Global Emotion: Transfixed by Tragedy

Slides:



Advertisements
Similar presentations
Quality control tools
Advertisements

Chapter 3 Examining Relationships Lindsey Van Cleave AP Statistics September 24, 2006.
A GLOBAL NETWORK of random sources shows deviations linked with events that affect millions of people. The results challenge common ideas about.
Yellow dots are nodes in the network Roger Nelson Effects of Collective Attention: Patterns Where.
The Global Consciousness Project Weak Signals, Strong Implications Roger Nelson Yellow spots are.
Class 10: Tuesday, Oct. 12 Hurricane data set, review of confidence intervals and hypothesis tests Confidence intervals for mean response Prediction intervals.
Yellow dots are nodes in the network Roger Nelson Patterns Where There Should Be None A Glimpse of.
As with averages, researchers need to transform data into a form conducive to interpretation, comparisons, and statistical analysis measures of dispersion.
September In Chapter 14: 14.1 Data 14.2 Scatterplots 14.3 Correlation 14.4 Regression.
Chapter 3: Examining relationships between Data
1 LES of Turbulent Flows: Lecture 1 Supplement (ME EN ) Prof. Rob Stoll Department of Mechanical Engineering University of Utah Fall 2014.
Statistics Recording the results from our studies.
Yellow dots are nodes in the network Roger Nelson Effects of Collective Attention: Patterns Where.
Max temp v min temp. It can be seen from the scatterplot that there is a correlation between max temp and min temp. Generally, as min temp increases,
Time series Model assessment. Tourist arrivals to NZ Period is quarterly.
The Global Consciousness Project Weak Signals, Strong Implications Roger Nelson.
Relationships If we are doing a study which involves more than one variable, how can we tell if there is a relationship between two (or more) of the.
Global Warming By: Jagat Ramdin. Average sunspots over time.
An overview of analytical findings and recent developments Roger Nelson, Director Peter Bancel, Principal Analyst Global Consciousness Project
Correlation The apparent relation between two variables.
Business Statistics for Managerial Decision Farideh Dehkordi-Vakil.
Residuals Recall that the vertical distances from the points to the least-squares regression line are as small as possible.  Because those vertical distances.
Organizing and Analyzing Data. Types of statistical analysis DESCRIPTIVE STATISTICS: Organizes data measures of central tendency mean, median, mode measures.
The Global Consciousness Project Weak Signals, Strong Implications Roger Nelson.
Types of Descriptive Research
Types of Descriptive Research
Linear Algebra Review.
Inference for Least Squares Lines
What is normal anyway?! Disclaimer: I am not an expert!
Least Squares Regression
What better example than Finally, What’s the Bottom Line?
Regression Analysis Simple Linear Regression
Do-Now-Day 2 Section 2.2 Find the mean, median, mode, and IQR from the following set of data values: 60, 64, 69, 73, 76, 122 Mean- Median- Mode- InterQuartile.
When you put a thing in order, and give it a name, and you are all in accord, it becomes. - - From the Navajo, Masked Gods, Waters, 1950.
Chapter 2: Modeling Distributions of Data
Aim: To Describe and explain the variations in health as reflected by changes in life expectancy at national and global scales since This unit is.
Standard Deviation.
Standard Deviation.
An Example of {AND, OR, Given that} Using a Normal Distribution
The Global Consciousness Project
Chapter 7 Part 1 Scatterplots, Association, and Correlation
Scatter Plots Below is a sample scatter plot, can you tell me what they are designed to show.
CHAPTER 26: Inference for Regression
I am comparing humans to a(n) (type animal here)
Standard Deviation.
Week 6 Statistics for comparisons
Sections 5-1 and 5-2 Quiz Review Warm-Up
The Weather Turbulence
Unit 4 Vocabulary.
3 4 Chapter Describing the Relation between Two Variables
Carbon: Transformations in Matter and Energy
Descriptive and Inferential
Figure 4-1 (p.104) The statistical model for defining abnormal behavior. The distribution of behavior scores for the entire population is divided into.
Least-Squares Regression
Trajectory Encoding in the Hippocampus and Entorhinal Cortex
Carbon: Transformations in Matter and Energy
UIG Task Force Progress Report
PCA of Waimea Wave Climate
Cortical Mechanisms of Smooth Eye Movements Revealed by Dynamic Covariations of Neural and Behavioral Responses  David Schoppik, Katherine I. Nagel, Stephen.
Dataset: Time-depth-recorder (TDR) raw data 1. Date 2
Examining Relationships
Carbon: Transformations in Matter and Energy
Correlation/regression using averages
Xbar Chart By Farrokh Alemi Ph.D
POSC 202A: Lecture 2 Today: Introduction to R
Jude F. Mitchell, Kristy A. Sundberg, John H. Reynolds  Neuron 
Standard Deviation.
Line Graphs.
OvulonaTM predicts & detects ovulation
Correlation/regression using averages
Presentation transcript:

Global Emotion: Transfixed by Tragedy On September 11 2001, early in the morning, a network of physical random event generators (called “eggs”) took on a striking trend. By 8:45 the non-random behavior was unmistakable. It peaked at about 10:30 with odds against chance of a thousand to one. See the red trace below. Several measures deviated from expectation on that day, indicating a reduction in randomness. The eggs became linked across distance and time in some subtle way that we do not yet know how to explain. See the figures on the right. This is not a physical or electromagnetic effect. It’s not due to ubiquitous mobile phone use, or saturation TV. It appears to be related to our strong emotions and extraordinary focus. On 9/11 the data showed extraordinary moments On 9/11 the data contained unique sequential structure On 9/11 deviations began that persisted for over two days The jagged red line shows three days of a measure (the squared cumulative deviation of variance) that represents the composite randomness of 37 eggs. On September 11, the data contain clear patterns where there should be none. Most remarkably, they begin well before the first airplane hit. More at http://noosphere.princeton.edu am am …………………………………………………………………………………………………………………..…….……………..… Normal range of variation am

Global Attention: Sharing New Year’s Eve All over the world, people celebrate the change to a New Year. Since 1998, we have recorded data from a global network of electronic random generators (called “eggs”). The four graphs here show patterns in the data around midnight as the New Year arrives in each time zone. The scientific prediction is that there will be evidence of increased correlation among the eggs. We test for trends away from the expected “random walk”. We look for, and find, reductions in the variation across the eggs. The graphs on this page speak for themselves. They are pictures of our deep engagement with each other. And again for last year. The pattern is replicated for the third time. We predicted the same pattern for the following year. Then, for the infamous Y2K transition, we looked at a measure of the variability among the eggs and predicted it would decrease as we all focused on midnight. Variance Drop, Midnight, 1999-2000 In the first year, 1998-1999, we looked for a change in the average deviation, and compared Maxi- and Mini-celebration time zones. More at http://noosphere.princeton.edu