lördag 18 februari 2017

New forms of data will eventually replace the survey

From Professor Mark Elliot and Patrick Sturgis - YouTube:

Professor Mark Elliot and Patrick Sturgis presented at the 7th ESRC Research Methods Festival, 5-7 July 2016, University of Bath. The Festival is organised every two years by the National Centre for Research Methods www.ncrm.ac.uk

Valresultatet och opinionsundersökningarna i USA

Från Karin Nelsson Inizio:
Valresultatet i USA sänder chockvågor över världen. Det som tolkades som en ganska klar seger för Clinton blev i själva verket en nagelbitare under valnatten där Trump utsågs som nästa president.

Hur ska valresultatet tolkas? Det är fortfarande väldigt tidigt att dra alltför långtgående slutsatser, men vi kan konstatera att Clinton förlorade viktiga sk swingstates (dvs stater där valutgången var osäker) och oväntat tappade traditionella demokratiska fästen som Michigan och Wisconsin.

Läs mer....

Structural Equation Modeling: what is it and what can we use it for?

From NCRM By Patrick Sturgis
To cite this resource:Sturgis, P. (2017) Structural Equation Modeling: what is it and what can we use it for?. National Centre for Research Methods online learning resource. Available at http://www.ncrm.ac.uk/resources/online/SEM2016/ [ accessed: February 18, 2017]'

onsdag 15 februari 2017

Statistics and politics have much to teach one another

Juggling Mega-Data Illustration by Greg Groesch/The Washington Times
Juggling Mega-Data Illustration by Greg Groesch/The Washington Times more >
From Washington Times:
Big Data provides the power to gauge opinion, and to shape it

What this past year has taught me more than anything is this: First, you can learn a lot about statistics from politics; and second, both are misunderstood.

It has been an interesting year in politics, polling and statistics. In talking to most people, one would get the impression all three are broken. If one were to believe the polls, Brexit would not have happened and Donald Trump would not be the 45th president of the United States.

Does this mean all polls are broken, or we are signaling the end of the era of statistics? It is highly probable that the answer is no, and I contend that Big Data and a statistically driven society is not dead, it is just not yet fully understood.

So why do we keep getting it so wrong? Read more ....

lördag 11 februari 2017

7 Tips to Succeed with Big Data

From Tableau Software:
One thing we know for sure is that big data will continue to grow. Terabytes are old news; now we’re hearing about petabytes, zettabytes, and beyond.

So how can you mine maximum value from your rapidly-expanding data? Read this guide to learn seven new ways to make the most of your big data:
  • Use one tool to analyze it all. Connect to multiple databases and file formats.
  • Play to your natural strengths. Human abilities naturally detect and understand patterns.
  • Free your data. Eliminate the reporting que with secure self-service.
  • Cross the streams. Unlock more insights by blending data across multiple sources.
  • With great power comes great responsibility.Allow IT to manage data architecture, security, and access controls.

One Dataset, Visualized 25 Ways

From  FlowingData
“Let the data speak.” It’s a common saying for chart design. The premise — strip out the bits that don’t help patterns in your data emerge — is fine, but people often misinterpret the mantra to mean that they should make a stripped down chart and let the data take it from there.

You have to guide the conversation though. You must help the data focus and get to the point. Otherwise, it just ends up rambling about what it had for breakfast this morning and how the coffee wasn’t hot enough.

To show you what I mean, I present you with twenty-five charts below, all based on the same dataset. It’s life expectancy data by country, it’s from the the World Health Organization and it spans 2000 to 2015. Each chart provides a different focus and interpretation.

tisdag 7 februari 2017

Hans Rosling: The Swedish physician and popular TED speaker who made statistics come alive has died

Hans Rosling at TEDIndia, Session 1, "Fast Forward," November 5, 2009, in Mysore, India. Credit: TED / James Duncan Davidson
Patron saint of stats. (ED / James Duncan Davidson)
Hans Rosling, the beloved Swedish statistician and self-described “edutainer” who taught the world how to use numbers and data in thrilling ways, has died of pancreatic cancer. He was 68. Rosling’s passing on the morning of Feb. 7 was announced by his son and daughter-in-law on their website, GapMinder.

Millions of viewers learned about the Uppsala University professor through the 10 TED talks he has delivered on topics ranging from child mortality to population growth. In each one, Rosling displayed his knack for engaging audiences by presenting seemingly boring statistics in startling ways, using humor, animated graphics, and live stage props.

Working with Statistics on OECD iLibrary

From OECD iLibrary - YouTube:

Gain a comprehensive overview of how to work with statistics on OECD iLibrary through a concrete example— a research project on "Teachers' salaries"— from search to citation. Discover how to access this data through the 'Statistics' page of OECD iLibrary as 'Indicators', 'Databases', archived data in CSV format, 'Statlinks', and within 'Book Series' publications as pre-defined tables.

Is Data Science Too Easy?

Photo by Peter Macdiarmid/Getty Images for Somerset House
From Forbes:
"Statistics, specifically, is concerned largely with methods for testing hypotheses using data; thus, before one can constructively use Hadoop or R, one needs to know statistics and know it well. Because, unlike statistics—which is concerned largely with testing the hypotheses and stops there—data science focuses on the implications of systematic departures from hypotheses (as evidenced by statistical tests) and the bigger conclusions we can make as a result of those departures."

onsdag 1 februari 2017

Alan Smith: Why we're so bad at statistics

Alan Smith: TED Talk | TED.com:

Think you're good at guessing stats? Guess again. Whether we consider ourselves math people or not, our ability to understand and work with numbers is terribly limited, says data visualization expert Alan Smith. In this delightful talk, Smith explores the mismatch between what we know and what we think we know.

tisdag 31 januari 2017

Comprehensive & Practical Inferential Statistics Guide for data science

Analytics Vidhya - Learn everything about AnalyticsFrom Analytics Vidhya
Statistics is one of the key fundamental skills required for data science. Any expert in data science would surely recommend learning / upskilling yourself in statistics.

However, if you go out and look for resources on statistics, you will see that a lot of them tend to focus on the mathematics. They will focus on derivation of formulas rather than simplifying the concept. I believe, statistics can be understood in very simple and practical manner. That is why I have created this guide.

In this guide, I will take you through Inferential Statistics, which is one of the most important concepts in statistics for data science. I will take you through all the related concepts of Inferential Statistics and their practical applications.

This guide would act as a comprehensive resource to learn Inferential Statistics. So, go through the guide, section by section. Work through the examples and develop your statistics skills for data science.

måndag 30 januari 2017

Structural Equation Modeling: what is it and what can we use it for? + two more videos

From https://youtu.be/eKkESdyMG9w and http://www.ncrm.ac.uk/resources/online/SEM2016/

Professor Patrick Sturgis, NCRM director, in the first (of three) part of the Structural Equiation Modeling NCRM online course.

This video is part of the online learning resources from the National Centre for Research Methods (NCRM). To access the supporting materials (presentation slides, datasets, recommended reading, links to related publications and resources) visit http://www.ncrm.ac.uk/resources/onlin...

lördag 28 januari 2017

Get Up to Speed with Data Science in 7 Easy Steps

Inside Big Data
From - insideBIGDATA:
Data science is an interdisciplinary field containing processes and systems to extract knowledge and insights from data in various forms, either structured or unstructured. The field is showing itself transformative for a broad range of organizations in the way it delivers real business value based on enterprise data assets. At its core, data science involves using automated methods to analyze massive amounts of data and to extract knowledge from them. With such automated methods turning up everywhere from retail to genomics, data science is helping to create new branches of knowledge discovery and predictive analytics. The trend is expected to accelerate in the coming years as the volume of data grows from sensors, sophisticated instruments, the web, and more.

Although use of the term “data science” has exploded in business environments, many academics and journalists see no distinction between data science and statistics. Writing in Forbes, analyst Gil Press argues that data science is a buzzword without a clear definition and has simply replaced “business analytics” in various contexts. In the question-and-answer section of his keynote address at the Joint Statistical Meetings of American Statistical Association, noted applied statistician Nate Silver said, “I think data-scientist is a sexed up term for a statistician….Statistics is a branch of science. Data scientist is slightly redundant in some way and people shouldn’t berate the term statistician.”

fredag 27 januari 2017

How statistics lost their power – and why we should fear what comes next

From William Davies | Politics | The Guardian:
In theory, statistics should help settle arguments. They ought to provide stable reference points that everyone – no matter what their politics – can agree on. Yet in recent years, divergent levels of trust in statistics has become one of the key schisms that have opened up in western liberal democracies. Shortly before the November presidential election, a study in the US discovered that 68% of Trump supporters distrusted the economic data published by the federal government. In the UK, a research project by Cambridge University and YouGov looking at conspiracy theories discovered that 55% of the population believes that the government “is hiding the truth about the number of immigrants living here”.

lördag 21 januari 2017

How Unconscious Sexism Could Help Explain Trump’s Win

From FiveThirtyEight:
A woman has never come closer to the presidency than Hillary Clinton did in winning the popular vote in November. Yet as women march in Washington on Saturday, many of them to protest the presidency of Donald Trump, an important obstacle to the first woman president remains: the hidden, internalized bias many people hold against career advancement by women. And perhaps surprisingly, there is evidence that women hold more of this bias, on average, than men do.