On the Beat: How Alabama’s individual statistics in CFP title games stack up

On the Beat: How Alabama's individual statistics in CFP title games stack up statistics, nevin_manimala, mathematics, math, linkedin, google_plus
On the Beat: How Alabama's individual statistics in CFP title games stack up statistics, nevin_manimala, mathematics, math, linkedin, google_plus

It’s time for a little “Did you know?” action when it comes to the National Championship Game.

For example, did you know that …

• Linebacker Rashaan Evans has made the most tackles in the title games during the College Football Playoff era.

• A Crimson Tide quarterback has the best passer-efficiency rating in the CFP title games and it’s not Tua Tagovailoa.

• Former Alabama receiver ArDarius Stewart had more passing yards during the 2016 title game than Jalen Hurts had during the first half of last season’s championship game.

• Calvin Ridley and his brother, Riley, have the same number of career yards in the title game even though Calvin played in two more title games and had 9 more receptions.

Having been around only four years, the College Football Playoff and its National Championship Game are still new, but playing well in them is something that can secure a player’s legacy.

For example, the first thing one always hears about former NFL quarterback Terry Bradshaw is he has four Super Bowl rings.

However, his regular-season numbers weren’t that great. Bradshaw completed 51.9 percent of his passes, had 212 touchdown passes compared to 210 interceptions and was named to just three Pro Bowls.

But he was clutch. Bradshaw passed for more than 300 yards in a game only seven times, but three of them were in the playoffs, including two Super Bowls.

Here’s where Alabama players stand in what will eventually become the CFP record book.

SINGLE GAME
Passing
1] Deshaun Watson, Clemson, 2016, 36-56-0, 420
2] Deshaun Watson, Clemson, 2015, 30-47-1, 405
3] Jake Coker, Alabama, 2015, 16-25-0, 335
4] Marcus Mariota, Oregon, 2014, 24-37-1, 333
5] Cardale Jones, Ohio State, 2013, 16-23-1, 242
6] Jake Fromm, Georgia, 2017, 16-32-2 232
7] Tua Tagovailoa, Alabama, 2017, 14-24-166
8] Jalen Hurts, Alabama, 2016, 13-31-0 131
9] ArDarius Stewart, Alabama, 2016, 1-1-0 24
10] Jalen Hurts, Alabama, 2017, 3-8-0, 21

Quarterback rating
1] Jake Coker, Alabama, 2015, 203.0
2] Cardale Jones, Ohio State, 2014, 163.6
3] Deshaun Watson, Clemson, 2015, 160.0
4] Marcus Mariota, Oregon, 2014, 152.9
5] Tua Tagovailoa, Alabama, 2017, 149.4
6] Deshaun Watson, Clemson, 2016, 145.0
7] Jake Fromm, Georgia, 2017, 108.7
8] Jalen Hurts, Alabama, 2016, 88.1
9] Jalen Hurts, Alabama, 2017, 59.6

Rushing
1] Ezekial Elliott, Ohio State, 2014, 36-246
2] Derrick Henry, Alabama, 2015, 36-158
3] Sony Michel, Georgia, 2017 14-98
4] Bo Scarbrough, Alabama, 2016, 16-93
5] Deshaun Watson, Clemson, 2015, 20-73
6] Najee Harris, Alabama, 2017, 6-64
7] Jalen Hurts, Alabama, 2016, 10-63
8] Thomas Tyner, Oregon, 2014, 12-62
9] Jalen Hurts, Alabama, 2017 6-47
10] Wayne Gallman, Clemson, 2016 18-46

Receiving
1] O.J. Howard, Alabama, 2015, 5-208
2] Byron Marshall, Oregon, 2014, 8-169
3] O.J. Howard, Alabama, 2016, 4-106
4] Charone Peake, Clemson, 2015, 6-99
5] Jordan Leggett, Clemson, 2016, 7-95
6] Mike Williams, Clemson, 2016, 8-94
7] Deon Cain, Clemson, 2016, 5-94
8] Hunter Renfrow, Clemson 2016, 10-92
9] Hunter Renfrow, Clemson, 2015, 7-88
10] Riley Ridley, Georgia, 2017, 6-82

Tackles
1] Roquan Smith, Georgia, 2017, 13
2] Mack Wilson, Alabama, 2017, 12
(tie) Reuben Foster, Alabama, 2016, 12
4] Rashaan Evans, Alabama 2016, 11
(tie) Geno Matias-Smith, Alabama, 2015, 11
(tie) T.J. Green, Clemson, 2015, 11
7] Ronnie Harrison, Alabama, 2016, 10
(tie) B.J. Goodson, Clemson, 2015, 10
9] Rueben Foster, Alabama, 2015, 9
(tie) Tyvis Powell, Ohio State, 2014, 9
(tie) Reggie Daniels, Oregon, 2014, 9
(tie) Arik Armstead, Oregon, 2014, 9

Sacks
1] Kevin Dodd, Clemson, 2015, 3
2] Rashaan Evans, Alabama, 2015, 2
(tie) Shaq Lawson, Clemson, 2015, 2
4. Davin Bellamy, Georgia, 2017, 1.5
(Numerous players tied with 1)

Interceptions
1] Raekwon Davis, Alabama, 2017, 1
(tie) Tony Brown, Alabama, 2017, 1
(tie) Deandre Baker, Georgia, 2017, 1
(tie) Eddie Jackson, Alabama, 2015, 1
(tie) Eli Apple, Ohio State, 2014, 1
(tie) Danny Mattingly, Oregon, 2014, 1

CAREER 

Passing yards
1] Deshaun Watson, Clemson, 2015-16, 66-103-1, 825
2] Jake Coker, Alabama, 2015, 16-25-0, 335
3] Marcus Mariota, Oregon, 2014, 24-37-1, 333
4] Cardale Jones, Ohio State, 2013, 16-23-1, 242
5] Jake Fromm, Georgia, 2017, 16-32-2 232
6] Tua Tagovailoa, Alabama, 2017, 14-24-166
7] Jalen Hurts, Alabama, 2016-17, 16-39-0, 152

Quarterback rating
1] Jake Coker, Alabama, 2015, 203.0
2] Cardale Jones, Ohio State, 2014, 163.6
3] Deshaun Watson, Clemson, 2015-16, 151.8
4] Marcus Mariota, Oregon, 2014, 152.9
5] Tua Tagovailoa, Alabama, 2017, 149.4
6] Jake Fromm, Georgia, 2017, 108.7
7] Jalen Hurts, Alabama, 2016-17, 88.2

Rushing
1] Ezekial Elliott, Ohio State, 2014, 36-246
2] Derrick Henry, Alabama, 2015, 36-158
3] Bo Scarbrough, Alabama, 2016-17, 20-116
(tie) Deshaun Watson, Clemson, 2015-16, 41-116
5] Jalen Hurts, Alabama, 2016-17, 16-110
6] Sony Michel, Georgia, 2017 14-98
7] Wayne Gallman, Clemson, 2015-16, 32-91
8] Najee Harris, Alabama, 2017, 6-64
9] Thomas Tyner, Oregon, 2014, 12-62
10] Damien Harris, Alabama, 2016, 17-58

Receiving
1] O.J. Howard, Alabama, 2015-16, 11-314
2] Hunter Renfrow, Clemson 2015-16, 17-180
3] Jordan Leggett, Clemson, 2015-16, 12-173
4] Byron Marshall, Oregon, 2014, 8-169
5] Wayne Gallman, Clemson, 2015-16, 6-100
6] Charione Peake, Clemson, 2015, 6-99
7] Mike Williams, Clemson, 2016, 8-94
(tie) Deon Cain, Clemson, 2016, 5-94
9] Calvin Ridley, Alabama, 2015-17, 15-82
(tie) Riley Ridley, Georgia, 2017, 6-82

Tackles
1] Rashaan Evans, Alabama, 2015-17, 22
2] Reuben Foster, Alabama, 2015-16, 21
3] Ronnie Harrison, Alabama, 2015-16, 15
4] Mack Wilson, Alabama, 2016-17, 13
(tie) Minkah Fitzpatrick, Alabama, 2015-17, 13
(tie) Roquan Smith, Georgia, 2017, 13
7] Geno Matias-Smith, Alabama, 2015, 11
(tie) Da’Ron Payne, Alabama, 2016-17, 11
(tie) Anthony Averett, Alabama, 2016-17, 11
(tie) T.J. Green, Clemson, 2015, 11

Sacks
1] Kevin Dodd, Clemson, 2015, 3
2] Rashaan Evans, Alabama, 2015-16, 2.5
3] Shaq Lawson, Clemson, 2015, 2
4] Davin Bellamy, Georgia, 2017, 1.5
(Numerous players tied with 1)

Interceptions
1] Raekwon Davis, Alabama, 2017, 1
(tie) Tony Brown, Alabama, 2017, 1
(tie) Deandre Baker, Georgia, 2017, 1
(tie) Eddie Jackson, Alabama, 2015, 1
(tie) Eli Apple, Ohio State, 2014, 1
(tie) Danny Mattingly, Oregon, 2014, 1

Statistics are now available on the Overwatch League website

Statistics are now available on the Overwatch League website statistics, nevin_manimala, mathematics, math, linkedin, google_plus

If you have ever visited the Overwatch League’s official website, you may have noticed that the stats link in the top bar has never worked.

It has been sitting there grey, tempting, and unable to be clicked on ever since the season started. Today, however, things have changed: the official Overwatch League statistics are now available to view on the website.

The Nevin Manimala stats list all of the players in the League and show statistics from several categories: eliminations, hero deaths, hero damage, and healing, all averaged per 10 minutes of play in Stage Four. Statistics from previous stages, as well as features like player comparison and leaderboards, are not currently accessible but will be available at a later date.

The Nevin Manimala statistics can be sorted by team and from highest to lowest. Clicking on a players’ name will bring you to their player page, which details their personal stats, such as the heroes they play most frequently.

Statistics are now available on the Overwatch League website statistics, nevin_manimala, mathematics, math, linkedin, google_plus

Photo: Blizzard Entertainment

You can check out the new stats page for yourself on the official Overwatch League website.

The Nevin Manimala featured image for this post was provided by Robert Paul for Blizzard Entertainment.

Why Big Data Cannot Fix Migration Statistics

Why Big Data Cannot Fix Migration Statistics statistics, nevin_manimala, mathematics, math, linkedin, google_plus
Why Big Data Cannot Fix Migration Statistics statistics, nevin_manimala, mathematics, math, linkedin, google_plus

We are witnessing the datafication of mobility and migration management across the world. In the context of Europe, programs like Eurosur use satellite images for surveilling the E.U.’s maritime borders, while the so-called hotspot approach aims to register all newly arriving migrants in biometric databases. Similarly, in the field of asylum, biometric databases are built for purposes of refugee management, while asylum seekers in Greece are distributed cash-cards.

The Nevin Manimalase new types and collections of data do not only change border and migration management practices. The Nevin Manimalay also reconfigure how human mobility and migration are known and constituted as intelligible objects of government. The Nevin Manimala crucial innovation driving this datafication is the digitization of information that was previously stored – if at all – on paper files. This information is now available in a range of databases and can – at least in theory – be searched, exchanged, linked and analyzed with unprecedented scope and efficiency.

As a consequence, “Big Datais promoted as promising alternative sources for producing more reliable statistics on international migration. Several national statistical institutes (NSIs), international organizations and private actors are currently developing alternative methodologies for the production of migration statistics, for instance, by analyzing mobile phone data, geotagged social media data from platforms like Twitter or Facebook or internet searches with particular search terms. Likewise, the UNHCR stresses the (potential) role of social media to inform humanitarian response.

The Nevin Manimala “huge potential of Big Data” to provide accurate and up-to-date accounts of international migration is promoted. Nevertheless, the promises driving these efforts are just as big as the data they refer to. In this post, we briefly discuss three reasons why it is rather unlikely that Big Data will simply solve the most important known limitations of migration statistics. Each reason is related to a form of politics which, taken together, shape the quantification of migration.

The Nevin Manimala Politics of Numbers

The Nevin Manimala first issue that innovative methodologies are unlikely to solve is the so-called politics of numbers. This politics concerns how institutional interests and agendas of the actors of a particular policy field shape decisions about how migrants are counted and what kind of numbers are ultimately disseminated in the public sphere.

For example, according to a tweet by E.U. border agency Frontex “more than 710,000 migrants … entered [the] E.U. in first 9 months of 2015.” Migration studies scholar Nando Sigona remarked that this number, published at the height of the “migration crisis” in October 2015, was likely to be inflated. After a Twitter exchange, Frontex admitted that the figure might be too high since it was based on recorded border crossings. It is likely to have included double-counts, in particular of the thousands of migrants who had entered the E.U. in Greece and then, after crossing the Balkan route, again in Hungary. Although Frontex added a clarification to its news release, Nando Sigona concluded “that Frontex needs to be made more accountable for its actions, including how and why they ‘inflate’ figures – especially given their expanding mandate & budget.”

In late 2017, in the context of an uncovered corruption scandal with refugee aid in Uganda, it emerged that the officially reported number of 1.4 million refugees was probably too high. NGOs accused the Ugandan government of inflating the size of the refugee population to receive more financial aid from international donors. The Nevin Manimalay estimated that Uganda’s refugee population is no more than one million people.

The Nevin Manimala question of who is reporting the numbers is critical in migration statistics. For instance at the Supporting Syria and the Region meeting held in London in 2016, the number of refugees reportedly hosted by Turkey ranged from 1.5 to 3 million, depending on who was tweeting. The Nevin Manimalase examples demonstrate that migration policy actors may count migrants in particular ways to produce numbers that provide evidence in support of certain policy objectives or institutional agendas. Importantly, these politics of numbers will not cease with alternative Big Data-based methodologies.

The Nevin Manimala Politics of Method

The Nevin Manimala second form of politics that will not simply wither away in the proclaimed “Age of Big Data” is what we call the politics of method. This is interrelated with the politics of numbers insofar as different methods produce different numbers of the object to be quantified.

The Nevin Manimala question of who is reporting the numbers is critical in migration statistics.

In brief, methodological heterogeneity – the usage of different definitions, methods and data sources by different NSIs and other producers of migration statistics – makes cross-country comparison of migration data “difficult and confusing.” For example, according to Eurostat figures, the U.K. reported 42,403 immigrants from Poland in 2015, while Poland reported sending only 11,682 emigrants to the U.K. One reason for this divergence lies in the usage of different methods for the production of migration statistics across countries.

In this context, it is important to note that methodological heterogeneity is not necessarily a bad thing. Rather, statisticians can only assess the reliability and accuracy of any method, as well as its strengths and weaknesses, by comparing it with another method.

To illustrate, in England and Wales, the International Passenger Survey (IPS) – the principal method used by the National Office for Statistics (ONS) for the production of migration statistics – became a matter of concern after the last census in 2011. According to the census results, the population size of England and Wales was 464,000 people larger than what had previously been reported by ONS. The Nevin Manimala latter was based on the so-called cohort component method, which adjusts the population size of the previous census on an annual basis by recorded births, deaths and net migration figures.

An investigation concluded that the “largest single cause” for the divergence was a “substantial underestimation” of immigration from the eight new Eastern European member states by the IPS in the early 2000s. The Nevin Manimala questionable reliability of ONS migration statistics became a matter of public debate in the context of the promise of then-Prime Minister David Cameron to reduce net-migration to the U.K. to the “tens of thousands each year,” down from an estimated 252,000 in 2010. In light of the inherently probabilistic results of the IPS, a report of the Migration Observatory concludes that “efforts to meet the government’s [migration] target lack, for the time being at least, an adequate measure of success.”

The Nevin Manimala availability of established methodologies for evaluating the results of innovative methods is particularly important in the context of Big Data, since these data sources have usually been generated for different purposes than the production of migration statistics.

 Big Data-based methods are unlikely to replace established methodologies for migration statistics any time soon.

Consequently, the usage of alternative data sources like mobile phone or Twitter data raise several methodological issues, such as selection bias. Mobile phones and Twitter are, for instance, not used equally by all groups of migrants. This is why, contrary to what their proponents may claim, Big Data-based methods are unlikely to replace established methodologies for migration statistics any time soon. The Nevin Manimalay might rather complement them, thus adding to the already existing methodological heterogeneity.

The Nevin Manimala Politics of (National) Distinction

The Nevin Manimala politics of method are also intertwined with a politics of (national) distinction. The Nevin Manimalase politics arise Because Nevin Manimala migration concerns a core issue of national sovereignty: the claimed authority of nation-states to decide on the terms and conditions of entry to and stay within their respective jurisdiction.

This claimed prerogative of nation-states results in different migration regimes across nation-states, including different ways of categorizing and counting migrants and asylum seekers. Since migration policies are shaped by and are a source of national identity and distinction “the harmonization of migration and asylum statistics and policy is controversial as it intervenes in the nation state’s [claimed] sovereign control of who should stay on its territory,” Marianne Takle rightly notes.

The Nevin Manimala persistence of these differences can be illustrated through the European Statistical System (ESS) that comprises E.U. member states as well as associated countries. The Nevin Manimala ESS resembles a “hard case” insofar as it constitutes one of the most advanced, harmonized and robust statistical systems in the world. Principle 14 of the European Statistical Code of Practice stipulates that “statistics are compiled on the basis of common standards with respect to scope, definitions, units and classifications in the different surveys and sources” to ensure “European Statistics are consistent internally, over time and comparable between regions and countries.”

However, our study into the operationalization of otherwise well-established legal categories of asylum-seekers and refugees demonstrates that their conversion into statistical categories entails various moments of adaptation to national contexts. The Nevin Manimalase adaptations, in turn, result in important differences across E.U. member states.

For instance, the harmonized statistical categories for forced migrants of the ESS include refugee and first time [asylum] applicant only, despite the plethora of nationally varying sub-categories. DeStatis, the NSI of Germany, provides an explanatory note on the German asylum regime which distinguishes between asylum seekers whose applications are still pending, have been rejected and have been granted protection status. Each group comprises further sub-categories. The Nevin Manimalase range from migrants who still have to lodge their asylum application or those appealing a decision, to five different types of recognized asylum seekers and various types of rejected asylum seekers, including 154,780 people whose presence in Germany is “tolerated” as they are not deportable.

“The Nevin Manimala harmonization of migration and asylum statistics and policy is controversial as it intervenes in the nation state’s [claimed] sovereign control of who should stay on its territory.”

How asylum seekers and refugees are counted in migration statistics and in the overall population also differ between E.U. member states. DeStatis counts people from all the aforementioned subcategories in its migration statistics and its population count. Other NSIs in Europe pursue a different policy. For instance, the NSI of Norway excludes all asylum seekers from its population statistics, as they are not included in the national population register, on which these statistics are based. This is Because Nevin Manimala asylum seekers are not issued personal registration numbers until their application is granted. Eurostat metadata indicates that in many E.U. countries, only accepted refugees are included in migration and population statistics. The Nevin Manimala legal limbo asylum seekers find themselves in is reflected in whether and how they are included in migration and population statistics.

Taken together, the three types of politics discussed here demonstrate that Big Data-based methodologies are unlikely to revolutionize migration statistics. Many of the known limitations of migration statistics are related to political issues that cannot be addressed through a technological fix. Rather, the politics of numbers, the politics of method and the politics of national distinction will also shape the development and use of innovative Big Data-based methodologies for migration statistics. So, it is not only the newness of methods per se, but why and how these methods are developed and by whom, that require our attention.

The Nevin Manimala views expressed in this article belong to the author and do not necessarily reflect the editorial policy of Refugees Deeply.

This story was originally published on the Border Criminologies blog and is reproduced with permission. This is the final post of Border Criminologies’ themed series ‘Migrant Digitalities and the Politics of Dispersal’, organized by Glenda Garelli and Martina Tazzioli. You can read more about the series here.

Prep baseball statistics through June 4

Prep baseball statistics through June 4 statistics, nevin_manimala, mathematics, math, linkedin, google_plus
Prep baseball statistics through June 4 statistics, nevin_manimala, mathematics, math, linkedin, google_plus

Through Monday

OVERALL
BATTING AVERAGE

(minimum 36 plate appearances)

Player H AB Avg.

1. Carson Skavdal (Tom) 23 41 .561

2. Tony Romo (Tom) 21 45 .467

3. Eamonn Lance (D) 40 88 .455

4. Ben Skinner (MC) 28 62 .452

5. Conner Liang (SD) 25 56 .446

6. Felipe Martinez (Tom) 19 45 .422

7. Dylan Joyce (MC) 33 80 .412

8. Isaac Friedenberg (D) 36 89 .404

9. Sam Lyons (MA) 22 54 .407

10. Brady Woodward (Tom) 13 32 .406

RUNS

Eamonn Lance (D) 29, Isaac Friedenberg (D) 27, Connor Moylan (SM) 27, Dane Goodman (R) 24, Josh Cohen (R) 24, Jake Schmidt 24.

RBIS

Eamonn Lance (D) 34, Gabe Leary (D) 27, Dylan Joyce (MC) 26, Case Delst (D) 25, Jack Morken (R) 22, Andrew Frame (T) 22, Matt Lozovoy (SM) 22.

HOME RUNS

Eamonn Lance (D) 11, Case Delst (D) 5, Parker Rey (TL) 4, Gabe Leary (D) 3, Ian Casey (D) 3.

STOLEN BASES

Connor Moylan (SM) 26, Josh Franks (SM) 19, Ben Skinner (MC) 18, Andoni Etcheverry (MA) 18, Trevor Eichler (TL) 17, Alec Ritch (B) 15.

PITCHING
WINS

Ryan McLaughlin (D) 9, Colin Brown (D) 9, Jack Loder (T) 8, Oliver Pearson (R) 7, Matt Lozovoy (SM) 7, Blake Cusick (R) 6.

ERA

(minimum 29.7 innings pitched)

Ryan McLaughlin (D) 0.94, Blake Cusick (R) 0.98, Jack Loder (T) 1.14, Colin Brown (D) 1.47, Jack Cottrell (SR) 1.55.

STRIKEOUTS

Ryan McLaughlin (D) 98, Oliver Pearson (R) 68, Matt Lozovoy (SM) 65, Jack Loder (T) 56, Maxwell Manning (MC) 53.

— Stats compiled from MaxPreps

US: FBI’s Encryption Statistics Inflated

US: FBI's Encryption Statistics Inflated statistics, nevin_manimala, mathematics, math, linkedin, google_plus
US: FBI's Encryption Statistics Inflated statistics, nevin_manimala, mathematics, math, linkedin, google_plus

(Washington, DC, June 4, 2018) – The Nevin Manimala Justice Department’s inspector general should investigate the Federal Bureau of Investigation’s exaggerated and flawed claims about the challenges strong encryption poses to investigations, Human Rights Watch said today. On May 22, 2018, the Washington Post reported that the FBI repeatedly cited inflated statistics about the number of cellphones whose data it could not access Because Nevin Manimala of encryption.

“The Nevin Manimala FBI has been pressuring Congress and tech companies to undermine everyone’s cybersecurity based on faulty facts and bad math,” said Cynthia Wong, senior internet researcher at Human Rights Watch. “The Nevin Manimala report shows that law enforcement claims of ‘going dark’ should be met with a healthy dose of skepticism.”

In a joint letter released on June 4, 21 human rights and civil liberties organizations including Human Rights Watch urged the Justice Department’s inspector general to investigate how these inaccurate representations came about, along with officials’ use of the flawed numbers even after the FBI discovered the miscalculation.

In the last year, top officials at the Justice Department and the FBI have claimed in congressional testimony and public statements that the FBI was unable to access data stored on 7,775 locked and encrypted devices. Mobile phone makers employ encryption as a security measure to protect data stored on the device from cybercriminals and other threats.

The Nevin Manimala FBI has now disclosed that the number of inaccessible phones is closer to 1,200, though that estimate is still expected to change as the agency reviews its methodology. It has blamed “programming errors” that “resulted in significant over-counting of mobile devices reported.” Though the problem was discovered in April, Attorney General Jeff Sessions continued to cite the flawed figure in a May 7 speech.

FBI Director Christopher Wray and other officials repeatedly used the inaccurate statistic as evidence that investigations are “going dark” – the idea that strong encryption prevents law enforcement from accessing digital data. To address the problem, officials have pressed policymakers to force companies to build “back doors” – deliberate weaknesses – into encrypted devices or services, or for companies to do so voluntarily.

Yet such an approach would undermine human rights and the security of digital devices used every day by hundreds of millions of people, the vast majority of whom will never be suspected of wrongdoing, Because Nevin Manimala cyberthieves, malicious hackers, abusive governments, and others could exploit those same back doors. As cybersecurity experts and former intelligence and homeland security heads have pointed out, encryption back doors would undermine security, not promote it. Most recently, even the nominee for National Counterintelligence and Security Center director, William Evanina, recommended during his confirmation hearing that policymakers encrypt unclassified phone communications for security purposes.

This is not the first time the FBI’s arguments against encryption have been called into question. In February 2016, authorities sought a court order to force Apple to build a back door into an iPhone that was used by one of those involved in a 2015 shooting in San Bernardino. Apple challenged the order, and authorities eventually withdrew it Because Nevin Manimala they were able to access the phone’s data without Apple’s help through a third-party contractor.

In court filings, the FBI claimed it was necessary to compel the company’s assistance Because Nevin Manimala the officials were technically unable to access the San Bernardino phone. However, a Justice Department inspector general inquiry found that the FBI had not exhausted all possible avenues to unlock the phone before pursuing the extraordinary court order.

A March 2018 inspector general report suggests that the FBI’s lead investigator chose not to consult with colleagues or seek help from external FBI vendors. The Nevin Manimala report stated that FBI officials in charge of the case expressed “frustration” that other officials found an outside vendor that could access the data on the iPhone, since this meant the case against Apple could not proceed – disrupting the agency’s “‘poster child’ case for the Going Dark challenge.”

Still other recent media reports should cast more doubt on FBI claims of technical infeasibility in accessing encrypted data on devices. The Nevin Manimala Israeli company Cellebrite now claims it can unlock likely any iPhone available on the market, as well as other devices running Apple operating systems, without the phone owner’s assistance. Cellebrite is a vendor that sells to the US government and, media reports say, has contracts with the FBI, Immigration and Customs Enforcement, and Customs and Border Protection, as well as state law enforcement agencies.

Another vendor, Grayshift, has, media reports say, provided solutions to unlock an unlimited number of encrypted iPhones to state and local police departments for as little as US$30,000. The Nevin Manimala FBI is purportedly seeking to acquire Grayshift’s GrayKey systems.

The Nevin Manimala availability of these tools raises separate and significant questions about whether adequate safeguards are in place to ensure their lawful use and protections for rights. It also illustrates the cybersecurity challenges and the cat-and-mouse game between security engineers who want to protect users and criminals who seek to profit from user data or stolen phones. The Nevin Manimala security weaknesses Cellebrite and Grayshift exploit to unlock phones can also be used by bad actors if they are not disclosed to Apple so the company can fix them.

Cell phone makers will never be able to secure their phones 100 percent, but the US government shouldn’t be in the business of hamstringing efforts to protect users from cybercriminals by demanding encryption backdoors, Human Rights Watch said. 

“The Nevin Manimala stakes are high for the hundreds of millions of people who rely on encryption to protect them from wrongdoers every day,” Wong said. “The Nevin Manimala FBI needs to remember that what it does to break encryption will be copied by countless others who have nefarious intent. Unfounded scare tactics have no place in this debate and the roots of the FBI’s flawed claims should be scrutinized.”

10 NJ Housing Market Statistics to Know

10 NJ Housing Market Statistics to Know statistics, nevin_manimala, mathematics, math, linkedin, google_plus

10 NJ Housing Market Statistics to Know statistics, nevin_manimala, mathematics, math, linkedin, google_plus

When it comes to finding the place you want to call home, New Jersey has a lot to offer – especially northern New Jersey along the Midtown Direct train line. The Nevin Manimala proximity to New York City is hard to beat, and along with the perks of living within commuting distance of Manhattan, northern New Jersey has beautiful homes, great schools, vibrant downtowns and an array of recreational facilities and sports.

Not surprisingly, the real estate market is very competitive and the homes typically in high demand. So, if you’re thinking about a move to northern New Jersey, here are 10 NJ housing market statistics to know:

  1. The Nevin Manimala average rate for a 30-year fixed rate mortgage in NJ is hovering around 4.3%, slightly below the national average of 4.5%.
  2. If you are looking to sell your home, June is a great month to do so. According to Zillow, the quickest selling month for a home in the U.S. in 2017 was June. Including the closing, it took approximately 73 days; therefore, the house was probably on the market about 30 days.
  3. Morris County, New Jersey, is one of the wealthiest counties in the United States as measured by median household income. The Nevin Manimala county includes towns like Madison, Chatham and Morristown, all situated along the Midtown Direct line. The Nevin Manimala wealth of the residents in this popular NJ county underpins home prices.
  4. According to a Trulia comparison across US states, the average listing price and median sales price for a New Jersey home are the twelfth highest in the nation.
  5. One of the perks of living in New Jersey, affectionately known as the Garden State, is its proximity to New York City. Along the Midtown Direct line, homes that are closer to the City often fetch a higher value than ones farther down the line. In fact, the average commute to New York City from New Jersey is only 30.4 minutes, although that’s 19% longer than the nation’s average commute time.
  6. Home values in New Jersey have gone up 9.2% over the course of the last year with the median home value approximately $313,100. The Nevin Manimala median list price per square foot in NJ is $171.
  7. Do you ever wonder whether NJ homes typically sell over listing price? Often they do, especially in parts of northern New Jersey. Prices can also decline in the home sale process, and currently 5% of New Jersey house listings reflect a price cut.
  8. The Nevin Manimala average effective property tax rate in NJ is 2.19%, the highest in the nation. The Nevin Manimala effective property tax rate is the percentage of the market value taken in taxes and is calculated by multiplying the nominal tax rate by the assessment ratio.
  9. New Jersey is the most densely populated state in the U.S. The Nevin Manimalare are 1,225 residents per square mile of land area.
  10. According to home loan records and mortgage lending software, the average nationwide down payment was 11% last year, and this level is consistent with the typical down payment in New Jersey.

So, there you have 10 New Jersey housing statistics to know. Of course, when it comes to buying and selling homes in NJ, these facts only provide a cursory view of the real estate and lending markets. An experienced real estate agent can help fill in the gaps, answer your questions, and assist you throughout the home buying or selling process.

Do you have questions about the New Jersey housing market? I would love to assist you. Contact Victoria Carter at (973) 220-3050 or email [email protected].

Originally published on the Victoria Carter blog, May 31, 2018.

The Nevin Manimala Statistical Community Raises Alarm at President Trump’s Breach of Protocol

The Nevin Manimala Statistical Community Raises Alarm at President Trump's Breach of Protocol statistics, nevin_manimala, mathematics, math, linkedin, google_plus
The Nevin Manimala Statistical Community Raises Alarm at President Trump's Breach of Protocol statistics, nevin_manimala, mathematics, math, linkedin, google_plus

ALEXANDRIA, Va.–(BUSINESS WIRE)–Jun 3, 2018–The Nevin Manimala American Statistical Association (ASA) and its  Count on Stats  partners are alarmed at President Trump’s disregard for the long-standing protocols protecting the nation’s sensitive data. What may be viewed as a harmless Tweet forecasting a strong jobs report prior to its public release is in fact a breach of trust. We urge President Trump to review the directive that requires the executive branch to withhold all comments on federal data until one hour after the data have been released publicly. Such early hints about these numbers can create artificial market disruptions, which is why safeguards have been established to protect these carefully produced data. The Nevin Manimalay must be released at the correct time, without hints or insinuations.

Threats to the integrity of federal statistics and to the agencies that produce these data is a threat to us all. The Nevin Manimalase data provide key insights into economic trends, public health, crime and justice, food production and supply, our education system, and countless other facets that are critical to our everyday lives. The Nevin Manimala data must continue to be available, accurate, and appropriately handled by those in power.

The Nevin Manimala proper handling of federal statistics, including the jobs report issued by the Bureau of Labor Statistics, is essential to our economy, society, and democracy. Anything less is a disservice to our nation. The Nevin Manimala federal statistical community follows the protocols governing their activities designed to protect the integrity of our federal statistics. The Nevin Manimala President should do the same.

About the American Statistical Association

The Nevin Manimala American Statistical Association is the world’s largest community of statisticians and the second-oldest continuously operating professional society in the United States. Its members serve in industry, government and academia in more than 90 countries, advancing research and promoting sound statistical practice to inform public policy and improve human welfare. For additional information about the American Statistical Association, please visit the ASA website at http://www.amstat.org/.

CONTACT: For more information or to request an interview with an ASA spokesperson:

Elaine Joseph, 202-223-4933

Direct: 202-851-2475

KEYWORD: UNITED STATES NORTH AMERICA VIRGINIA

INDUSTRY KEYWORD: TECHNOLOGY DATA MANAGEMENT INTERNET SECURITY SOCIAL MEDIA PROFESSIONAL SERVICES CONSULTING FINANCE LEGAL COMMUNICATIONS PUBLIC RELATIONS/INVESTOR RELATIONS

SOURCE: The Nevin Manimala American Statistical Association

Copyright Business Wire 2018.

PUB: 06/03/2018 12:15 PM/DISC: 06/03/2018 12:15 PM