lördag 17 februari 2018

Visst kan man lita på opinionsundersökningar

Medievärlden - Diskussion:
"Att VLT, och dess associerade publikationer, nu väljer att blankt vägra presentera opinionsundersökningar är ett uppseendeväckande ställningstagande, som på intet sätt bidrar till ett bättre undersökningsklimat, skriver branschorganisationen SMIF i ett svar till Daniel Nordström."

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tisdag 13 februari 2018

Häggström hävdar: Meningsutbyte med Bo Rothstein om matematisk modellering

Häggström hävdar:
"Bo Rothstein är med varje rimligt mått mätt en av Sveriges mest framstående statsvetare, och på tidningarnas debattsidor utgör han en frisk fläkt. Ibland går han dock en smula överstyr i sin argumentation, som i gårdagens artikel på DN Debatt, rubricerad Felaktig tolkning av metoo riskerar att skada tilliten. Det finns mycket att diskutera och kritisera i den artikeln, men här skall jag uppehålla mig vid en enda detalj, nämligen följande passage:
[På] min egen arbetsplats, Göteborgs universitet, [utförs] ett antal undersökningar [...] där de tillfrågade fått svara på frågan om de under det senaste året utsatts för sexuella trakasserier. Resultatet är att cirka 2 procent av kvinnorna uppger att de blivit utsatta (och ungefär 1 procent av männen).
Det är statistiskt inte helt enkelt att översätta dessa två procent per år till längre tidsperioder, men om man utgår från en anställningstid om 20 år är en någotsånär rimlig uppskattning att femton procent av de anställda kvinnorna blivit utsatta för vad de själva uppfattar vara sexuella trakasserier någon gång under en tjugoårsperiod.
Dessa 15% synes mig gripna ur luften, något som föranledde mig att igår skriva en Facebookuppdatering med följande innehåll."

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Så fungerar Tensorflow – verktygslådan för AI

TechWorld:
"Egentligen kan man lika gärna kalla maskininlärning för dataanalys, som för AI. Men eftersom tekniken gör helt nya analyslösningar möjliga är det naturligt att sortera in den under paraplybegreppet AI.

Ett sätt att beskriva maskininlärning är att det är en svart låda, i vilken man stoppar in data och får ut förutsägelser. Men hur gör man i praktiken för att bygga en lösning?"

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Så funkar maskininlärning – steg för steg

TechWorld:
"En allmän iakttagelse att är maskinlärning egentligen lika gärna kan kallas för dataanalys, eller till och med matte, som för AI. Att man kallar maskinlärning för AI kanske beror på att det är en teknik som gör det möjligt att dra slutsatser som människor åtminstone i de flesta fall inte klarar av."

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Why Data Analysis & Not Math Is A Prerequisite For Machine Learning

Why Data Analysis & Not Math Is A Prerequisite For Machine Learning
"If you are an absolute Machine Learning beginner and are wondering whether data analysis is a prerequisite, then here’s the hard-fact – data analysis meaning the task of gathering data, cleaning data, exploring and visualizing data is an absolute must before one gets started on machine learning."

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söndag 11 februari 2018

SCB: V-förslag inte förenligt med lagen

Sydsvenskan:
"Vänsterpartiet vill att SCB ska börja föra statistik över etnicitet och religion – men den berörda myndigheten avfärdar idén. – Det är inte tillåtet att samla in statistik över etnisk tillhörighet, säger Nizar Chakkour, pressansvarig på SCB."

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Why is it so hard to train data scientists?

Data Science Central:
"A data scientist must also be highly familiar with statistics, and understand multiple statistical methods for tasks such as regression, dimensionality reduction, statistical significance analysis, Mote Carlo simulations, and Bayesian methods, to name a few. The data scientist needs to have knowledge in statistics at a level close to the knowledge of the statistician."

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fredag 26 januari 2018

Great R packages for data import, wrangling and visualization


Computerworld:
Here are my go-to R packages -- in a handy searchable table.
One of the great things about R is the thousands of packages users have written to solve specific problems in various disciplines -- analyzing everything from weather or financial data to the human genome -- not to mention analyzing computer security-breach data.
[ Need to learn R or brush up on basics? Download our free Beginner's Guide to R or the Advanced Beginner's Guide to R ]Some tasks are common to almost all users, though, regardless of subject area: data import, data wrangling and data visualization. The table below show my favorite go-to packages for one of these three tasks (plus a few miscellaneous ones tossed in). The package names in the table are clickable if you want more information. To find out more about a package once you've installed it, type help(package = "packagename") in your R console (of course substituting the actual package name ).
[To comment on this story, visit Computerworld's Facebook page.]
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måndag 22 januari 2018

7 Visualizations You Should Learn in R

Data Science Central:
With ever increasing volume of data, it is impossible to tell stories without visualizations. Data visualization is an art of how to turn numbers into useful knowledge.

R Programming lets you learn this art by offering a set of inbuilt functions and libraries to build visualizations and present data. Before the technical implementations of the visualization, let’s see first how to select the right chart type.

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Factfulness: Ten Reasons We're Wrong About the World--and Why Things Are Better Than You Think

Hans Rosling, Anna Rosling Rönnlund, Ola Rosling: 9781250107817: Amazon.com: Books
Factfulness: The stress-reducing habit of only carrying opinions for which you have strong supporting facts.

When asked simple questions about global trends―what percentage of the world’s population live in poverty; why the world’s population is increasing; how many girls finish school―we systematically get the answers wrong. So wrong that a chimpanzee choosing answers at random will consistently outguess teachers, journalists, Nobel laureates, and investment bankers.

In Factfulness, Professor of International Health and global TED phenomenon Hans Rosling, together with his two long-time collaborators, Anna and Ola, offers a radical new explanation of why this happens.

onsdag 17 januari 2018

Selected Recent Articles from Top DSC Contributors

This is a new series, featuring great content from our top contributors. Some of these articles are rather technical in nature, but many are business-oriented and written in simple English. The entire series consists of about 120 articles. We intend to publish a new set every two weeks or so. Click here to check out the previous edition. To read more articles from a same author, read one of his/her articles and click on his/her profile picture to access the full list. Some of these articles are curated or posted as guest blogs.

Selected Recent Articles from Top DSC Contributors

EMOS WEBINARS 2018

EMOS WEBINARS 2018:
Registrations for the next three EMOS Webinars are now open. Registration is on a first-come first-serve basis.

Webinar title: Web surveys
Presenters: Vasja Vehovar and Nejc Berzelak, University of Ljubljana
Date: 7 February 2018

Webinar title: Innovations in business statistics data collection
Presenter: Ger Snijkers, Statistics Netherlands
Date: 21 February 2018

Webinar title: Statistical monitoring of sustainable development at global, EU and national level (with a case study of Poland)
Presenters: Nicola Massarelli, Eurostat, and Monika Gorzelak, Statistics Poland
Date: 28 February 2018

You can check the content and register for these webinars at: http://konference.ef.uni-lj.si/emos/webinars/ where you will also find the recording of the webinar The European Statistical System that took place on 10 January 2018.

fredag 12 januari 2018

The Guinness Brewer Who Revolutionized Statistics

The Guinness Brewer Who Revolutionized Statistics:
One of the greatest minds in 20th Century statistics was not a scholar. He brewed beer.

Guinness brewer William S. Gosset’s work is responsible for inspiring the concept of statistical significance, industrial quality control, efficient design of experiments and, not least of all, consistently great tasting beer.

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Traits of a Successful Statistician

Traits of a Successful Statistician:
It goes without saying that a successful statistician must have strong analytical and technical skills. Clearly, you need to know and understand statistics—this is, after all, the added value that you uniquely provide. Most statisticians have master’s degrees, but some—especially those who plan to go into academia—earn PhDs. Still others might hold, at least initially, an undergraduate degree only. How far to take your training is highly dependent on the specific career path you expect to follow.

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How Not To Lie With Statistics

How Not To Lie With Statistics:
Darrell Huff's classic How to Lie with Statistics is perhaps more relevant than ever. In this short article, I revisit this theme from some different angles.

"What is truth?" and "What is a lie?" are questions that have drawn the attention of philosophers, theologians, legal scholars and intellectuals of many kinds for centuries. I am not a scholar or intellectual, merely a hardhat statistician working in marketing research and what is vaguely called data science. Regardless of what we do for a living, however, all of us are consumers of statistics at work and in our daily lives. “Statistics” can refer to figures or mathematical models, and either can be used to deceive us, are often misinterpreted or can be flat out wrong.

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onsdag 10 januari 2018

Investeringsprocess Avanza Auto

Från White Paper, Avanza Fonder AB:
Avanza Auto använder den s.k. Black-Littermanmodellen som är en praktisk utveckling av HarryMarkowitz nobelprisbelönta och klassiska portföljvalsmetod.

Markowitz portföljvalsmodell från 1950-talet bygger på att hitta den optimala avvägningen mellan risk och avkastning i en portfölj beroende på investerarens preferenser för risktagande.

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The 10 most important breakthroughs in Artificial Intelligence

TechRadar:
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Switching to statistics

Though neural networks had existed as a concept for some time (see above!), it wasn’t until the late 1980s when there was a big shift amongst AI researchers from a “rules based” approach to one instead based on statistics - or machine learning. This means that rather than try to build systems that imitate intelligence by attempting to divine the rules by which humans operate, instead taking a trial-and-error approach and adjusting the probabilities based on feedback is a much better way to teach machines to think. This is a big deal - as it is this concept that underpins the amazing things that AI can do today.

Gil Press at Forbes argues that this switch was heralded in 1988, as IBM’s TJ Watson Research Center published a paper called “A statistical approach to language translation”, which is specifically talking about using machine learning to do exactly what Google Translate works today.

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A User's Guide To China's Economic Statistics

Forbes:
China will soon release full-year economic growth figures for 2017. Don't be surprised if the headline number is 6.7%. After all, that is the target. Economic output tracked 6.9% above last year's level throughout Q1 and Q2 of 2017, falling to 6.8% in Q3. With China's leaders talking up the "quality" rather than the quantity of economic growth, it's a safe bet that growth will be allowed to glide down toward the 6.7% target for the final quarter.

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tisdag 9 januari 2018

Videos for teaching and learning statistics

Helping learners and teachers feel better about maths and stats:
It delights me that several of my statistics videos have been viewed over half a million times each. As well there is a stream of lovely comments (with the odd weird one) from happy viewers, who have found in the videos an answer to their problems.

In this post I will outline the main videos available on the Statistics Learning Centre YouTube Channel. They already belong to 24,000 playlists and lists of recommended resources in textbooks the world over. We are happy for teachers and learners to continue to link to them. Having them all in one place should make it easier for instructors to decide which ones to use in their courses.

Read and view.....