela in June, which caused significant damage to properties and vehicles in parts of France, Germany and Belgium, resulting in overall insured losses of USD 2.7 billion. Bulgaria was also hit by hail activity in June. Other severe weather events were heavy rains and flooding in the UK, Serbia, Croatia, Italy and France at different times during the year.
In Asia, monsoon rains in September brought extensive flooding and damage across India and Pakistan. These floods have claimed the largest number of lives of any flood event in 2014. The following month, India was hit by another extreme event, this time Cyclone Hudhud on the east coast.
Where there was excessive rain in some places, other areas of the world had little. For example, some areas in China had a very dry summer, leading to severe drought conditions that affected agricultural output. The loss estimates for these events are not yet known.
The estimates in this release include all latest updates to source data made by 28 November 2014. Ongoing events and revisions to estimates for previous ones may further change the 2014 loss outcomes. Estimates in USD terms for prior years are in 2014 prices.
Notes to editors
Accessing data by sigma:
The data from the study can be accessed and visualised at www.sigma-explorer.com.
This mobile enabled web-application allows users to create charts, share them via social media and export them as standard graphic files.
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Extremely flexible to your needs. Unlike other estimating software, Sigma allows you to build your own system through a very familiar excel interface. The modular structure of Sigma also allows you to quickly and seamlessly modify/add/delete anything in your quotations and even reference part or all of it for future estimates.
I love the scripting and the ability to "nest" assemblies within the system. This ability is so powerful and blows the doors off of anything else out there. The ability to import price lists and update libraries is critical to keeping your estimates up to date.
We maintain our construction estimates and quantity takeoffs at competitive market rates. Resultantly, offering our estimates and takeoffs at 200$ on average. (However, it can be less or more depending on the scope of the project)
Testing leads to data samples. There are many goals for testing data. Two common goals for project managers and Six-sigma practitioners are: finding the root cause of an issue by mapping the set of symptoms, or achieving execution conformance so that a desired outcome might be consistently achieved. Test samples might be random, scattered but correlated, grouped or clustered, linear or curvilinear, or related through more complex associations.
Using the three point estimating technique, our well trained project manager interviews the work-package owner or subject matter expert to obtain time or cost estimates. In return, the respondent provides their most optimistic (O), most pessimistic (P), and Most Likely (ML) estimates for work results.
One other formula that may be applied to the normal distribution is the ability to determine the standard deviation (σ) given only the Pessimistic (P) and Optimistic (O) estimates. Because of the even population distribution:
In other words, if I repeatedly take random samples from a normally distributed population and calculate sigma, then all my samplings of sigma should begin to form a distribution with the average estimate of sigma equaling the true sigma of the data. Improved estimates of sigma will have a tighter distribution of estimates from this repeated sampling than other methods.
where Xi is each individual estimate of sigma generated in the simulation and sigma (σ) is the true population standard deviation from which the random normal data was calculated. In this case, N is the number of total estimates in the simulation. This formula sums the absolute value of the difference between each estimate of sigma and the true sigma of the population and divides by the number of estimates. The higher the ADTS factor, the worse the estimation of sigma. (Note: The distribution of standard deviation is a chi-square distribution and is, therefore, not evenly balanced about the mean. This unbalance can be seen in some of the simulations, but was not always clearly observed.)
From all these samplings of the random data, I could then calculate the sigma using each of the methods. This gave me a set of data for each method of calculating sigma. A dot plot clearly shows the spread and frequency of each of the estimates of sigma. I could have used other displays of this data such as the classic histogram, but I felt that the dot plot improved the visual clarity of the data.
For example, the dot plot shown below in Figure 1 was created from selecting 1,000 five-piece samples out of 5,000 pieces of random normal data with mean = 0 and sigma = 1. The sigma was calculated for each of these 1,000 five-piece samples using the classic formula (Method A).
Calculating the ADTS factor for this set of sigma estimations gives us a value of 0.28. Values of ADTS can be compared to each other as long as the estimates of sigma come from the same population (same population mean and sigma).
Using the moving range (average MR/1.128) for an estimate of sigma is problematic as it requires that the data is in time order. If we have a small sample size of data and if the order of this data is either unknown or not relevant, then using MR to estimate sigma is not valid.
N < 10, the MR is a better estimate of sigma than the classic formula. I performed a test of this using a smaller subset of my normal population dataset and calculated sigma using both the classic formula and the MR. Figure 2 below shows that sigma calculated from MR gives no obvious advantage over the classic formula. The calculated ADTS values support this conclusion.
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