Paul askew rss 2014
•Download as PPTX, PDF•
0 likes•739 views
Presentation at the Royal Statistical Society Annual Conference, Sheffield 2014. Communication of Statistical Ideas Stream
Report
Share
Report
Share
Recommended
Statistics
This document discusses why statistics are fascinating and useful, even for those without a strong math background. It argues that statistics can reveal the disconnect between people's perceptions and reality. Simplicity is key when presenting statistics, which should use clear language and visuals to communicate insights effectively. These insights from statistics are relevant for managers in India to make better decisions by understanding perceptions versus facts.
Give Your Data an Extreme Makeover in Under 5 Minutes (SMX Advanced)
In this presentation I demonstrate some quick and easy steps you can do to sexy up your data and also delve into why this is important.
Implied volatility explanation
Implied volatility represents the volatility that makes the theoretical value of an option equal to its market price. It is typically expressed as an annual percentage that represents how much a stock's price could move up or down in one standard deviation. The document explains how to convert implied annual volatility into expected price movements over different time periods like days or weeks by taking the square root of the fraction of days relative to a year. For example, a stock with 35% annual implied volatility would be expected to move up or down around 2.2% within one day, 4.93% within five days, and 9.86% within 20 days. The document demonstrates how to use these expected movements to assess risk for options positions.
Consumer Snapshot - February 2017
The document summarizes consumer sentiment and spending behaviors based on a survey from Prosper Insights & Analytics. It finds that while consumer confidence declined slightly from its peak in December, it remains high at 50.1% and well above the 13-month average. Sentiment has surged 19% year-over-year. Consumers continue focusing spending on necessities and essential needs rather than wants. Savings and paying down debt also remain financial priorities over the next three months as consumers maintain a cautious approach.
3 ways to spot a bad statistic
Presentation on " ANALYSIS OF TED TALK BY MONA CHALABI ON 3 WAYS TO SPOT A BAD STATISTIC" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
The Rise in Economic Equality: Causes and Cures
Greg Mankiw, Robert M. Beren Professor of Economics at Harvard University, delivered the Hutchinson lecture at the University of Delaware's Lerner College of Business and Economics. E on “The Rise in Entitled "Economic Inequality: Causes and Cures.”
The best stats you’ve ever seen
The document discusses insights from statistical data that contradict common assumptions. It summarizes a study where participants estimated child mortality rates with only 1.8% accuracy, whereas the actual probability of a correct answer would be around 2.5%. The document advocates that managers make decisions based on analyzing data and facts rather than common sense. It also notes that available data, if properly analyzed, can provide understanding of tremendous changes and lead to better decision-making.
Scrum imposters
1) The document discusses a survey conducted on imposter syndrome among agile professionals. 205 individuals participated in an anonymous online survey about their experiences with imposter syndrome and use of agile methods.
2) The results found that 72% of respondents experienced imposter syndrome to some degree that interfered with their life. However, no significant relationship was found between experiencing imposter syndrome and an individual's ability to act according to scrum values.
3) The author suggests that support from teams, forward-thinking organizations, good project managers, and emphasis on emotional intelligence may explain why imposter syndrome does not affect adherence to agile values.
Recommended
Statistics
This document discusses why statistics are fascinating and useful, even for those without a strong math background. It argues that statistics can reveal the disconnect between people's perceptions and reality. Simplicity is key when presenting statistics, which should use clear language and visuals to communicate insights effectively. These insights from statistics are relevant for managers in India to make better decisions by understanding perceptions versus facts.
Give Your Data an Extreme Makeover in Under 5 Minutes (SMX Advanced)
In this presentation I demonstrate some quick and easy steps you can do to sexy up your data and also delve into why this is important.
Implied volatility explanation
Implied volatility represents the volatility that makes the theoretical value of an option equal to its market price. It is typically expressed as an annual percentage that represents how much a stock's price could move up or down in one standard deviation. The document explains how to convert implied annual volatility into expected price movements over different time periods like days or weeks by taking the square root of the fraction of days relative to a year. For example, a stock with 35% annual implied volatility would be expected to move up or down around 2.2% within one day, 4.93% within five days, and 9.86% within 20 days. The document demonstrates how to use these expected movements to assess risk for options positions.
Consumer Snapshot - February 2017
The document summarizes consumer sentiment and spending behaviors based on a survey from Prosper Insights & Analytics. It finds that while consumer confidence declined slightly from its peak in December, it remains high at 50.1% and well above the 13-month average. Sentiment has surged 19% year-over-year. Consumers continue focusing spending on necessities and essential needs rather than wants. Savings and paying down debt also remain financial priorities over the next three months as consumers maintain a cautious approach.
3 ways to spot a bad statistic
Presentation on " ANALYSIS OF TED TALK BY MONA CHALABI ON 3 WAYS TO SPOT A BAD STATISTIC" made as a task for the internship on "DATA ANALYTICS WITH MANAGERIAL APPLICATIONS" under Professor Sameer Mathur, IIM Lucknow. Submitted by TARANG JAIN,DTU
The Rise in Economic Equality: Causes and Cures
Greg Mankiw, Robert M. Beren Professor of Economics at Harvard University, delivered the Hutchinson lecture at the University of Delaware's Lerner College of Business and Economics. E on “The Rise in Entitled "Economic Inequality: Causes and Cures.”
The best stats you’ve ever seen
The document discusses insights from statistical data that contradict common assumptions. It summarizes a study where participants estimated child mortality rates with only 1.8% accuracy, whereas the actual probability of a correct answer would be around 2.5%. The document advocates that managers make decisions based on analyzing data and facts rather than common sense. It also notes that available data, if properly analyzed, can provide understanding of tremendous changes and lead to better decision-making.
Scrum imposters
1) The document discusses a survey conducted on imposter syndrome among agile professionals. 205 individuals participated in an anonymous online survey about their experiences with imposter syndrome and use of agile methods.
2) The results found that 72% of respondents experienced imposter syndrome to some degree that interfered with their life. However, no significant relationship was found between experiencing imposter syndrome and an individual's ability to act according to scrum values.
3) The author suggests that support from teams, forward-thinking organizations, good project managers, and emphasis on emotional intelligence may explain why imposter syndrome does not affect adherence to agile values.
Strategic planning session
The document discusses strategic planning for Cogito Electronics Company. It begins by asking employees for their thoughts on strategic planning and defines key terms. It then lists reasons why strategic planning can fail, such as an unknowable future or narrow thinking. The document contrasts good and bad decision making, noting pitfalls to avoid like failing to consider alternatives. It distinguishes between a company's mission statement, which defines who and what it is, and its strategic plan, which outlines how it will achieve its goals.
How Do You Do (Culture)?
The document discusses how to build a great organizational culture. It notes that the nature of work is changing, with more jobs requiring strong social and analytical skills. It explores what behaviors shape culture, such as being ambitious, inspired, and accountable. It suggests that culture can be influenced by focusing on mindsets, feelings and values to change behaviors. Some ways to change the context and influence behaviors are through onboarding processes, strategic hiring, coaching and training, and ensuring a great culture. It advises finding culture champions, piloting initiatives first, and continually measuring and refining efforts to build the desired culture over time.
5 THINGS TO KNOW ABOUT U.S. APRIL PERFORMANCE DATA
U.S. hotel performance indicators continued to reach all-time highs in April according to an analysis. RevPAR has now been growing for 62 consecutive months. New York City had the highest occupancy of any U.S. market at 86.9% in April. Supply growth hit a new low of +0.1% for the third straight month while three of the top 10 hottest under construction markets are located in Texas.
Lecture - 4 / Misc Thoughts
1. When studying percentages or averages, consider additional context like sample size, standard deviations, and absolute values to gain a fuller perspective.
2. Events that seem independent can be interconnected, so consider second and third-order effects and unique perspectives from interconnected systems.
3. Progress is not always linear. Don't get discouraged by slow initial stages as building a strong foundation is important. Later stages may also slow down due to overlooked details.
Storytelling in Technical Talks
Why is that when we present facts alone, we can get met with resistance? Is there another way to influence? We discuss how storytelling in technical talks, when done right, can make your ideas more memorable and influential.
How Designers Can Make the World a Happier Place
Design is powerful – it can generate excitement, bring joy, provoke anger, or trigger anxiety, sometimes all in the same interaction. From the big decisions about a product’s purpose all the way down to the myriad pixel-level arguments lost and won, designers have a great responsibility to safeguard the happiness of the users we serve. But what do we really know about the nature of happiness? And how can we actually make everyone happy?
In this talk, Kathi Kaiser (Co-Founder & COO, Centralis) deconstructs the concept of “happiness” and offer designers a framework for considering the emotional impact of their work. She explores the meaning, dimensions, and pre-conditions of happiness while examining the wide range of satisfying outcomes and their implications for design. Drawing on recent research in psychology as well as real-world design examples, you’ll learn when and how to evoke joy, humor, reassurance, comfort, and other positive feelings through applying a set of guiding principles for the pursuit of happiness.
Consumer Snapshot - December 2016
- Consumer sentiment surged in December 2016 with 54.5% of Americans confident in the economy, the highest level in over 10 years, as political anxiety eased somewhat.
- While confidence improved, practical purchase intentions and focus on necessities declined from last month, and one in ten plan to increase holiday spending by an average of $103 due to the election outcome.
- Men, higher-income households, and younger consumers under 45 have a more positive post-election holiday spending outlook compared to other groups.
Enlargement presentation
Enlargement refers to changing the size of an object while keeping its shape the same. The object can either increase or decrease in size. The scale factor indicates the amount of enlargement or reduction, with a scale factor less than 1 meaning the image is smaller than the original object, and a scale factor greater than 1 meaning the image is larger. The scale factor represents how many times bigger or smaller the image is compared to the original object.
VoxxedDaysMicroservices WIT
With less than 3% of young female students identifying a career in technology as their first choice, how are we ever going to achieve a diverse workforce and bridge the gender gap that persists in technology careers? But, don’t panic, this can all still change! Learn how even small events can have a lasting impact in encouraging more females into STEM and technology careers and explore how you can get involved to actively make a difference to the diversity of the tech industry.
The Landscape of Trust Research Partner webinar #1 July 2017 v1
This is the first of a series of community webinars, sharing initial analysis of the Landscape of Trust research. This will be a regular update around this work.
Evidence Based Management And Data Dashboard
Case study of the International School of Brussels and Bedrick Consulting working together to create a data dashboard
Data is love data viz best practices
Greg Nelson presented on best practices for data visualization and storytelling. He began with an introduction that outlined his background and experience in analytics, data science, and data visualization. Nelson then led the audience through an interactive exercise where they rated different data visualizations. He discussed key factors that affect how audiences consume and engage with data visualizations. Nelson outlined objectives like learning how to critically evaluate visualizations, identify misleading techniques, and understand the competencies important for visual storytelling. He emphasized that effective stories can motivate action by addressing emotions and provided tips for developing compelling data stories based on Pixar's rules of storytelling.
Nacubo Heaf Speech On Dashboards
The document discusses key performance indicator (KPI) dashboards and benchmarking for higher education institutions. It outlines the case for good communication of financial and operational data through dashboards to highlight potential problems. It describes effective dashboard principles like understanding context, perceiving and presenting data accurately and linking data to mission and strategy. Benchmarking is presented as a way to maintain viability by comparing performance to peers. Examples of common higher education KPIs and benchmarking groups are provided.
assignment of statistics 2.pdf
This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Oct 2017 Measurement Hour: Highlights from the Summit on the Future of Measur...
The document summarizes Katie Paine's presentation on measurement and the future of communications at the Summit on the Future of Communications. The presentation discussed various topics including how to understand an uncertain future through scenarios, dealing with risk and uncertainty, and the importance of understanding risk. It also provided case studies on how Southwest Airlines and Cisco use data and analytics in communications measurement. Key themes from research on measurement standards and examples from Exeter Health Resources were also summarized.
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
There are three kinds of lies
This document discusses how statistics can be misleading and manipulated. It provides examples of selection bias, such as only surveying a non-representative sample. Other ways statistics can be misleading include using biased questions, asking the wrong question, misleading graphs, implying causation from correlation, making results seem more precise than they are, and making up statistics. The document encourages critically analyzing data and graphs by checking for correct information, potential influence attempts, and proper scale usage. It also discusses how to manually manipulate real data and graphs to draw misleading conclusions.
webinar introducing measurement for improvement
This document provides an overview of a seminar on introducing measurement for improvement. The seminar agenda includes a welcome, introduction to the topic, and contact details. The presentation discusses using measurement to demonstrate whether improvement interventions are effective, provides examples of run charts to track data over time, and addresses challenges in measuring complex topics. Key points are that measurement for improvement can be kept simple, understanding baseline data is important, and capturing data over time can show whether unusual variation indicates an intervention worked. Resources for further information are also listed.
Edelman Trust Barometer 2016 - Argentina
Los niveles relativos de confianza en las instituciones individuales han subido y han caído. Ya desde el 2005, identificamos el crecimiento de la influencia de los pares, con “una persona como yo” estableciéndose como un vocero en el 2006, antes de que Facebook fuera muy conocido.
El año pasado observamos el papel esencial de la confianza en la innovación. Este año observamos algo nuevo: la creciente desigualdad de la confianza.
Remaining sane in the age of data
Martina Pugliese, a data science lead, discusses remaining sane in the age of data. She warns of common traps when analyzing and reporting on data, including confirmation bias, Simpson's paradox, and correlation not implying causation. Pugliese emphasizes inspecting data for biases, looking at sources critically, and questioning interests and motivations behind data reporting. While data can provide insights, true science requires scrutinizing data collection and analysis methods.
Health, Wealth & Happiness - Reflections on Research and the Greater Good
This document discusses several research studies and their relationship to policymaking in different areas such as education, health, and economics. It finds that research often has an indirect and gradual influence on policy and may help achieve objectives but cannot determine objectives which involve value judgments. Research in areas like education has had mixed impacts, with some studies clearly influencing policy and practice while others had no discernible impact. Research on health programs similarly found common problems across studies but also identified cultural differences that impacted outcomes. The document also describes how the Bank of England uses qualitative research through agencies to supplement quantitative data and models in making monetary policy decisions, with over 12,000 interviews conducted annually to provide a reality check and gauge economic mood.
Statistics for Management.pdf
This document contains an introduction to statistics and questions about key statistical concepts. It covers topics like:
- Measures of central tendency (mean, median, mode) and how they are calculated
- Measures of dispersion (range, mean deviation, quartiles)
- When to use different statistical measures based on the type of data
- Classification of data and different types of classifications
- Tabulation and methods of presenting data visually through graphs, charts and diagrams
More Related Content
What's hot
Strategic planning session
The document discusses strategic planning for Cogito Electronics Company. It begins by asking employees for their thoughts on strategic planning and defines key terms. It then lists reasons why strategic planning can fail, such as an unknowable future or narrow thinking. The document contrasts good and bad decision making, noting pitfalls to avoid like failing to consider alternatives. It distinguishes between a company's mission statement, which defines who and what it is, and its strategic plan, which outlines how it will achieve its goals.
How Do You Do (Culture)?
The document discusses how to build a great organizational culture. It notes that the nature of work is changing, with more jobs requiring strong social and analytical skills. It explores what behaviors shape culture, such as being ambitious, inspired, and accountable. It suggests that culture can be influenced by focusing on mindsets, feelings and values to change behaviors. Some ways to change the context and influence behaviors are through onboarding processes, strategic hiring, coaching and training, and ensuring a great culture. It advises finding culture champions, piloting initiatives first, and continually measuring and refining efforts to build the desired culture over time.
5 THINGS TO KNOW ABOUT U.S. APRIL PERFORMANCE DATA
U.S. hotel performance indicators continued to reach all-time highs in April according to an analysis. RevPAR has now been growing for 62 consecutive months. New York City had the highest occupancy of any U.S. market at 86.9% in April. Supply growth hit a new low of +0.1% for the third straight month while three of the top 10 hottest under construction markets are located in Texas.
Lecture - 4 / Misc Thoughts
1. When studying percentages or averages, consider additional context like sample size, standard deviations, and absolute values to gain a fuller perspective.
2. Events that seem independent can be interconnected, so consider second and third-order effects and unique perspectives from interconnected systems.
3. Progress is not always linear. Don't get discouraged by slow initial stages as building a strong foundation is important. Later stages may also slow down due to overlooked details.
Storytelling in Technical Talks
Why is that when we present facts alone, we can get met with resistance? Is there another way to influence? We discuss how storytelling in technical talks, when done right, can make your ideas more memorable and influential.
How Designers Can Make the World a Happier Place
Design is powerful – it can generate excitement, bring joy, provoke anger, or trigger anxiety, sometimes all in the same interaction. From the big decisions about a product’s purpose all the way down to the myriad pixel-level arguments lost and won, designers have a great responsibility to safeguard the happiness of the users we serve. But what do we really know about the nature of happiness? And how can we actually make everyone happy?
In this talk, Kathi Kaiser (Co-Founder & COO, Centralis) deconstructs the concept of “happiness” and offer designers a framework for considering the emotional impact of their work. She explores the meaning, dimensions, and pre-conditions of happiness while examining the wide range of satisfying outcomes and their implications for design. Drawing on recent research in psychology as well as real-world design examples, you’ll learn when and how to evoke joy, humor, reassurance, comfort, and other positive feelings through applying a set of guiding principles for the pursuit of happiness.
Consumer Snapshot - December 2016
- Consumer sentiment surged in December 2016 with 54.5% of Americans confident in the economy, the highest level in over 10 years, as political anxiety eased somewhat.
- While confidence improved, practical purchase intentions and focus on necessities declined from last month, and one in ten plan to increase holiday spending by an average of $103 due to the election outcome.
- Men, higher-income households, and younger consumers under 45 have a more positive post-election holiday spending outlook compared to other groups.
Enlargement presentation
Enlargement refers to changing the size of an object while keeping its shape the same. The object can either increase or decrease in size. The scale factor indicates the amount of enlargement or reduction, with a scale factor less than 1 meaning the image is smaller than the original object, and a scale factor greater than 1 meaning the image is larger. The scale factor represents how many times bigger or smaller the image is compared to the original object.
VoxxedDaysMicroservices WIT
With less than 3% of young female students identifying a career in technology as their first choice, how are we ever going to achieve a diverse workforce and bridge the gender gap that persists in technology careers? But, don’t panic, this can all still change! Learn how even small events can have a lasting impact in encouraging more females into STEM and technology careers and explore how you can get involved to actively make a difference to the diversity of the tech industry.
The Landscape of Trust Research Partner webinar #1 July 2017 v1
This is the first of a series of community webinars, sharing initial analysis of the Landscape of Trust research. This will be a regular update around this work.
What's hot (10)
5 THINGS TO KNOW ABOUT U.S. APRIL PERFORMANCE DATA
5 THINGS TO KNOW ABOUT U.S. APRIL PERFORMANCE DATA
The Landscape of Trust Research Partner webinar #1 July 2017 v1
The Landscape of Trust Research Partner webinar #1 July 2017 v1
Similar to Paul askew rss 2014
Evidence Based Management And Data Dashboard
Case study of the International School of Brussels and Bedrick Consulting working together to create a data dashboard
Data is love data viz best practices
Greg Nelson presented on best practices for data visualization and storytelling. He began with an introduction that outlined his background and experience in analytics, data science, and data visualization. Nelson then led the audience through an interactive exercise where they rated different data visualizations. He discussed key factors that affect how audiences consume and engage with data visualizations. Nelson outlined objectives like learning how to critically evaluate visualizations, identify misleading techniques, and understand the competencies important for visual storytelling. He emphasized that effective stories can motivate action by addressing emotions and provided tips for developing compelling data stories based on Pixar's rules of storytelling.
Nacubo Heaf Speech On Dashboards
The document discusses key performance indicator (KPI) dashboards and benchmarking for higher education institutions. It outlines the case for good communication of financial and operational data through dashboards to highlight potential problems. It describes effective dashboard principles like understanding context, perceiving and presenting data accurately and linking data to mission and strategy. Benchmarking is presented as a way to maintain viability by comparing performance to peers. Examples of common higher education KPIs and benchmarking groups are provided.
assignment of statistics 2.pdf
This document provides an introduction to statistics. It discusses descriptive statistics, which summarize and describe data, versus inferential statistics, which make generalizations about a population based on a sample. Descriptive statistics include measures like percentages, averages, and tables to characterize data. Inferential statistics are used to compare treatment groups and determine whether observed differences could occur by chance or are likely due to the treatments. The document provides examples of statistics encountered in various fields and emphasizes the importance of understanding statistics to evaluate claims critically.
Oct 2017 Measurement Hour: Highlights from the Summit on the Future of Measur...
The document summarizes Katie Paine's presentation on measurement and the future of communications at the Summit on the Future of Communications. The presentation discussed various topics including how to understand an uncertain future through scenarios, dealing with risk and uncertainty, and the importance of understanding risk. It also provided case studies on how Southwest Airlines and Cisco use data and analytics in communications measurement. Key themes from research on measurement standards and examples from Exeter Health Resources were also summarized.
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Homework #1
SOCY 3115
Spring 20
Read the Syllabus and FAQ on how to do your homework before beginning the assignment!
To get consideration for full credit, you must:
· Follow directions;
· Show all work required to arrive at answer (statistical calculations often require multiple steps, so you need to write these down, not just skip to the final answer)
· Use appropriate statistical notation at all times (e.g. if you are calculating a population mean, begin with the equation for population mean)
· Use units in your answer, where appropriate (e.g. a mean time would be “6.5 hours” rather than just “6.5”)
Understanding the Structure of Data
1. For the following rectangular dataset:
Id
Highest degree
Works full-time
Annual income cat
1
Did not grad HS
Yes
Low
2
HS dip
Yes
Low
3
HS dip
No
Med
4
BA
No
Low
5
BA
Yes
Med
6
MA
Yes
High
7
HS dip
Yes
Med
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For eachvariable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement?
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
2. For the following rectangular dataset:
Id
num_bdrms
num_bthrms
sqft
Ranch
1
4
3
3200
Yes
2
2
1.5
2800
Yes
3
2
1
1200
Yes
4
3
2
1500
No
5
2
2
1100
No
a. What is the unit-of-analysis of the dataset?
b. How many variables are in the dataset?
c. How many observations/cases are in the dataset?
d. For each variable that is not named “id”:
i. What is the variable name?
ii. What is the level-of-measurement? Before answering, be sure to consult the slide called “Level of measurement – language to use”. Use the formal language!
iii. What are the values for the variable?
iv. If you had to make a guess, what do you think the “question” was that was asked of the unit-of-analysis to get these data? (for example, if we had a continuous variable called “num_pets” the question might be “How many pets live in your household?”)
3. For each of the following questions (1) construct a dataset with one variable and three observations (2) add data that could have theoretically been collected (just make up the actual responses to the question); and (3) indicate the level-of-measurement of the variable. I’ve done two examples for you.
Example#1:
What is your current age? (individual is the unit-of-analysis)
idage
1 25
2 32
3 61
The age variable is continuous/interval ratio.
Example#2:
What is the size of this hospital based on number of beds? (hospital is the unit-of-analysis)? Answers can be small (1-100 beds), medium (101-500 beds), large (501 beds to 1000 beds), extra large (1001+ beds)
idhosp_size
1 med
2 med
3 ext ...
There are three kinds of lies
This document discusses how statistics can be misleading and manipulated. It provides examples of selection bias, such as only surveying a non-representative sample. Other ways statistics can be misleading include using biased questions, asking the wrong question, misleading graphs, implying causation from correlation, making results seem more precise than they are, and making up statistics. The document encourages critically analyzing data and graphs by checking for correct information, potential influence attempts, and proper scale usage. It also discusses how to manually manipulate real data and graphs to draw misleading conclusions.
webinar introducing measurement for improvement
This document provides an overview of a seminar on introducing measurement for improvement. The seminar agenda includes a welcome, introduction to the topic, and contact details. The presentation discusses using measurement to demonstrate whether improvement interventions are effective, provides examples of run charts to track data over time, and addresses challenges in measuring complex topics. Key points are that measurement for improvement can be kept simple, understanding baseline data is important, and capturing data over time can show whether unusual variation indicates an intervention worked. Resources for further information are also listed.
Edelman Trust Barometer 2016 - Argentina
Los niveles relativos de confianza en las instituciones individuales han subido y han caído. Ya desde el 2005, identificamos el crecimiento de la influencia de los pares, con “una persona como yo” estableciéndose como un vocero en el 2006, antes de que Facebook fuera muy conocido.
El año pasado observamos el papel esencial de la confianza en la innovación. Este año observamos algo nuevo: la creciente desigualdad de la confianza.
Remaining sane in the age of data
Martina Pugliese, a data science lead, discusses remaining sane in the age of data. She warns of common traps when analyzing and reporting on data, including confirmation bias, Simpson's paradox, and correlation not implying causation. Pugliese emphasizes inspecting data for biases, looking at sources critically, and questioning interests and motivations behind data reporting. While data can provide insights, true science requires scrutinizing data collection and analysis methods.
Health, Wealth & Happiness - Reflections on Research and the Greater Good
This document discusses several research studies and their relationship to policymaking in different areas such as education, health, and economics. It finds that research often has an indirect and gradual influence on policy and may help achieve objectives but cannot determine objectives which involve value judgments. Research in areas like education has had mixed impacts, with some studies clearly influencing policy and practice while others had no discernible impact. Research on health programs similarly found common problems across studies but also identified cultural differences that impacted outcomes. The document also describes how the Bank of England uses qualitative research through agencies to supplement quantitative data and models in making monetary policy decisions, with over 12,000 interviews conducted annually to provide a reality check and gauge economic mood.
Statistics for Management.pdf
This document contains an introduction to statistics and questions about key statistical concepts. It covers topics like:
- Measures of central tendency (mean, median, mode) and how they are calculated
- Measures of dispersion (range, mean deviation, quartiles)
- When to use different statistical measures based on the type of data
- Classification of data and different types of classifications
- Tabulation and methods of presenting data visually through graphs, charts and diagrams
Reuters/Ipsos Data: Core Political (04/03/2019)
This document summarizes the results of an Ipsos poll conducted for Reuters between March 26th and April 1st, 2019. It surveyed 3,962 American adults, including breakdowns by political party. Key findings include: 35% said the country is heading in the right direction, while 54% said wrong track; healthcare was cited as the most important problem facing the US at 19%; and 42% approved of Trump's overall job performance, while 53% disapproved. Approval and disapproval ratings are also provided for various issues.
Edelman Trust Barometer Special Flash Poll - Mexico’s Trust Challenges
Edelman Trust Barometer Special Flash Poll on Mexico’s Trust Challenges — the U.S. Perspective — conducted in mid-November in the U.S. of 1,000 people in the general population 18 years and older.
The findings provide important context on the bilateral relationship as NAFTA negotiations come to the finish line and Mexico begins its presidential campaign.
Newsletter Winter 2015- Vol 22 No 2 -final revision
This document provides an overview of recent developments in the ECU Department of Economics from the Chair's perspective. Some key points:
- The Chair, Dr. Richard Ericson, is stepping down after 12 years in the role. A search for a new Chair is underway.
- The Department has grown substantially over Dr. Ericson's tenure, with faculty increasing from 11 to 19 and majors from 110 to 180. Over 1100 alumni have graduated.
- The Department faces budget cuts but remains strong in teaching and research. A new faculty member, Dr. Jacob Hochard, was recently hired.
- 37 students graduated with BA/BS degrees in fall 2014. Guest speaker at the graduation ceremony
Reuters/Ipsos Core Political Survey: Presidential Approval Tracker (03/11/2020)
This document provides a 3-sentence summary of an Ipsos poll conducted for Thomson Reuters between March 9-10, 2020. It summarizes the results of an online poll of 1,113 Americans, including 457 Democratic and 374 Republican registered voters. The document outlines the methodology used in the poll and provides data on topics including views on the direction of the country, the most important problems facing America, approval ratings of President Trump, and political party identification. It also includes an appendix describing how Bayesian credibility intervals are calculated for the poll results.
Telling A Story With Data
Data surrounds us. In business and personal life. At work and on the go. But how do we make sense of it, or more specifically, how do we allow others to make sense of it. Learn how to deliver data ... <reports>.
Reuters/Ipsos Data: Core Political (09/13/2017)
Following a bump in approval last week, President Trump’s job approval has dropped back down to pre-Harvey/Irma levels, now at 35% (down from 40% last week). Trump’s numbers have also dropped across all specific policy areas. Notably, as the Republicans begin to broach tax reform, Trump’s approval on the US economy dropped by 5 points from 47% last week to 42%. Despite the drop, Trump still enjoys higher approval than Congress, which is now at 24%.
Healthcare continues to be top of mind for Americans (17%), distantly followed by the economy (12%) and terrorism (12%). Concern about the environment rose one point from 4% last week to 5% this week.
Final Deck
The document discusses recommendations for how the Catholic Charities of St. Louis can benchmark its refugee resettlement program. It suggests that the Charities collect standardized data across branches and group cities based on economic and population factors to allow for more effective comparisons. A survey found that stakeholders thought the current definition of "self-sufficiency" was inadequate and should be redefined. The recommendations aim to foster discussion, improve understanding of strengths and weaknesses, and enhance refugee outcomes.
Honorhealth Case Study
Honorhealth Case Study discusses the merger between Scottsdale Healthcare and John C. Lincoln to form HonorHealth. The merger has led to some resistance to change from staff during its first 18 months as one organization. Issues include modified staffing ratios, new and changed policies, and disagreements over who is responsible for changes. HonorHealth needs to ensure employees understand how the changes fit into the organization's future and their role in it. Evaluation is also needed to show changes are more efficient and cost effective.
Similar to Paul askew rss 2014 (20)
Oct 2017 Measurement Hour: Highlights from the Summit on the Future of Measur...
Oct 2017 Measurement Hour: Highlights from the Summit on the Future of Measur...
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Health, Wealth & Happiness - Reflections on Research and the Greater Good
Health, Wealth & Happiness - Reflections on Research and the Greater Good
Edelman Trust Barometer Special Flash Poll - Mexico’s Trust Challenges
Edelman Trust Barometer Special Flash Poll - Mexico’s Trust Challenges
Newsletter Winter 2015- Vol 22 No 2 -final revision
Newsletter Winter 2015- Vol 22 No 2 -final revision
Reuters/Ipsos Core Political Survey: Presidential Approval Tracker (03/11/2020)
Reuters/Ipsos Core Political Survey: Presidential Approval Tracker (03/11/2020)
More from Paul Askew
UK Statistical Policy Landscape
Presentation at the Royal Statistical Society's International Conference, Newcastle, September 2013.
This infographic presents and inter-relates the key components of the UK's statistical policy landscape. The scope includes key bodies, legislation, policy, guidance, and key contextual factors and influences.
This provides a single strategic visual overview and reference source, illustrating the evolution, relationship and synthesis of those components over the last decade. This also helps to illustrate to a lay audience the extent and development of the background rigour and governance in public data management, use and communication.
Paul Askew
paul.askew@speakingdata.org.uk
A Framework for Statistical Performance
The document presents a framework for understanding statistical performance. It outlines operational and strategic drivers for managing statistics about performance. Operationally, performance is important to outcomes like safety and education. Strategically, there is more data and emphasis on using data for decisions. The framework provides a macro level overview of the data, analysis, insight, and product cycle. It also details an analytical level approach involving a snapshot, trend, benchmark, and target analysis to understand a measure over time and versus comparisons. The goal is to extract insight from data to improve performance.
Projects - Management & Delivery
This document outlines an approach to project management that focuses on both effectiveness and visibility. It emphasizes managing projects through established processes and tools to ensure the intended purpose and results are delivered, while also making sure project progress and outcomes are clearly communicated so people understand what is being worked on and accomplished.
Fabric - Performance Framework
The document outlines a framework for developing an effective corporate performance information system including an overall framework, criteria for individual measures, and components for managing performance information. The framework consists of 6 criteria: focused, integrated, balanced, cost effective, appropriate, and robust. Individual measures should be relevant, attributable, well-defined, reliable, comparable, timely, verifiable, and avoid perverse incentives. Key components include aligning measures with strategic objectives, monitoring results, evaluating performance, and comparing performance over time.
Statistically speaking
This document contains a collection of quotes about statistics from various notable figures throughout history. It explores how statistics can be used to both illuminate and mislead, as well as the importance of statistical thinking. Some quotes praise statistics for allowing social laws to be understood, while others warn against putting too much faith in numbers without considering what is not being said. Overall, the quotes reflect on the power yet limitations of statistical analysis.
Analytical Insight - Road Map
The document outlines the data analysis process which includes collecting and recording data, defining requirements and constraints, validating the data, and storing the data. It then discusses deriving insights from the data by looking at trends, benchmarks, and targets. The analysis process involves comparing data across time periods and stakeholders. The outputs of the process are analytical insights and products.
Analytical insight portrait
This document outlines a 4 step process: Step 1 involves considering different perspectives on an issue, Step 2 synthesizes information from various sources, Step 3 determines the key messages to convey, Step 4 shares insights with relevant stakeholders.
Data smoothie
This document compares smoothed data to real data using different time steps and trends, showing how smoothed data takes fluctuations out of real data by using a rolling average over 12 months. Ten examples are given of different real data patterns and their corresponding smoothed data patterns.
For whom the buck stops
Analysis of Government Department budgets and corresponding senior staff salaries.
www.speakingdata.org.uk
Bye VFM, Hello BYP
See the complete content at...
http://speakingdata.blogspot.com/2010/12/value-for-money-for-time.html
Comprehensive Spending Review - Big Picture
Full content here...
http://speakingdata.blogspot.com/2010/11/comprehensive-spending-review-big.html
What a performance
The document outlines a process for analyzing key metrics over time and in comparison to others. It includes taking a snapshot of the current metric, analyzing its trend over time, benchmarking it against peer metrics, and consolidating the assessment by comparing trends and trajectories both over time and to other metrics. Graphs are provided to illustrate trends and trajectories.
Statutory Statistical
The document outlines principles and protocols for official statistics in the UK. It discusses how official statistics should meet user needs, be impartial, maintain integrity, use sound methods, ensure confidentiality, impose proportionate burden, have sufficient resources, and be frank and accessible. It also covers protocols for user engagement, release practices, and using administrative sources for statistics.
Web Analytics: A new Statistical Domain
Presentation to the Royal Statistical Society's International Conference, Brighton, September 2010.
Paul Askew
www.speakingdata.org.uk
More from Paul Askew (14)
Recently uploaded
Population Growth in Bataan: The effects of population growth around rural pl...
A population analysis specific to Bataan.
一比一原版(CU毕业证)卡尔顿大学毕业证如何办理
原件一模一样【微信：95270640】【卡尔顿大学毕业证CU学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理卡尔顿大学毕业证CU学位证本科学位证成绩单【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理卡尔顿大学毕业证本科学位证成绩单CU学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版(UO毕业证)渥太华大学毕业证如何办理
原件一模一样【微信：95270640】【渥太华大学毕业证UO学位证成绩单】【微信：95270640】（留信学历认证永久存档查询）采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信：95270640】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信：95270640】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份【微信：95270640】
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
→ 【关于价格问题（保证一手价格）
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：可来公司面谈，可签订合同，会陪同客户一起到教育部认证窗口递交认证材料，客户在教育部官方认证查询网站查询到认证通过结果后付款，不成功不收费！
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】外观非常精致，由特殊纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理渥太华大学毕业证UO学位证毕业证offer【微信：95270640 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理渥太华大学毕业证毕业证offerUO学位证【微信：95270640 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
The Ipsos - AI - Monitor 2024 Report.pdf
According to Ipsos AI Monitor's 2024 report, 65% Indians said that products and services using AI have profoundly changed their daily life in the past 3-5 years.
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Dynamic policy enforcement is becoming an increasingly important topic in today’s world where data privacy and compliance is a top priority for companies, individuals, and regulators alike. In these slides, we discuss how LinkedIn implements a powerful dynamic policy enforcement engine, called ViewShift, and integrates it within its data lake. We show the query engine architecture and how catalog implementations can automatically route table resolutions to compliance-enforcing SQL views. Such views have a set of very interesting properties: (1) They are auto-generated from declarative data annotations. (2) They respect user-level consent and preferences (3) They are context-aware, encoding a different set of transformations for different use cases (4) They are portable; while the SQL logic is only implemented in one SQL dialect, it is accessible in all engines.
#SQL #Views #Privacy #Compliance #DataLake
Intelligence supported media monitoring in veterinary medicine
Media monitoring in veterinary medicien
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Navigating today's data landscape isn't just about managing workflows; it's about strategically propelling your business forward. Apache Airflow has stood out as the benchmark in this arena, driving data orchestration forward since its early days. As we dive into the complexities of our current data-rich environment, where the sheer volume of information and its timely, accurate processing are crucial for AI and ML applications, the role of Airflow has never been more critical.
In my journey as the Senior Engineering Director and a pivotal member of Apache Airflow's Project Management Committee (PMC), I've witnessed Airflow transform data handling, making agility and insight the norm in an ever-evolving digital space. At Astronomer, our collaboration with leading AI & ML teams worldwide has not only tested but also proven Airflow's mettle in delivering data reliably and efficiently—data that now powers not just insights but core business functions.
This session is a deep dive into the essence of Airflow's success. We'll trace its evolution from a budding project to the backbone of data orchestration it is today, constantly adapting to meet the next wave of data challenges, including those brought on by Generative AI. It's this forward-thinking adaptability that keeps Airflow at the forefront of innovation, ready for whatever comes next.
The ever-growing demands of AI and ML applications have ushered in an era where sophisticated data management isn't a luxury—it's a necessity. Airflow's innate flexibility and scalability are what makes it indispensable in managing the intricate workflows of today, especially those involving Large Language Models (LLMs).
This talk isn't just a rundown of Airflow's features; it's about harnessing these capabilities to turn your data workflows into a strategic asset. Together, we'll explore how Airflow remains at the cutting edge of data orchestration, ensuring your organization is not just keeping pace but setting the pace in a data-driven future.
Session in https://budapestdata.hu/2024/04/kaxil-naik-astronomer-io/ | https://dataml24.sessionize.com/session/667627
Build applications with generative AI on Google Cloud
We will explore Vertex AI - Model Garden powered experiences, we are going to learn more about the integration of these generative AI APIs. We are going to see in action what the Gemini family of generative models are for developers to build and deploy AI-driven applications. Vertex AI includes a suite of foundation models, these are referred to as the PaLM and Gemini family of generative ai models, and they come in different versions. We are going to cover how to use via API to: - execute prompts in text and chat - cover multimodal use cases with image prompts. - finetune and distill to improve knowledge domains - run function calls with foundation models to optimize them for specific tasks. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative ai industry trends.
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
by
Timothy Spann
Principal Developer Advocate
https://budapestdata.hu/2024/en/
https://budapestml.hu/2024/en/
tim.spann@zilliz.com
https://www.linkedin.com/in/timothyspann/
https://x.com/paasdev
https://github.com/tspannhw
https://www.youtube.com/@flank-stack
milvus
vector database
gen ai
generative ai
deep learning
machine learning
apache nifi
apache pulsar
apache kafka
apache flink
一比一原版(UO毕业证)渥太华大学毕业证如何办理
UO毕业证录取书【微信95270640】购买（渥太华大学毕业证成绩单硕士学历）Q微信95270640代办UO学历认证留信网伪造渥太华大学学位证书精仿渥太华大学本科/硕士文凭证书补办渥太华大学 diplomaoffer,Transcript购买渥太华大学毕业证成绩单购买UO假毕业证学位证书购买伪造渥太华大学文凭证书学位证书,专业办理雅思、托福成绩单，学生ID卡，在读证明，海外各大学offer录取通知书，毕业证书，成绩单，文凭等材料:1:1完美还原毕业证、offer录取通知书、学生卡等各种在读或毕业材料的防伪工艺（包括 烫金、烫银、钢印、底纹、凹凸版、水印、防伪光标、热敏防伪、文字图案浮雕，激光镭射，紫外荧光，温感光标）学校原版上有的工艺我们一样不会少，不论是老版本还是最新版本，都能保证最高程度还原，力争完美以求让所有同学都能享受到完美的品质服务。
文凭办理流程：
1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：微信95270640我们有专业老师帮你查询）；
2开始安排制作毕业证成绩单电子图；
3毕业证成绩单电子版做好以后发送给您确认；
4毕业证成绩单电子版您确认信息无误之后安排制作成品；
5成品做好拍照或者视频给您确认；
6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。
7完成交易删除客户资料
高精端提供以下服务：
一：渥太华大学渥太华大学毕业证文凭证书全套材料从防伪到印刷水印底纹到钢印烫金
二：真实使馆认证（留学人员回国证明）使馆存档
三：真实教育部认证教育部存档教育部留服网站可查
四：留信认证留学生信息网站可查
五：与学校颁发的相关证件1:1纸质尺寸制定（定期向各大院校毕业生购买最新版本毕,业证成绩单保证您拿到的是鲁昂大学内部最新版本毕业证成绩单微信95270640）
A.为什么留学生需要操作留信认证?
留信认证全称全国留学生信息服务网认证,隶属于北京中科院。①留信认证门槛条件更低,费用更美丽,并且包过,完单周期短,效率高②留信认证虽然不能去国企,但是一般的公司都没有问题,因为国内很多公司连基本的留学生学历认证都不了解。这对于留学生来说,这就比自己光拿一个证书更有说服力,因为留学学历可以在留信网站上进行查询!
B.为什么我们提供的毕业证成绩单具有使用价值？
查询留服认证是国内鉴别留学生海外学历的唯一途径但认证只是个体行为不是所有留学生都操作所以没有办理认证的留学生的学历在国内也是查询不到的他们也仅仅只有一张文凭。所以这时候我们提供的和学校颁发的一模一样的毕业证成绩单就有了使用价值。只硕大的蛇皮袋手里拎着长铁钩正站在门口朝黑色的屋内张望不好坏人小偷山娃一怔却也灵机一动立马仰起头双手拢在嘴边朝楼上大喊：“爸爸爸——有人找——那人一听朝山娃尴尬地笑笑悻悻地走了山娃立马“嘭的一声将铁门锁死心却咚咚地乱跳当山娃跟父亲说起这事时父亲很吃惊抚摸着山娃的头说还好醒得及时要不家早被人掏空了到时连电视也没得看啰不过父亲还是夸山娃能临危不乱随机应变有胆有谋山娃笑笑说那都是书上学的看童话和小说时多
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
This webinar will explore cutting-edge, less familiar but powerful experimentation methodologies which address well-known limitations of standard A/B Testing. Designed for data and product leaders, this session aims to inspire the embrace of innovative approaches and provide insights into the frontiers of experimentation!
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Embark on a captivating financial journey with 'Financial Odyssey,' our hackathon project. Delve deep into the past performance of two companies as we employ an array of financial statement analysis techniques. From ratio analysis to trend analysis, uncover insights crucial for informed decision-making in the dynamic world of finance."
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
原版办理【微信号:BYZS866】【南十字星大学毕业证(SCU毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版巴斯大学毕业证(Bath毕业证书)学历如何办理
原版办理【微信号:BYZS866】【巴斯大学毕业证(Bath毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【关于学历材料质量】
我们承诺采用的是学校原版纸张（原版纸质、底色、纹路、）我们工厂拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有成品以及工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
Recently uploaded (20)
Challenges of Nation Building-1.pptx with more important
Challenges of Nation Building-1.pptx with more important
Population Growth in Bataan: The effects of population growth around rural pl...
Population Growth in Bataan: The effects of population growth around rural pl...
Monthly Management report for the Month of May 2024
Monthly Management report for the Month of May 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
ViewShift: Hassle-free Dynamic Policy Enforcement for Every Data Lake
Intelligence supported media monitoring in veterinary medicine
Intelligence supported media monitoring in veterinary medicine
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Orchestrating the Future: Navigating Today's Data Workflow Challenges with Ai...
Build applications with generative AI on Google Cloud
Build applications with generative AI on Google Cloud
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
06-12-2024-BudapestDataForum-BuildingReal-timePipelineswithFLaNK AIM
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
"Financial Odyssey: Navigating Past Performance Through Diverse Analytical Lens"
Paul askew rss 2014
- 1. Five Steps for Analytical Insight Paul Askew CONFERENCE SEPTEMBER 2014 SHEFFIELD
- 2. Five Steps for Analytical Insight - Contents A. Context B. Five Steps C. Key Message
- 3. The only statistics you can trust are those you falsified yourself. Winston Churchill Statistical thinking will one day be as necessary for efficient citizenship as the ability to read and write. HG Wells You cannot ask us to take sides against arithmetic. Winston Churchill The War Office kept three sets of figures: one to mislead the public, another to mislead the Cabinet, and a third to mislead itself. Herbert Asquith It is by the art of Statistics that law in the social sphere can be ascertained and codified, and certain aspects of the character of God thereby revealed. The study of statistics is thus a religious service. Florence Nightingale Smoking is one of the leading causes of statistics. Fletcher Knebel If you want to inspire confidence, give plenty of statistics. It does not matter that they should be accurate, or even intelligible, as long as there is enough of them. Lewis Carroll A small error at the outset can lead to great errors in the final conclusions. Saint Thomas Aquinas Facts are stubborn, but statistics are more pliable. Mark Twain Errors using inadequate data are much less than those using no data at all. Charles Babbage
- 4. Skills for Life Survey 2011 (England) Department for Business Innovation and Skills % Adults at GCSE+ Levels The numeracy challenge is big and getting bigger… 7.5m adults with GCSE+ 17m adults at primary school level Maths
- 5. A Framework for Understanding Statistical Performance Paul Askew
- 6. Policy Gap Aging Population 2x [36%, actually 16% 65+] Immigration 2x [31%, actually 13% immigrant] Religion 5x [24%, actually 5% muslim] Teenage Pregnancy 25x [15%, actually 0.6% under 15 girls annually] Benefit Fraud 34x [£24/£100, actually 70p/£100] RSS & Ipsos Mori
- 7. Confirmation Bias Clustering Illusion Hindsight Bias Overconfidence Effect Incentive Super Response Tendency Illusion of Control Endowment Effect Scarcity Error Loss Aversion Cognitive Dissonance Hyperbolic discounting The Art of Thinking Clearly, Dobelli Conjunction Fallacy Forecast Illusion Fundamental Attribution Error Contrast Effect Availability Bias Outcome Bias
- 9. Operational Delivery Methodological Leadership
- 11. ✗ Yes but it’s up compared the same month last year ✗ Yes but its up for the calendar year so far ✗ Yes but they are reducing faster than we are this year Performance Pantomime ✓ Burglary is down compared to last month ✓ Yes but it’s down overall for the financial year to date ✓ Yes but we’re still better than our neighbours ✓ “Yes but we’re still on target”
- 12. Five Steps for Analytical Insight - Contents A. Context B. Five Steps C. Key Message
- 13. B. Five Steps 1. Snapshot 2. Trend 3. Benchmark 4. Trajectory 5. Target
- 14. 1. Snapshot 5. Define 6. Specify 8. Record 7. Collect 9. Enter 10. Process 12. Store 11. Validate Plan Implement Manage 1. Purpose 2. Require-ments 4. Design 3. Const-raints
- 15. Aggregated Data or Real Data Aggregated Data This aggregated (averaged) data is derived from any of these underlying raw data examples. Two month step Three month step Six month step Increasing Decreasing Increasing convergence High and low Highs and lows Decreasing convergence Smoothed Data – 12 month rolling average Example Real Data Notes: Real data for 12 months, previous 12 months is exactly the same, to create 12 month rolling average (mean).
- 16. 2. Five Steps 1. Snapshot 2. Trend 3. Benchmark 4. Trajectory 5. Target Purpose Week National Future Estimate Requirements Month Regional Intervention Constraints Quarter Neighbours Impact Design Six Months Similar Define Annual Specific Specify FYTD Collect Record Enter Process Validate Storage
- 17. Value 1. Snapshot – we have a number which is important to us Time
- 18. Value Time 2. Trend – what’s happening over time (s)
- 19. Value 3. Benchmark – how this measure compares to others Time
- 20. Value 3. Benchmark-Trend - comparison to others over time Time
- 21. Value Time 4. Trajectory - the future for our measure
- 22. Value Time 4. Trajectory Benchmark - comparison to others
- 23. Value Time 5. Target Consideration
- 24. Five Steps for Analytical Insight - Contents A. Context B. Five Steps C. Key Message
- 25. C. Key Message Framework Multidimensional measures - times - comparisons
- 26. C. Key Message 42
- 27. Five Steps for Analytical Insight Thank you CONFERENCE SEPTEMBER 2014 SHEFFIELD