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Year Industry Dec 31, 2008 0.5045 Dec 31, 2009 0.5519 Dec 31, 2010 0.6088 Dec 31, 2011 0.5882 Dec 31, 2012 0.5661 Dec 31, 2013 0.5272 Dec 31, 2014 0.4818 Dec 31, 2015 0.4504 Dec 31, 2016 0.4547 Dec 31, 2017 0.4878 Dec 31, 2018 0.4847

Industry increased exponentially in 2011, however dropped to an all time low in 2016, before slowly recovering.

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Year Industry Dec 31, 2008 0.5045 Dec 31, 2009 0.5519 Dec 31, 2010 0.6088 Dec 31, 2011 0.5882 Dec 31, 2012 0.5661 Dec 31, 2013 0.5272 Dec 31, 2014 0.4818 Dec 31, 2015 0.4504 Dec 31, 2016 0.4547 Dec 31, 2017 0.4878 Dec 31, 2018 0.4847

GDP peaked in 2011 then continued to fall until 2016. It then slightly increased upto 2019.

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Year Industry Dec 31, 2008 0.5045 Dec 31, 2009 0.5519 Dec 31, 2010 0.6088 Dec 31, 2011 0.5882 Dec 31, 2012 0.5661 Dec 31, 2013 0.5272 Dec 31, 2014 0.4818 Dec 31, 2015 0.4504 Dec 31, 2016 0.4547 Dec 31, 2017 0.4878 Dec 31, 2018 0.4847

A sharp increase from 2009 to 2011 but that leads to a slow decline over the next five years which plateaus and starts to rise again into 2018.

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Year Industry Dec 31, 2008 0.5045 Dec 31, 2009 0.5519 Dec 31, 2010 0.6088 Dec 31, 2011 0.5882 Dec 31, 2012 0.5661 Dec 31, 2013 0.5272 Dec 31, 2014 0.4818 Dec 31, 2015 0.4504 Dec 31, 2016 0.4547 Dec 31, 2017 0.4878 Dec 31, 2018 0.4847

There is a steady increase up to 2011 then a decrease up to 2016 then levels out in 2018.

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Year Services Dec 31, 2008 0.3682 Dec 31, 2009 0.3243 Dec 31, 2010 0.2752 Dec 31, 2011 0.2957 Dec 31, 2012 0.3186 Dec 31, 2013 0.3535 Dec 31, 2014 0.3903 Dec 31, 2015 0.4174 Dec 31, 2016 0.4265 Dec 31, 2017 0.4063 Dec 31, 2018 0.4011

this graph shows the share of gabons gdp the services is taking is increasing over the years. in the first few years it was falling. however from 2011 onwards it as been on an upwards trend.

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Year 0-14 years Dec 31, 2008 0.1668 Dec 31, 2009 0.1649 Dec 31, 2010 0.1642 Dec 31, 2011 0.163 Dec 31, 2012 0.1617 Dec 31, 2013 0.1606 Dec 31, 2014 0.1599 Dec 31, 2015 0.159 Dec 31, 2016 0.1587 Dec 31, 2017 0.1587 Dec 31, 2018 0.1585

There are fewer people between the ages of 0-14 years in Canada in 2019, than there were in 2009.

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Response Traditional agencies with $3m to $10m revenue Familiarization trips 0.58 Educational programs 0.48 Email updates on special offers 0.51 Overrides / incentives 0.57 Agent portion of websites 0.34 Consumer referrals 0.2 Co-op marketing efforts 0.29 Co-op advertising 0.34 Trade publication offers 0.12 Mail updates on special offers 0.1 Social media co-op 0.07

The most popular being 0.6 and least popular is social medial at 0.05.

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Home-based agents with $250,000 plus revenue Response 0.65 Familiarization trips 0.56 Educational programs 0.46 Email updates on special offers 0.49 Overrides / incentives 0.39 Agent portion of websites 0.3 Consumer referrals 0.19 Co-op marketing efforts 0.14 Co-op advertising 0.13 Trade publication offers 0.12 Mail updates on special offers 0.08 Social media co-op

The support service with the highest agency size is 'Familiarization trips' with agency size of 0.65. The support service with the lowest agency size is 'Social Media Co-op' with an agency size of approximately 0.08. This is an extremely badly arranged chart. The units on the x axis have not been clearly defined.

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Month 2016 De 223.1; Month: Dec No 222.8; Month: Nov Oc 201.9; Month: Oct Se 232.3; Month: Sep Au 248.5; Month: Aug Ju 263.6; Month: Jul Ju 228.1; Month: Jun Ma 210.3; Month: May Ap 203.8; Month: Apr Ma 194.3; Month: Mar Fe 203.7; Month: Feb Ja 208.2; Month: Jan

July had the highest rise in value exceeding 250. March has the lowest drop in value falling under 200.

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Operational capacity region of Italy 6156 Lombardy 6050 Campania 6446 Sicily 5213 Lazio 3938 Piedmont 2683 Apulia 3100 Tuscany 2995 Emilia-Romagna 2710 Calabria 1915 Veneto 2611 Sardinia 1655 Abruzzo 1120 Liguria 1323 Umbria 846 Marche 464 Friuli-Venezia Giulia 497 Trentino-South Tyrol 407 Basilicata 268 Molise 177 Aosta Valley

Sicily is way ahead of the other areas . campanula and Mombardy are close followers.

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Operational capacity region of Italy 6156 Lombardy 6050 Campania 6446 Sicily 5213 Lazio 3938 Piedmont 2683 Apulia 3100 Tuscany 2995 Emilia-Romagna 2710 Calabria 1915 Veneto 2611 Sardinia 1655 Abruzzo 1120 Liguria 1323 Umbria 846 Marche 464 Friuli-Venezia Giulia 497 Trentino-South Tyrol 407 Basilicata 268 Molise 177 Aosta Valley

In 2020, Sicily had the highest prison capacity in Italy, followed closely by Lombardy and Campania. These regions each had a capacity of over 6000. The Aosta Valley had the lowest prison capacity. Ten regions had a prison capacity of over 2000. Six regions had a capacity of under 1000.

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Current population region of Italy 7569 Lombardy 6422 Campania 5816 Sicily 5803 Lazio 4235 Piedmont 3463 Apulia 3229 Tuscany 3071 Emilia-Romagna 2603 Calabria 2286 Veneto 2051 Sardinia 1753 Abruzzo 1443 Liguria 1361 Umbria 875 Marche 612 Friuli-Venezia Giulia 392 Trentino-South Tyrol 387 Basilicata 372 Molise 178 Aosta Valley

The Lombardy region has the most prison capacity and population at nearly 8000. This contrasts with Aosta valley which seems to have substantially less than 1000. It does show that each region showed has a prison population and capacity.

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Month Pasta Apr 6-12 1.4 Mar 30-Apr 5 1.76 Mar 23-29 2.67 Mar 16-22 3.5 Mar 9-15 2.73 Mar 2-8 2.09 Feb 24-Mar 1 0.7 Feb 17-23 0.21 Feb 10-16 0.48 Feb 3-9 0.61

pasta sales were highest in russia in march 16 22 lowest in feb 10 16.

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Toilet paper Month 0.58 Apr 6-12 1.11 Mar 30-Apr 5 1.62 Mar 23-29 2.78 Mar 16-22 1.85 Mar 9-15 1.25 Mar 2-8 0.34 Feb 24-Mar 1 0.19 Feb 17-23 0.21 Feb 10-16 0.22 Feb 3-9

Overall, March had the highest online sales for toilet paper during the COVID-19 pandemic. March 16-22nd shows the highest spike for online sales overall from dates Feb 3 - April 12th 2020.

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Month Laundry detergents Apr 6-12 0.91 Mar 30-Apr 5 1.28 Mar 23-29 1.59 Mar 16-22 2.11 Mar 9-15 1.48 Mar 2-8 1.11 Feb 24-Mar 1 0.57 Feb 17-23 0.57 Feb 10-16 0.94 Feb 3-9 0.9

The only trend I can observe is that from March onwards Laundry Detergents was sold as a much higher rate from this point onwards reach over 2.0, and kept higher than the months before March which was low than 1.0 so you can notice the 100% increase after March.

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e-commerce category March to February 2020 Household appliances and electronics 2.7 Household chemistry 2.16 Supplements 0.52 Clothing 0.63 Courier services 0.53 Game Streaming 0.76 Perfume 0.81 Marketplaces 0.37 Online gambling 0.22 Financial support (eg. charity, funding) 0.35 Food delivery 0.17 Bookstore 0.85 TV Streaming 0.47 Mobile services 0.14 Pharmacies 0.35 Bookmaker's betting −0.26 Tickets/transport −0.53

Household appliances were the most popular products purchased in online stores. Tickets/Transport was the least popular category during the period.

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Response William Hill Feb 28, 2009 2238 Feb 28, 2010 2263 Feb 28, 2011 2350 Feb 29, 2012 2320 Feb 28, 2013 2345 Feb 28, 2014 2382 Feb 28, 2015 2308 Feb 29, 2016 2326 Feb 28, 2017 2379 Feb 28, 2018 2298 Feb 28, 2019 2264

William Hill had the least amount of betting shops in years 2009 and 2019.

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Response Betfred** Feb 28, 2009 806 Feb 28, 2010 829 Feb 28, 2011 840 Feb 29, 2012 1345 Feb 28, 2013 1369 Feb 28, 2014 1383 Feb 28, 2015 1375 Feb 29, 2016 1366 Feb 28, 2017 1637 Feb 28, 2018 1667 Feb 28, 2019 1620

The amount of betting shops has been increasing, there was one slight decrease in 2016 and 2019 shows the amount is decreasing. The amount of shops 2012 to 2016 remained fairly steady. There was a sharp increase in shops 2011 to 2012 and from 2016 to 2017.

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2012 Numerical control (NC) software companies 0.737 Vero Software 0.761 Delcam 0.45 Tebis 0.331 CGTech 0.287 SolidCAM 0.086 SPRING Technologies 0.038 ICAM 0.025 NCCS 0.012 SPRUT Technology 0.013 SmartCAMcnc

Delcam had the highest revenue in 2012. No company had a revenue higher than 0.8 million in 2012. Half of the companies here had revenue less than 0.2 million.

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2012 Numerical control (NC) software companies 0.737 Vero Software 0.761 Delcam 0.45 Tebis 0.331 CGTech 0.287 SolidCAM 0.086 SPRING Technologies 0.038 ICAM 0.025 NCCS 0.012 SPRUT Technology 0.013 SmartCAMcnc

The chart shows that the company Delcam had the highest revenue for the period and that SmartCAMCNC and SPRUT Technology were joint for the lowest revenue.

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Numerical control (NC) software companies 2013 Vero Softwar 1.1; Numerical control (NC) software companies: Vero Software Delca 0.852; Numerical control (NC) software companies: Delcam Tebi 0.48; Numerical control (NC) software companies: Tebis CGTec 0.378; Numerical control (NC) software companies: CGTech SolidCA 0.36; Numerical control (NC) software companies: SolidCAM SPRING Technologie 0.094; Numerical control (NC) software companies: SPRING Technologies ICA 0.04; Numerical control (NC) software companies: ICAM NCC 0.027; Numerical control (NC) software companies: NCCS SPRUT Technolog 0.018; Numerical control (NC) software companies: SPRUT Technology SmartCAMcn 0.015; Numerical control (NC) software companies: SmartCAMcnc

Vero Software achieved the most revenue of over 1 million US dollars, whilst SPRUT Technology and SmartCAM cnc achieved the least at just $100,000.

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Numerical control (NC) software companies 2013 Vero Softwar 1.1; Numerical control (NC) software companies: Vero Software Delca 0.852; Numerical control (NC) software companies: Delcam Tebi 0.48; Numerical control (NC) software companies: Tebis CGTec 0.378; Numerical control (NC) software companies: CGTech SolidCA 0.36; Numerical control (NC) software companies: SolidCAM SPRING Technologie 0.094; Numerical control (NC) software companies: SPRING Technologies ICA 0.04; Numerical control (NC) software companies: ICAM NCC 0.027; Numerical control (NC) software companies: NCCS SPRUT Technolog 0.018; Numerical control (NC) software companies: SPRUT Technology SmartCAMcn 0.015; Numerical control (NC) software companies: SmartCAMcnc

revenue is divided in 2 types, bad performing (3rd till 7th on the x-axis) and good performing companies (1. and second and the last 3 on x-axis)Vero Software and Delcam outperform all the others.

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Year Latin America 2025* 45 2024* 32 2023* 31 2022* 30 2021* 29 2020* 28 2019* 26 2018 24 2017 23 2016 21 2015 20 2014 21 2013 23 2012 23 2011 21

Subscriptions peak in 2018 and are predicted to continue to steadily rise year on year to 2024. In 2025 it is predicted to increase by over 10 million subscriptionsthe lowest year for subscription is 2015.

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North East Asia Year 77 2025* 69 2024* 67 2023* 65 2022* 63 2021* 60 2020* 57 2019* 55 2018 53 2017 50 2016 50 2015 52 2014 53 2013 52 2012 49 2011

The number of cellular subscriptions was projected to continue an increasing trend from 2019 to 2025 in north east Asia. the 2011 level of cellular subscriptions is expected to increase by up to 50% in 2025.

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Year 0-14 years Dec 31, 2008 0.2249 Dec 31, 2009 0.222 Dec 31, 2010 0.2192 Dec 31, 2011 0.2168 Dec 31, 2012 0.2148 Dec 31, 2013 0.2129 Dec 31, 2014 0.2109 Dec 31, 2015 0.2094 Dec 31, 2016 0.2078 Dec 31, 2017 0.2061 Dec 31, 2018 0.2046

From 2009 to 2019 the percentage of 0-14 year olds has been slowly declining from 0.26 to 0.2.

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Year Immigration Dec 31, 2009 128813 Dec 31, 2010 131869 Dec 31, 2011 123037 Dec 31, 2012 119034 Dec 31, 2013 124347 Dec 31, 2014 124557 Dec 31, 2015 123550 Dec 31, 2016 128058 Dec 31, 2017 133729 Dec 31, 2018 144756

Migration flow increased in Belgium from 2016-2018. Overall, migration flow in Belgium has increased from 2010-2018.

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Year Immigration Dec 31, 2009 128813 Dec 31, 2010 131869 Dec 31, 2011 123037 Dec 31, 2012 119034 Dec 31, 2013 124347 Dec 31, 2014 124557 Dec 31, 2015 123550 Dec 31, 2016 128058 Dec 31, 2017 133729 Dec 31, 2018 144756

The migration flow in Belgium generally remained consistent from 2010-2016, after this time the amount of immigration increased to its highest point in at least 9 years. From 2010-2019, the lowest point for migration flow in Belgium was 2013.

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Year Rental property Dec 31, 2005 23917 Dec 31, 2006 23743 Dec 31, 2007 23741 Dec 31, 2008 25951 Dec 31, 2009 19397 Dec 31, 2010 17681 Dec 31, 2011 15484 Dec 31, 2012 10726 Dec 31, 2013 13704 Dec 31, 2014 17591 Dec 31, 2015 15846

Rental property building permits saw a significant fall from 25000 in 2009 to 11000 in 2013. From 2013 to 2015, the building permits for rental properties increased from 11000 to around 18000, but has since then seen a small decline.

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Year Owner-occupied property Dec 31, 2005 72530 Dec 31, 2006 64175 Dec 31, 2007 63457 Dec 31, 2008 46695 Dec 31, 2009 41631 Dec 31, 2010 38123 Dec 31, 2011 21886 Dec 31, 2012 15458 Dec 31, 2013 25661 Dec 31, 2014 35942 Dec 31, 2015 35151

Between 2006 and 2012 building permits steeply declined. Building permits then slowly increased until they leveled off a few years after 2014.

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Country Asylum seekers Peru 482571 United States 104989 Brazil 103657 Spain 33960 Ecuador 14442 Trinidad and Tobago 14296 Colombia 8824 Mexico 8614 Costa Rica 6809 Panama 3724 Chile 2768 Argentina 2718 Canada 1388 Germany 923 France 676 Italy 580

Most asylum seekers sought refuge in Peru of almost 500,000. Brazil and the USA both had a similar amount of asylum seekers, both just under 100,000.

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Country Asylum seekers Peru 482571 United States 104989 Brazil 103657 Spain 33960 Ecuador 14442 Trinidad and Tobago 14296 Colombia 8824 Mexico 8614 Costa Rica 6809 Panama 3724 Chile 2768 Argentina 2718 Canada 1388 Germany 923 France 676 Italy 580

Peru has by had the most refugee applications with nearly 500,000 applications. In a distance second is jointly Brazil and the USA with just over 100,000 applications and finally Spain has had circa 30,000 applications. Other countries are shown on the graph but have had minimal applications in comparison showing where the trend for applications lie with the aforementioned countries.

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Country Asylum seekers Peru 482571 United States 104989 Brazil 103657 Spain 33960 Ecuador 14442 Trinidad and Tobago 14296 Colombia 8824 Mexico 8614 Costa Rica 6809 Panama 3724 Chile 2768 Argentina 2718 Canada 1388 Germany 923 France 676 Italy 580

Peru had the most amount on asylum seekers. Italy had the least amount of asylum seekers. Argentina and the United States have about the same amount of asylum seekers. Three EU countries have a very low amount of asylum seekers.

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Refugees Country 1230 Peru 15706 United States 20941 Brazil 35243 Spain 374 Ecuador 2040 Trinidad and Tobago 425 Colombia 8781 Mexico 577 Costa Rica 61 Panama 14 Chile 309 Argentina 3067 Canada 320 Germany 551 France 1988 Italy

Spain are leading the way in taking refugees. South American countries put together still take less refugees than Spain.

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1990 type of service 0.129 General hospital, specialty units 0.029 General hospital, nonspecialty care 0.237 Specialty hospitals 0.086 Psychiatrists 0.044 Nonpsychiatric physicians 0.059 Other professionals 0.124 Freestanding nursing homes 0.004 Freestanding home health 0.152 Specialty mental health centers 0 Specialty substance abuse centers 0.086 Retail prescription medication 0.05 Insurance administration

Specialty hospital was the service using the highest mental health expenditures in the United States in 1990. The second highest was specialty mental health centers.

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1990 type of service 0.129 General hospital, specialty units 0.029 General hospital, nonspecialty care 0.237 Specialty hospitals 0.086 Psychiatrists 0.044 Nonpsychiatric physicians 0.059 Other professionals 0.124 Freestanding nursing homes 0.004 Freestanding home health 0.152 Specialty mental health centers 0 Specialty substance abuse centers 0.086 Retail prescription medication 0.05 Insurance administration

Specialty hospitals had the greatest expenditures in 1990. Freestanding home health had the least expenditures in 1990. Specialty substance abuse centres had no expenditures in 1990. Specialty mental health centres had greater expenditure that both freestanding nursing homes and general hospital, speciality units.

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Year 65 years and older Dec 31, 2008 0.0406 Dec 31, 2009 0.0412 Dec 31, 2010 0.0405 Dec 31, 2011 0.0398 Dec 31, 2012 0.039 Dec 31, 2013 0.0382 Dec 31, 2014 0.0374 Dec 31, 2015 0.037 Dec 31, 2016 0.0367 Dec 31, 2017 0.0364 Dec 31, 2018 0.0361

The ratio of people who are 65 years and older in Namibia reached a peak in 2010 during the period 2009-2019, however it has been gradually decreasing since 2010.

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Year 65 years and older Dec 31, 2008 0.0406 Dec 31, 2009 0.0412 Dec 31, 2010 0.0405 Dec 31, 2011 0.0398 Dec 31, 2012 0.039 Dec 31, 2013 0.0382 Dec 31, 2014 0.0374 Dec 31, 2015 0.037 Dec 31, 2016 0.0367 Dec 31, 2017 0.0364 Dec 31, 2018 0.0361

The average age in Namibia is declining- this may be because more babies are being born and less childhood mortality or may be due to increased mortality in over 65s.

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QinetiQ Group PLC Year 0.603 2019/20 0.584 2018/19 0.62 2017/18 0.617 2016/17 0.679 2015/16 0.686 2014/15 0.6 2013/14** 0.338 2012/13 0.293 2011/12 0.277 2010/11 0.352 2009/10 0.351 2008/09 0.412 2007/08 0.477 2006/07 0.587 2005/06

Between 2007/2008 and 2010/2011 there was a marked decline in defence contractors being dependent on MOD business in the UK.

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QinetiQ Group PLC Year 0.603 2019/20 0.584 2018/19 0.62 2017/18 0.617 2016/17 0.679 2015/16 0.686 2014/15 0.6 2013/14** 0.338 2012/13 0.293 2011/12 0.277 2010/11 0.352 2009/10 0.351 2008/09 0.412 2007/08 0.477 2006/07 0.587 2005/06

Between 2005/06 and 2019/20 dependency of QinetiQ Group PLC on MOD business varied somewhat between around 0.3 and above 0.6. Years 2014/15 and 2015/16 showed the highest level of dependency of QinetiQ Group PLC on MOD business with values of above 0.6 in both cases. Dependency of QinetiQ Group PLC on MOD business fell to its lowest point in 2010/11, at below 0.3.

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Babcock International Group PLC Year 0.502 2019/20 0.417 2018/19 0.384 2017/18 0.372 2016/17 0.372 2015/16 0.372 2014/15 0.395 2013/14** 0.44 2012/13 0.438 2011/12 0.49 2010/11 0.467 2009/10 0.481 2008/09 0.574 2007/08 0.42 2006/07 0.476 2005/06

Babcock International Group's dependency on MoD supplier contracts account for a small proportion of their business, averaging about 0.4% between the period from 2005 to 2020. It slightly increased in 2007/8 but fell to average levels in the following years.

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BAE Systems PLC Year 0.2 2019/20 0.201 2018/19 0.191 2017/18 0.205 2016/17 0.219 2015/16 0.228 2014/15 0.206 2013/14** 0.202 2012/13 0.174 2011/12 0.156 2010/11 0.192 2009/10 0.195 2008/09 0.206 2007/08 0.221 2006/07 0.222 2005/06

There is no explanation of what the x axis measurement scale is - is it percentage of overall business?. The chart appears to show the BAE systems dependency on MOD business peaked in 2014/15.

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BAE Systems PLC Year 0.2 2019/20 0.201 2018/19 0.191 2017/18 0.205 2016/17 0.219 2015/16 0.228 2014/15 0.206 2013/14** 0.202 2012/13 0.174 2011/12 0.156 2010/11 0.192 2009/10 0.195 2008/09 0.206 2007/08 0.221 2006/07 0.222 2005/06

2010/11 shows the least dependency on suppliers for ministry of defence.

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Rolls-Royce Holdings PLC Year 0.054 2019/20 0.056 2018/19 0.054 2017/18 0.051 2016/17 0.06 2015/16 0.055 2014/15 0.052 2013/14** 0.048 2012/13 0.061 2011/12 0.049 2010/11 0.057 2009/10 0.056 2008/09 0.063 2007/08 0.052 2006/07 0.058 2005/06

Rolls Royce were most dependent on MOD business in 2007/8. they were least dependent on MOD business in 2012/3. Only two years have been above 0.06. All years have been above 0.04.

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Rolls-Royce Holdings PLC Year 0.054 2019/20 0.056 2018/19 0.054 2017/18 0.051 2016/17 0.06 2015/16 0.055 2014/15 0.052 2013/14** 0.048 2012/13 0.061 2011/12 0.049 2010/11 0.057 2009/10 0.056 2008/09 0.063 2007/08 0.052 2006/07 0.058 2005/06

Holdings have been consistent year after year from 2006 to 2020.

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Year Airbus Group SE 2019/20 0.015 2018/19 0.015 2017/18 0.016 2016/17 0.015 2015/16 0.016 2014/15 0.019 2013/14** 0.018 2012/13 0.014 2011/12 0.018 2010/11 0.014 2009/10 0.014 2008/09 0.014 2007/08 0.015 2006/07 0.012 2005/06 0.014

Airbus Groups SE had its largest year of business dependecy in 2014/2015. Airbus Groups SE had its lowest year of business dependecy in 2006/2007Between 2008 and 2011 Airbus Group SE business deoeency on the MOD was linear.

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Ultimately approved disease areas 0.89 Oncology 0.93 Allergy 0.94 Respiratory 0.85 Cardiovascular 0.92 Infectious disease 0.82 Urology 0.86 Autoimmune 0.83 Metabolic 0.73 Ophthalmology 0.86 All diseases 0.9 Hematology 0.92 Gastroenterology 0.83 Endocrine 0.81 Neurology 0.91 Psychiatry

Ophthalmology has the lowest percentage of approved drugs in the US during the time period the chart represents. The maximum percentage of approved drugs is 1% and the lowest is approximately 0.7%. The approval rate overall is very low.

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Ultimately approved disease areas 0.89 Oncology 0.93 Allergy 0.94 Respiratory 0.85 Cardiovascular 0.92 Infectious disease 0.82 Urology 0.86 Autoimmune 0.83 Metabolic 0.73 Ophthalmology 0.86 All diseases 0.9 Hematology 0.92 Gastroenterology 0.83 Endocrine 0.81 Neurology 0.91 Psychiatry

On average the approval rate for all drugs is about 0.8/0.85%. The area with the most drugs approved is respiratory drugs.

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Year Americas Dec 31, 2007 89.5 Dec 31, 2008 81.2 Dec 31, 2009 107.6 Dec 31, 2010 118.5 Dec 31, 2011 136.7 Dec 31, 2012 141.9 Dec 31, 2013 142.5 Dec 31, 2014 167.2 Dec 31, 2015 174.2 Dec 31, 2016 190 Dec 31, 2017 216.4 Dec 31, 2018 227.6

The sales of teslas between the years 2008 and 2019 in America is increasing.

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Year State Dec 31, 1990 0 Dec 31, 1991 0 Dec 31, 1992 0 Dec 31, 1993 0 Dec 31, 1994 2 Dec 31, 1995 2 Dec 31, 1996 0 Dec 31, 1997 3 Dec 31, 1998 42 Dec 31, 1999 85 Dec 31, 2000 0 Dec 31, 2001 0 Dec 31, 2002 6 Dec 31, 2003 39 Dec 31, 2004 22 Dec 31, 2005 104 Dec 31, 2006 32 Dec 31, 2007 307 Dec 31, 2008 410 Dec 31, 2009 527 Dec 31, 2010 625 Dec 31, 2011 793 Dec 31, 2012 377 Dec 31, 2013 287 Dec 31, 2014 137

Financial penalties for the pharmaceutical industry in the usa began to peak circa 2008 according to the graph.

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Year Federal Dec 31, 1990 10 Dec 31, 1991 22 Dec 31, 1992 1 Dec 31, 1993 0 Dec 31, 1994 8 Dec 31, 1995 5 Dec 31, 1996 4 Dec 31, 1997 0 Dec 31, 1998 58 Dec 31, 1999 404 Dec 31, 2000 889 Dec 31, 2001 549 Dec 31, 2002 961 Dec 31, 2003 960 Dec 31, 2004 1045 Dec 31, 2005 3872 Dec 31, 2006 1410 Dec 31, 2007 1122 Dec 31, 2008 4002 Dec 31, 2009 3812 Dec 31, 2010 1712 Dec 31, 2011 5555 Dec 31, 2012 3123 Dec 31, 2013 356 Dec 31, 2014 2071

Federally started to grow around 2000 and had major spikes around 2005 and late 2010 or 2011.

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Year Agriculture Dec 31, 2009 0.0377 Dec 31, 2010 0.0368 Dec 31, 2011 0.0369 Dec 31, 2012 0.036 Dec 31, 2013 0.0364 Dec 31, 2014 0.0375 Dec 31, 2015 0.0388 Dec 31, 2016 0.0378 Dec 31, 2017 0.0376 Dec 31, 2018 0.0368 Dec 31, 2019 0.0361

The percentage of Italy's workforce employed in agriculture peaked in 2016. The percentage of Italy's workforce employed in agriculture has been in decline since 2016. The percentage of Italy's workforce employed in agriculture is now lower than it has been in ten years.

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Year Industry Dec 31, 2009 0.2861 Dec 31, 2010 0.2829 Dec 31, 2011 0.2758 Dec 31, 2012 0.2705 Dec 31, 2013 0.269 Dec 31, 2014 0.266 Dec 31, 2015 0.2612 Dec 31, 2016 0.26 Dec 31, 2017 0.261 Dec 31, 2018 0.2587 Dec 31, 2019 0.2561

There is a slight downward trend in the data between 0.30 and 0.25 industry, during the time period.

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Year Industry Dec 31, 2009 0.2861 Dec 31, 2010 0.2829 Dec 31, 2011 0.2758 Dec 31, 2012 0.2705 Dec 31, 2013 0.269 Dec 31, 2014 0.266 Dec 31, 2015 0.2612 Dec 31, 2016 0.26 Dec 31, 2017 0.261 Dec 31, 2018 0.2587 Dec 31, 2019 0.2561

The distribution of workforce decreased from 0.29 in 2010 to 0.26 in 2020.

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Year Industry Dec 31, 2009 0.2861 Dec 31, 2010 0.2829 Dec 31, 2011 0.2758 Dec 31, 2012 0.2705 Dec 31, 2013 0.269 Dec 31, 2014 0.266 Dec 31, 2015 0.2612 Dec 31, 2016 0.26 Dec 31, 2017 0.261 Dec 31, 2018 0.2587 Dec 31, 2019 0.2561

The y axis (industry) is shown in increments of 0.05 while the x axis (year) is in increments of 2 years the line shows a gradual workforce decrease of approximately 0.025 over 10 years. The measurement of the workforce (e.g. %) is not shown.

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Year Services Dec 31, 2009 0.6762 Dec 31, 2010 0.6803 Dec 31, 2011 0.6872 Dec 31, 2012 0.6935 Dec 31, 2013 0.6946 Dec 31, 2014 0.6965 Dec 31, 2015 0.6999 Dec 31, 2016 0.7021 Dec 31, 2017 0.7014 Dec 31, 2018 0.7044 Dec 31, 2019 0.7078

the distribution appears stable between 0.6-0.8there is a very slight upward trend.

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Year Services Dec 31, 2009 0.6762 Dec 31, 2010 0.6803 Dec 31, 2011 0.6872 Dec 31, 2012 0.6935 Dec 31, 2013 0.6946 Dec 31, 2014 0.6965 Dec 31, 2015 0.6999 Dec 31, 2016 0.7021 Dec 31, 2017 0.7014 Dec 31, 2018 0.7044 Dec 31, 2019 0.7078

The distribution of the workforce remained consistent throughout 2010 up to 2018.

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year Wireless (UMTS, LTE, WiMAX, WLAN) End 2018 0.989 Mid 2018 0.985 End 2017 0.979 Mid 2017 0.982 End 2016 0.981 Mid 2016 0.983 End 2015 0.982 Mid 2015 0.978 End 2014 0.977 Mid 2014 0.972 End 2013 0.969 Mid 2013 0.958 End 2012 0.945 Mid 2012 0.936 End 2011 0.92 Mid 2011 0.892 End 2010 0.881

Availability of broadband internet (at least 1 Mbit/s) gradually increased from end 2010, to end 2018, it then sharply decreased at start of 2019 and then continued to gradually increase till mid 2018.

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Year Food Dec 31, 1999 950 Dec 31, 2000 926 Dec 31, 2001 919 Dec 31, 2002 912 Dec 31, 2003 910 Dec 31, 2004 917 Dec 31, 2005 938 Dec 31, 2006 948 Dec 31, 2007 927 Dec 31, 2008 919 Dec 31, 2009 926 Dec 31, 2010 941 Dec 31, 2011 951 Dec 31, 2012 955 Dec 31, 2013 958 Dec 31, 2014 957 Dec 31, 2015 949 Dec 31, 2016 964 Dec 31, 2017 970 Dec 31, 2018 955

The domestic use of wheat has been stable over the past 15 years.

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Year Food Dec 31, 1999 950 Dec 31, 2000 926 Dec 31, 2001 919 Dec 31, 2002 912 Dec 31, 2003 910 Dec 31, 2004 917 Dec 31, 2005 938 Dec 31, 2006 948 Dec 31, 2007 927 Dec 31, 2008 919 Dec 31, 2009 926 Dec 31, 2010 941 Dec 31, 2011 951 Dec 31, 2012 955 Dec 31, 2013 958 Dec 31, 2014 957 Dec 31, 2015 949 Dec 31, 2016 964 Dec 31, 2017 970 Dec 31, 2018 955

the domestic use of wheat in the US from 2000 to 2019 has gradually increased a small amount, although it has sometimes dipped.

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2012 Name (Company) 37.5 Amancio Ortega (Inditex) 29.08 The Brenninkmeijer family (C&A) 41 Bernard Arnault (LVMH) 26 Stefan Persson (Hennes & Mauritz) 15.63 Betrand Puech & family (Hermes International) 11.5 Leonardo Del Vecchio (Luxottica) 13 Francis Pinault & family (PPR) 14.4 Phil Knight (Nike) 17.6 Michael Otto & family (Otto Group) 10 Tadashi Yanai & family (Fast Retailing, Uniqlo) 6.8 Miuccia Prada (Prada) 10.32 The Reimann family (Labelux Group) 7.2 Giorgio Armani (Armani) 7.1 Leonard Lauder (Estee Lauder) 7.6 Galen Weston & family (Selfridges Group) 7.5 Alain & Gerard Wertheimer (Chanel) 7.87 Isidoro Alvarez (El Corte Ingles) 7.5 Ralph Lauren (Ralph Lauren) 3.7 Patrizio Bertelli (Prada) 5.1 Johann Rupert & family (Compagnie Financiere Richemont)

The richest person in fashion worldwide in 2012 and 2013 was Bernard Arnault. The one with the lowest wealth was Patrizio Bertelli (Prada).

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2013 Name (Company) 57 Amancio Ortega (Inditex) 34.56 The Brenninkmeijer family (C&A) 29 Bernard Arnault (LVMH) 28 Stefan Persson (Hennes & Mauritz) 22.59 Betrand Puech & family (Hermes International) 15.3 Leonardo Del Vecchio (Luxottica) 15 Francis Pinault & family (PPR) 14.4 Phil Knight (Nike) 14.2 Michael Otto & family (Otto Group) 13.3 Tadashi Yanai & family (Fast Retailing, Uniqlo) 12.4 Miuccia Prada (Prada) 10.32 The Reimann family (Labelux Group) 8.5 Giorgio Armani (Armani) 8.1 Leonard Lauder (Estee Lauder) 8 Galen Weston & family (Selfridges Group) 8 Alain & Gerard Wertheimer (Chanel) 7.87 Isidoro Alvarez (El Corte Ingles) 7 Ralph Lauren (Ralph Lauren) 6.7 Patrizio Bertelli (Prada) 6.6 Johann Rupert & family (Compagnie Financiere Richemont)

Inditex is clear that they are the most richest in fashion, the next closes is C&A with just about half of Inditexs wealth. The majority, good 80% of the fashion companies are all under 10 billion.

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Race Smartphone Never arrange to meet someone that I have only met online 0.85 Not allowed to talk to strangers 0.84 Not allowed to share personal information 0.81 Tell my parents if something worries me 0.82 Not allowed to spend any money 0.75 Be respectful to others online 0.69 Only allowed on for a certain amount of time 0.5 Only allowed to use certain apps 0.52 Ask my parent's permission first 0.37 Not allowed to download apps 0.21 Not allowed to use it on my own 0.11 Other 0.04

The x axis some of the race doesn't have the full quotes. the chart again fluctuates this is because everyone's parents might have different ways of handling smartphones. for example majority of people would never arrange to meet someone which is at 0.8 smartphones then the lowest with roughly 0.0 is other, this means the choice isn't on the chart for them to decide from. only allowed on phones for a certain time and only allowed to use certain apps are close together with roughly 0.5.

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Race Smartphone Never arrange to meet someone that I have only met online 0.85 Not allowed to talk to strangers 0.84 Not allowed to share personal information 0.81 Tell my parents if something worries me 0.82 Not allowed to spend any money 0.75 Be respectful to others online 0.69 Only allowed on for a certain amount of time 0.5 Only allowed to use certain apps 0.52 Ask my parent's permission first 0.37 Not allowed to download apps 0.21 Not allowed to use it on my own 0.11 Other 0.04

Excluding other, "not allowed to use it on my own" has the lowest smartphone value. "Never arrange to meet someone t..." has the highest smartphone value. All race values had a smartphone value higher than zero.

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Brussels-Capital Region Year 0 0-4 0 5-9 1 10-14 1 15-19 3 20-24 4 25-29 1 30-34 0 35-39 1 40-44 0 45-49 3 50-54 1 55-59 1 60-64 1 65-69 2 70-74 1 75 and older 0 Information unavailable

In 2019, there were more car accident fatality in the age between 25 to 29 then any other age group.

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Brussels-Capital Region Year 0 0-4 0 5-9 1 10-14 1 15-19 3 20-24 4 25-29 1 30-34 0 35-39 1 40-44 0 45-49 3 50-54 1 55-59 1 60-64 1 65-69 2 70-74 1 75 and older 0 Information unavailable

People aged between 20 - 29 were most likely to be killed in traffic accidents. A total of 7 were in these age groups. Three people died in traffic accidents between age group 50 - 54. 2 people died in the age group 70 - 74. No one below 9 died. No one died between 35- 39 or between 45 - 49. In other age groups, one person died.

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10-14 & 5-9-year-olds involved in crashes have the least fatalities for their age groups. 20-24 have the most fatalities overall.

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Never Response 0.12 Paying online purchases from your bank account 0.4 Credit card payment verification likeVerified by Visa or Mastercard Secure Code 0.36 Kela, the Finnish Social Insurance Institution 0.24 Verohallinto (the Finnish Tax office) 0.38 Insurance company's services 0.49 Municipal healthcare services 0.54 Posti (the main Finnish postal service) 0.56 Other governmental services 0.71 Trafi (Finnish Transport Safety Agency) 0.68 Other municipal services 0.69 Väestörekisterikeskus (the Population Register Centre)

Government run services seems to ask for credit card credentials more frequently.

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Response Rarely Paying online purchases from your bank account 0.44 Credit card payment verification likeVerified by Visa or Mastercard Secure Code 0.35 Kela, the Finnish Social Insurance Institution 0.43 Verohallinto (the Finnish Tax office) 0.61 Insurance company's services 0.47 Municipal healthcare services 0.41 Posti (the main Finnish postal service) 0.37 Other governmental services 0.4 Trafi (Finnish Transport Safety Agency) 0.25 Other municipal services 0.28 Väestörekisterikeskus (the Population Register Centre) 0.29

There seems to similar values for all categories with 1 group particular large rarely.

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Year of enterprise birth 4 years Dec 31, 2006 0.52 Dec 31, 2007 0.489 Dec 31, 2008 0.489 Dec 31, 2009 0.481 Dec 31, 2010 0.51 Dec 31, 2011 0.504 Dec 31, 2012 0.512 Dec 31, 2013 0.493 Dec 31, 2014 0 Dec 31, 2015 0 Dec 31, 2016 0

The line stops at 2014, indicating no new enterprises after that time.

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Year of enterprise birth 4 years Dec 31, 2006 0.52 Dec 31, 2007 0.489 Dec 31, 2008 0.489 Dec 31, 2009 0.481 Dec 31, 2010 0.51 Dec 31, 2011 0.504 Dec 31, 2012 0.512 Dec 31, 2013 0.493 Dec 31, 2014 0 Dec 31, 2015 0 Dec 31, 2016 0

The survival rate of enterprise remained steady from 2007 to 2014, where since then we have no more data.

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Year Industry Dec 31, 2008 0.1319 Dec 31, 2009 0.1195 Dec 31, 2010 0.1158 Dec 31, 2011 0.1131 Dec 31, 2012 0.1053 Dec 31, 2013 0.1032 Dec 31, 2014 0.1094 Dec 31, 2015 0.1101 Dec 31, 2016 0.1098 Dec 31, 2017 0.1015 Dec 31, 2018 0.0997

The highest industry was 0.13 in 2009. Then started to decline to a low of 0.10 in 2014. Again it started to rise until 2015 where it leveled out until 2017. Then a steady decline till 2019.

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Year Services Dec 31, 2008 0.7088 Dec 31, 2009 0.7387 Dec 31, 2010 0.752 Dec 31, 2011 0.7566 Dec 31, 2012 0.7494 Dec 31, 2013 0.749 Dec 31, 2014 0.7391 Dec 31, 2015 0.739 Dec 31, 2016 0.7479 Dec 31, 2017 0.7521 Dec 31, 2018 0.7518

The graph indicates that share of economic sectors in GDP has remained relatively stable between 0.7 and 0.8 since 2012. GDP steadily increased until 2012 before levelling out, dropping slightly around 2015 but maintaining consistency. In the time shown on the chart, GDP has not dropped below 0.6 or exceeded 0.8.

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Year Services Dec 31, 2008 0.7088 Dec 31, 2009 0.7387 Dec 31, 2010 0.752 Dec 31, 2011 0.7566 Dec 31, 2012 0.7494 Dec 31, 2013 0.749 Dec 31, 2014 0.7391 Dec 31, 2015 0.739 Dec 31, 2016 0.7479 Dec 31, 2017 0.7521 Dec 31, 2018 0.7518

The sharpest increase was from 2009 to 2010. Between 2010 and 2019 the GDP has remained fairly constant. The overall variance between 2009 and 2019 is approx 0.05.

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Year Inspector General Dec 31, 2009 36 Dec 31, 2010 36 Dec 31, 2011 38 Dec 31, 2012 35 Dec 31, 2013 38 Dec 31, 2014 37 Dec 31, 2015 37 Dec 31, 2016 37.9 Dec 31, 2017 39 Dec 31, 2018 39.3 Dec 31, 2019 41.7

Inspector General has risen from approximately 36 million in 2010 to 41 million in 2020. It dipped in 2013 and 2015 from the previous years but other than this has grown each year.

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Year Spaceflight Science Dec 31, 2009 4498 Dec 31, 2010 4919 Dec 31, 2011 4193 Dec 31, 2012 4782 Dec 31, 2013 5148 Dec 31, 2014 5243 Dec 31, 2015 5583 Dec 31, 2016 5762.2 Dec 31, 2017 6221.5 Dec 31, 2018 6905.7 Dec 31, 2019 6393.7

The overall trend is one of increase. I do not understand the y-axis, however, and what Spaceflight Science means. If this is a breakdown of budget, should there not be a measure of that instead?.

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Year Spaceflight Science Dec 31, 2009 4498 Dec 31, 2010 4919 Dec 31, 2011 4193 Dec 31, 2012 4782 Dec 31, 2013 5148 Dec 31, 2014 5243 Dec 31, 2015 5583 Dec 31, 2016 5762.2 Dec 31, 2017 6221.5 Dec 31, 2018 6905.7 Dec 31, 2019 6393.7

2012-2019 sees increasesSlight reduction observed in 2020.

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Year Space Exploration Dec 31, 2009 3777 Dec 31, 2010 3928 Dec 31, 2011 3715 Dec 31, 2012 3706 Dec 31, 2013 4113 Dec 31, 2014 4367 Dec 31, 2015 4014 Dec 31, 2016 4324 Dec 31, 2017 4790 Dec 31, 2018 5050.8 Dec 31, 2019 6396.4

The diagram shows an upward trend between 2010 and 2020, although it is not a steady trend. Spend stayed steady between 2010 and 2014 at just under 4,000 million dollars. Spending increased in 2015 above 4,000 then reduced in 2016 to 4,000. From then on, there is a steady increase year-on-year, with an expectation that spend will go above 6,000 by 2020.

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Year Aeronautics Dec 31, 2009 497 Dec 31, 2010 534 Dec 31, 2011 569 Dec 31, 2012 530 Dec 31, 2013 566 Dec 31, 2014 642 Dec 31, 2015 634 Dec 31, 2016 656 Dec 31, 2017 690 Dec 31, 2018 725 Dec 31, 2019 666.9

The budget increased between 2010 and 2020, from 500 million, to 700 million. 2013, the amount decrease from the previous year. 2015, the amount decreased from the previous year.

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Year Education & STEM Engagement Dec 31, 2009 181 Dec 31, 2010 146 Dec 31, 2011 136 Dec 31, 2012 116 Dec 31, 2013 117 Dec 31, 2014 119 Dec 31, 2015 115 Dec 31, 2016 100 Dec 31, 2017 100 Dec 31, 2018 110 Dec 31, 2019 0

The budget for Education & STEM Engagement gradually decreased overall between 2010 and 2018. In ~2019 there was an extremely sharp decline from $110 million spent to $0.

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Year Space & Exploration Technology Dec 31, 2009 0 Dec 31, 2010 0 Dec 31, 2011 548 Dec 31, 2012 615 Dec 31, 2013 576 Dec 31, 2014 596 Dec 31, 2015 687 Dec 31, 2016 686.5 Dec 31, 2017 760 Dec 31, 2018 926.9 Dec 31, 2019 1146.3

The budget was relatively stable from 2012- 2017 after which there showed a marked increase. The budget For Space and Exploration Technology has doubled since 2012.

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Year Space & Exploration Technology Dec 31, 2009 0 Dec 31, 2010 0 Dec 31, 2011 548 Dec 31, 2012 615 Dec 31, 2013 576 Dec 31, 2014 596 Dec 31, 2015 687 Dec 31, 2016 686.5 Dec 31, 2017 760 Dec 31, 2018 926.9 Dec 31, 2019 1146.3

There is no budget recorded on the chart for the years 2010 to 2012. The chart shows erratic spending between 2012 and 2017 after which there was a sharp increase in the kast 3 years to 2020.

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Year Space & Exploration Technology Dec 31, 2009 0 Dec 31, 2010 0 Dec 31, 2011 548 Dec 31, 2012 615 Dec 31, 2013 576 Dec 31, 2014 596 Dec 31, 2015 687 Dec 31, 2016 686.5 Dec 31, 2017 760 Dec 31, 2018 926.9 Dec 31, 2019 1146.3

Apart from a slight reduction between 2013 and 2014, NASA's budget has grwon each year. The rate of growth increased significantly between 2018 and 2020.

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Year Single stores Dec 31, 1999 59876 Dec 31, 2000 67612 Dec 31, 2001 78395 Dec 31, 2002 76044 Dec 31, 2003 84770 Dec 31, 2004 84574 Dec 31, 2005 89957 Dec 31, 2006 90683 Dec 31, 2007 89567 Dec 31, 2008 90049 Dec 31, 2009 91815 Dec 31, 2010 93209 Dec 31, 2011 93819 Dec 31, 2012 95056 Dec 31, 2013 96318 Dec 31, 2014 97359 Dec 31, 2015 97504 Dec 31, 2016 97642

The number of convenience stores has risen at the end of the chart, however it has had some dips over the years where some stores may have shut or closed completely.

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Year Tobacco control spending Dec 31, 1997 262.3 Dec 31, 1998 345.4 Dec 31, 1999 436.6 Dec 31, 2000 782.3 Dec 31, 2001 820.9 Dec 31, 2002 736.7 Dec 31, 2003 610.8 Dec 31, 2004 625.5 Dec 31, 2005 638.1 Dec 31, 2006 670.5 Dec 31, 2007 778.9 Dec 31, 2008 735.3 Dec 31, 2009 641.1

Tobacco control spending peaked in 2002. Tobacco control spending increased from 1998 to 2002. Between 2002 and 2010 spending has remained at a similar level.

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Percentage of requests where some data produced half a year 44 H1 '13 50 H2 '13 53.99 H1 '14 57.71 H2 '14 78.78 H1 '15 79.63 H2 '15 82.87 H1 '16 84.48 H2 '16 85 H1 '17 87 H2 '17 88 H1 '18 84 H2 '18 83 H1 '19 82 H2 '19 83 H1 '20

The number of user data requests to Facebook grew over the years. The number of data requests reached a peak in 2017/2018.

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Year 15-64 years Dec 31, 2008 0.6134 Dec 31, 2009 0.6187 Dec 31, 2010 0.6215 Dec 31, 2011 0.624 Dec 31, 2012 0.6263 Dec 31, 2013 0.6286 Dec 31, 2014 0.6312 Dec 31, 2015 0.6332 Dec 31, 2016 0.6359 Dec 31, 2017 0.6391 Dec 31, 2018 0.6421

Let’s go missing like that age group between 15 to 64 which is it working age group has been increasing over time in the Philippines.

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Year 15-64 years Dec 31, 2008 0.6134 Dec 31, 2009 0.6187 Dec 31, 2010 0.6215 Dec 31, 2011 0.624 Dec 31, 2012 0.6263 Dec 31, 2013 0.6286 Dec 31, 2014 0.6312 Dec 31, 2015 0.6332 Dec 31, 2016 0.6359 Dec 31, 2017 0.6391 Dec 31, 2018 0.6421

The age structure in the Philippines from 2009 and 2019 shows a steadily increasing trend for 15-65 year olds over the time period.

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Year 65 years and older Dec 31, 2008 0.04 Dec 31, 2009 0.0414 Dec 31, 2010 0.0428 Dec 31, 2011 0.0431 Dec 31, 2012 0.044 Dec 31, 2013 0.0449 Dec 31, 2014 0.046 Dec 31, 2015 0.0476 Dec 31, 2016 0.0493 Dec 31, 2017 0.0512 Dec 31, 2018 0.0531

The aging population of the Philippines is increasing with time.

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Year 65 years and older Dec 31, 2008 0.04 Dec 31, 2009 0.0414 Dec 31, 2010 0.0428 Dec 31, 2011 0.0431 Dec 31, 2012 0.044 Dec 31, 2013 0.0449 Dec 31, 2014 0.046 Dec 31, 2015 0.0476 Dec 31, 2016 0.0493 Dec 31, 2017 0.0512 Dec 31, 2018 0.0531

The population of over 65s has increased year on year. There was a levelling off around 2011 but growth has accelerated since.

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Year 65 years and older Dec 31, 2008 0.04 Dec 31, 2009 0.0414 Dec 31, 2010 0.0428 Dec 31, 2011 0.0431 Dec 31, 2012 0.044 Dec 31, 2013 0.0449 Dec 31, 2014 0.046 Dec 31, 2015 0.0476 Dec 31, 2016 0.0493 Dec 31, 2017 0.0512 Dec 31, 2018 0.0531

In the Philippines there is an increasingly ageing population. With rates of over 65s steadily increasing.

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Flight searches Month −0.06 March 16 −0.12 March 17 −0.09 March 18 −0.06 March 19 −0.05 March 20 −0.01 March 21 −0.14 March 22 −0.3 March 23 −0.37 March 24 −0.45 March 25 −0.44 March 26 −0.5 March 27 −0.49 March 28

The searches took a huge dip around the terror attacks, and then restored.

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Flight bookings Month −0.11 March 16 −0.16 March 17 −0.09 March 18 −0.07 March 19 −0.11 March 20 −0.1 March 21 −0.55 March 22 −0.52 March 23 −0.45 March 24 −0.55 March 25 −0.49 March 26 −0.62 March 27 −0.58 March 28

the highest flights booked is march 27ths whereas the lowest flights booked is march 19th. The number of flights booked fluctuate. from march 16th to march 21st is low ranging between 0.0 to 0.1. from march 21 to march 22 there is a big increase in flight books it goes from 0.1 on march 21 to roughly 0.5 on march 22 which is an 0.4 increase on flights.

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Country Commercial vehicles China 4360.47 United States 8367.24 Japan 1355.54 Germany* 0 India 892.68 Mexico 2604.08 South Korea 338.03 Brazil 496.5 Spain 574.34 France* 527.26 Thailand 1218.46 Canada 1455.22

This bar chart shows the huge difference in motor production between different countries around the world. It is clear from this data that the United States produced the most amount of vehicles in 2019 with a staggering 8000 vehicles, whereas Germany produced 0. There are also countries such as France, Brazil and South Korea who produced less than 500.

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Year Self-employed Dec 31, 1999 0.395 Dec 31, 2000 0.395 Dec 31, 2001 0.377 Dec 31, 2002 0.362 Dec 31, 2003 0.354 Dec 31, 2004 0.341 Dec 31, 2005 0.331 Dec 31, 2006 0.322 Dec 31, 2007 0.314 Dec 31, 2008 0.305 Dec 31, 2009 0.306 Dec 31, 2010 0.295 Dec 31, 2011 0.284 Dec 31, 2012 0.281 Dec 31, 2013 0.287 Dec 31, 2014 0.283 Dec 31, 2015 0.279 Dec 31, 2016 0.276 Dec 31, 2017 0.257 Dec 31, 2018 0.262

The chart shows self employed people in the year 2000 would work longer hours than the average self employed person in 2015.

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Year 15-64 years Dec 31, 2008 0.6581 Dec 31, 2009 0.6578 Dec 31, 2010 0.6565 Dec 31, 2011 0.6547 Dec 31, 2012 0.6526 Dec 31, 2013 0.6505 Dec 31, 2014 0.6485 Dec 31, 2015 0.6461 Dec 31, 2016 0.6438 Dec 31, 2017 0.6416 Dec 31, 2018 0.6394

Since 2010, the number of people with ages between 15-64 has been gradually declining.

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Year 65 years and older Dec 31, 2008 0.1727 Dec 31, 2009 0.1733 Dec 31, 2010 0.1743 Dec 31, 2011 0.1759 Dec 31, 2012 0.1779 Dec 31, 2013 0.1798 Dec 31, 2014 0.1814 Dec 31, 2015 0.1837 Dec 31, 2016 0.1858 Dec 31, 2017 0.1879 Dec 31, 2018 0.1901

There is a gradual increased number of people in Belgium between 0.15 and 0.20 over the age of 65 between the year 2010 and 2018.

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Response Handheld games console Dec 31, 2008 0.16 Dec 31, 2009 0.13 Dec 31, 2010 0.12 Dec 31, 2011 0.12 Dec 31, 2012 0.12 Dec 31, 2013 0.07 Dec 31, 2014 0.06 Dec 31, 2015 0.05 Dec 31, 2016 0.05 Dec 31, 2017 0.06 Dec 31, 2018 0.06

The use of handheld games console decreased over the years.

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Year EU Dec 31, 2003 9403019 Dec 31, 2004 11562795 Dec 31, 2005 12563752 Dec 31, 2006 13345066 Dec 31, 2007 12679656 Dec 31, 2008 14529010 Dec 31, 2009 9625281 Dec 31, 2010 9240505 Dec 31, 2011 8017103 Dec 31, 2012 7427804 Dec 31, 2013 6638237 Dec 31, 2014 5414694 Dec 31, 2015 5671047 Dec 31, 2016 6002313 Dec 31, 2017 7530029

Between 2004 and 2018 the highest value of chewing gum imports into the Uk was in 2009. The lowest was in 2015. It has risen steadily from 2015.

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Natural gas leading electric power producers 0.28 Duke 0.12 Exelon 0.48 Southern 0.47 NextEra Energy 0.19 Tennessee ValleyAuthority 0.4 Entergy 0.52 Dynergy 0.25 Berkshire HathawayEnergy 0.37 Dominion 0.1 AEP

Dynergy uses the most natural gas. AEP uses the least natural gas.

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Oil leading electric power producers 0.002 Duke 0.001 Exelon 0 Southern 0.003 NextEra Energy 0.001 Tennessee ValleyAuthority 0 Entergy 0.001 Dynergy 0.001 Berkshire HathawayEnergy 0.003 Dominion 0.002 AEP

During 2017, Dominion and NextEra Energy were the dominant producers of electricity generated in the US using oil. AEP and Duke generated two-thirds as much as the leaders, whilst Berkshire Hathaway Energy, Dynergy, Exelon and Tennessee Valley Authority produced one third as much as the two largest producers. Southern and Entergy did not generate any electricity using oil as the fuel source.