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Київська школа економіки (в минулому EERC) – це навчальний заклад світового рівня. Школа пропонує дворічні магістерські програми за напрямами “Економічний аналіз” та “Фінансова економіка”. Всі професори, які викладають у Школі, отримали дипломи Ph.D в університетах США та Західної Європи. Вони постійно проводять дослідження на різноманітні теми і готові давати інтерв'ю та коментарі для ЗМІ.
Київська школа економіки. Можливості для журналістів
Київська школа економіки. Можливості для журналістів
Nataly Nikitina
The Kyiv School of Economics (KSE), founded by the Economics Education and Research Consortium (EERC) (www.eerc.ru) and the Victor Pinchuk Foundation (www.pinchukfund.org), is a world-class institution with a reputation for excellence both in Ukraine and internationally. It aims to rank among the top schools in Central and Eastern Europe, producing a future generation of world-class economists. In doing so, it helps to improve the economic, business, and policymaking environments of Ukraine and neighbouring countries.
Kyiv School of Economics. 19.04.2011
Kyiv School of Economics. 19.04.2011
Nataly Nikitina
Київська школа економіка (в минулому EERC) – це навчальний заклад світового рівня. Школа пропонує дворічні магістерські програми за напрямами “Економічний аналіз” та “Фінансова економіка”. Навчальна програма відповідає міжнародним вимогам і складена за зразком найкращих економічних та фінансово-економічних магістерських програм Північної Америки та Західної Європи. Міжнародне визнання Школи підтверджується тим, що випускники отримують дипломи магістрів економіки (MA in Economics) не лише KSE, а й Університету Х'юстона, з яким Школа має домовленість. Сайт школи - kse.org.ua.
Kse presentation October 2010
Kse presentation October 2010
Nataly Nikitina
Macro bornukova
Macro bornukova
Nataly Nikitina
KSE Open Lecture with Dr. Charles Calomiris (Columbia University Graduate School of Business) on "An Incentive-Robust Program for Financial Reform" was held on April 12, 2011.
Dr. Charles Calomiris "An Incentive-Robust Program for Financial Reform"
Dr. Charles Calomiris "An Incentive-Robust Program for Financial Reform"
Nataly Nikitina
Презентація професора Київської школи економіки Олесі Верченко про види плагіату та про те, як за нього карають у західних вишах.
Етика Наукових Публікацій та Системний Підхід до Плагіату
Етика Наукових Публікацій та Системний Підхід до Плагіату
Nataly Nikitina
Презентация Владимира Вахитова
Презентация Владимира Вахитова
Nataly Nikitina
Киевская школа экономики (в прошлом EERC) – это учебное заведение мирового уровня. Школа предлагает двухгодичные магистерские программы по направлениям “Экономический анализ” и “Финансовая экономика”. Все профессора, которые преподают в Школе, получили дипломы Ph.D в университетах США и Западной Европы. Они постоянно проводят исследования на различные темы и готовы давать интервью и комментарии для СМИ.
Киевская школа экономики. Возможности для журналистов
Киевская школа экономики. Возможности для журналистов
Nataly Nikitina
Recomendados
Київська школа економіки (в минулому EERC) – це навчальний заклад світового рівня. Школа пропонує дворічні магістерські програми за напрямами “Економічний аналіз” та “Фінансова економіка”. Всі професори, які викладають у Школі, отримали дипломи Ph.D в університетах США та Західної Європи. Вони постійно проводять дослідження на різноманітні теми і готові давати інтерв'ю та коментарі для ЗМІ.
Київська школа економіки. Можливості для журналістів
Київська школа економіки. Можливості для журналістів
Nataly Nikitina
The Kyiv School of Economics (KSE), founded by the Economics Education and Research Consortium (EERC) (www.eerc.ru) and the Victor Pinchuk Foundation (www.pinchukfund.org), is a world-class institution with a reputation for excellence both in Ukraine and internationally. It aims to rank among the top schools in Central and Eastern Europe, producing a future generation of world-class economists. In doing so, it helps to improve the economic, business, and policymaking environments of Ukraine and neighbouring countries.
Kyiv School of Economics. 19.04.2011
Kyiv School of Economics. 19.04.2011
Nataly Nikitina
Київська школа економіка (в минулому EERC) – це навчальний заклад світового рівня. Школа пропонує дворічні магістерські програми за напрямами “Економічний аналіз” та “Фінансова економіка”. Навчальна програма відповідає міжнародним вимогам і складена за зразком найкращих економічних та фінансово-економічних магістерських програм Північної Америки та Західної Європи. Міжнародне визнання Школи підтверджується тим, що випускники отримують дипломи магістрів економіки (MA in Economics) не лише KSE, а й Університету Х'юстона, з яким Школа має домовленість. Сайт школи - kse.org.ua.
Kse presentation October 2010
Kse presentation October 2010
Nataly Nikitina
Macro bornukova
Macro bornukova
Nataly Nikitina
KSE Open Lecture with Dr. Charles Calomiris (Columbia University Graduate School of Business) on "An Incentive-Robust Program for Financial Reform" was held on April 12, 2011.
Dr. Charles Calomiris "An Incentive-Robust Program for Financial Reform"
Dr. Charles Calomiris "An Incentive-Robust Program for Financial Reform"
Nataly Nikitina
Презентація професора Київської школи економіки Олесі Верченко про види плагіату та про те, як за нього карають у західних вишах.
Етика Наукових Публікацій та Системний Підхід до Плагіату
Етика Наукових Публікацій та Системний Підхід до Плагіату
Nataly Nikitina
Презентация Владимира Вахитова
Презентация Владимира Вахитова
Nataly Nikitina
Киевская школа экономики (в прошлом EERC) – это учебное заведение мирового уровня. Школа предлагает двухгодичные магистерские программы по направлениям “Экономический анализ” и “Финансовая экономика”. Все профессора, которые преподают в Школе, получили дипломы Ph.D в университетах США и Западной Европы. Они постоянно проводят исследования на различные темы и готовы давать интервью и комментарии для СМИ.
Киевская школа экономики. Возможности для журналистов
Киевская школа экономики. Возможности для журналистов
Nataly Nikitina
Gramener's Director - Client success, Shravan Kumar A, delivered an online session to the students of Praxis Business School. In his session he talked about how converting data into stories can benefit businesses and enable quick decision making. Furthermore, he shared approaches to create data stories along with some use cases and case studies we solved at Gramener to benefit our clients. Check out our initiative to teach data storytelling to data scientists and analysts so that they can think out of the box and create wonderful data stories for their stakeholders: https://gramener.com/data-storytelling-workshop
Storyfying your Data: How to go from Data to Insights to Stories
Storyfying your Data: How to go from Data to Insights to Stories
Gramener
Gramener's Shravan Kumar took a virtual session with the students of IIM Shillong. It was a fantastic interaction and the participants were keen to understand how data and storytelling are making a difference to new-age businesses. Check out the slides from the session
The value of storytelling through data
The value of storytelling through data
Gramener
This presentation was shared by Gramener's Kanishk Kumar Abhishek during his guest lecture session at School of Business Management, NMIMS Mumbai. Check out Gramener's data storytelling workshop for analysts and data scientists at https://gramener.com/data-storytelling-workshop
Humanizing Data Storytelling for Greater Business Impact
Humanizing Data Storytelling for Greater Business Impact
Gramener
Interpretation of Data and Statistical Fallacies
Interpretation of Data and Statistical Fallacies
Interpretation of Data and Statistical Fallacies
RHIMRJ Journal
Chief data and analytics officers (CDAO) Apex Winter 2020 conference in Orlando was an intimate gathering of analytics leaders from across the world. Gramener's Co-founder and COO, Naveen Gattu, spoke about the importance of storytelling on easy data consumption and impact on data-driven decision making for business users.
Storytelling for analytics | Naveen Gattu | CDAO Apex 2020
Storytelling for analytics | Naveen Gattu | CDAO Apex 2020
Gramener
Outline: - Fact checking – what is it and why do we need it? - False information online - Content-based automatic fact checking - Explainability – what is it and why do we need it? - Making the right predictions for the right reasons - Model training pipeline - Explainable fact checking – some first solutions - Rationale selection - Generating free-text explanations - Wrap-up
Towards Explainable Fact Checking (DIKU Business Club presentation)
Towards Explainable Fact Checking (DIKU Business Club presentation)
Isabelle Augenstein
50 Percent of Data Science Projects Fail at Consumption: Can Storytelling Be Your Game Changer Growth of Self Service BI has generated a lot of dashboards, but “lots” does not always mean “good” or “useful”. • While advances in AI/ML lead to deeper insights, business teams struggle with the adoption of algorithms and consumption • How can data officers and analytics leaders get better business ROI from their data science investments? • This session will show you how to unleash the power of data storytelling for business decision- making, using industry examples
Data Storytelling - Game changer for Analytics
Data Storytelling - Game changer for Analytics
Gramener
Plenary Talk by Danny Dorling held at the Royal Statistical Society Annual Conference, Brighton 17th September 2010
Statistical Shenanigans: From Sweet Peas to Nobel Prizes, how much that we ta...
Statistical Shenanigans: From Sweet Peas to Nobel Prizes, how much that we ta...
Danny Dorling
Scenario: You are a lieutenant in charge of an undercover strike force team, charged with the responsibility of apprehending fugitives from justice. Your team has been criticized by the local media for some of its members' actions in carrying out their responsibilities, such as using questionable methods that could be seen as potential violation of some individual civil rights. Your team has been very effective in carrying out its assigned duties, resulting in an 80% apprehension rate. You have been advised by the chief that all he wants is results, not excuses. He wants you to use whatever means are necessary to apprehend fugitives because anything less would reflect badly on the department and his leadership. He reminds you that he has the firm backing of the mayor and city commission in how he runs the department. The next day, a news reporter informs you that he is working on a story regarding the apprehension of child rapist. Information he has gathered indicates that the arresting officers on the team, under your supervision, may have used questionable methods during the apprehension, which resulted in significant injuries to the individual. He asks for you to comment on the potential violation, and you inform him that you will look into the matter and get back with him later. Later that evening, you call a meeting of your team and advise the members of the allegations made. It is then brought to your attention that there was some force used in the apprehension that may have exceeded what was necessary. The next morning, you advise the chief of the inquiry by the media, and you tell him that based on your preliminary inquiry, there may be some validity to what the reporter told you. He reminds you of what he expects out of your team: results, not excuses. Ethics and Police Administration Respond to the given scenario in 500-600 words addressing the following 8 questions Due March 5th Primary Task Response: Write 500–600 words that respond to the following questions with your thoughts, ideas, and comments. Be substantive and clear, and use examples to reinforce your ideas: 1. What do you think are the legal issues involved in the scenario? Explain. 2. What do you think are the ethical issues involved in the scenario? Explain. 3. What are the possible consequences of not addressing these ethical issues? Explain. 4. Considering the directive given to you by your chief that he wants results and not excuses, what are some of the factors that you should take into consideration? 5. How would you respond to the follow-up questions from the reporter? Why? 6. What will most likely result from your responses, and how will you protect yourself and your career? Explain. 7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain. 8. How did this affect what you would say to the reporter? Explain. *Must have a minimum of 2 reliable references with websit.
Scenario You are a lieutenant in charge of an undercove.docx
Scenario You are a lieutenant in charge of an undercove.docx
kenjordan97598
Travel Essay - 4+ Examples, Format, Pdf | Examples. Breathtaking Essay On Travelling ~ Thatsnotus. Travelling Essay 100 Words - Grateful Study. ⚡ Traveling essay sample. Travelling Essay for Students and Children .... Importance Of Travelling Paragraph Writing |Essay On importance of .... Travelling as a hobby essay in english:describe travelling as a hobby ....
Traveling Essay. Travelling Experience Essay Example 300 Words - PHDessay.com
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Graphic Representation Grading Guide COM/TM541 Version 2 2 Individual Assignment: Graphic Representation Purpose of Assignment The purpose of this assignment reinforces the concept of how two different or opposing conclusions can be drawn from similar sets of data. Since data is often the basis for business decision making, you must think critically about how charts are created and where information presented originates. By telling two different stories in the creation of two charts or graphs, you will be more capable of analyzing and questioning graphics that you may encounter in your career. Another goal of the assignment is to have you locate raw data and convert it into a professional chart or graph using best design practices by paying attention to the use of colors, labels, sizing, and fonts. Resources Required · Misleading Statistics · Census data · Internet usage data · U. S. Energy stats Grading Guide Content Met Partially Met Not Met Comments: The student chooses a data source. The student creates a graph or table that illustrates the story behind the data. The student creates another graph or table of a contradicting story using the same data set. The student compares the results of the graphs and includes the source of the data. The student compares the results of the graphs and includes the story the data is telling the reader. The student compares the results of the graphs and includes the reasons behind the graphing techniques used. The student compares the results of the graphs and includes the contradicting story that was being told. The student compares the results of the graphs and includes ethical implications of altering the aesthetics of the graphic. The student compares the results of the graphs and includes how technology is used to create graphs and clarify business communication. The summary is no more than 525 words in length. Total Available Total Earned 8.5 #/8.5 Writing Guidelines Met Partially Met Not Met Comments: The paper—including tables and graphs, headings, title page, and reference page—is consistent with APA formatting guidelines and meets course-level requirements. Intellectual property is recognized with in-text citations and a reference page. Paragraph and sentence transitions are present, logical, and maintain the flow throughout the paper. Sentences are complete, clear, and concise. Rules of grammar and usage are followed including spelling and punctuation. Total Available Total Earned 3.5 #/3.5 Assignment Total # 12 #/12 Additional comments: Copyright © 2016 by University of Phoenix. All rights reserved. Misleading Statistics COM/TM541 Version 2 3Misleading Statistics When talking about misleading statistics, two references are most often cited: 1) The quote, “There are three kinds of lies: lies, damned lies, and statistics” (attributed to Mark Twain, Benjamin Disraeli, or someone else) 2) The book, How t ...
Graphic Representation Grading GuideCOMTM541 Version 22.docx
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whittemorelucilla
Professional Writing Service http://HelpWriting.net/How-To-Write-A-Descriptive-Essay---The- 👈
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How to Write My Personality Essay: Example Included!. My Personality Essay — What is a describe your personality essay? The .... Myers Briggs Personality Type Research Paper. Personality Type Essay - bbbbbLevi 1/17/19 ELS Essay Personality Essay .... 003 Personality Essay Help Essays Psychology Writing Service Uk .... Essay personality - report574.web.fc2.com. Personality Essay. My Personality Essay | Sample Essay on My Personality for Students and .... 013 Our2bnational2bpoet2b252822529 Personality Essay ~ Thatsnotus. 2 Personal Essay Examples with Awesome Personality. My favourite personality essay creativesavantz.com.
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Presentation on the uses & misues of data, embracing illustrations & examples, as presented to the Numis Securities Media Conference in London April 2011
Facts, Figures & Fictions
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Johnbillett.com
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 ...
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
Homework #1SOCY 3115Spring 20Read the Syllabus and FAQ on ho.docx
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Gramener's Director - Client success, Shravan Kumar A, delivered an online session to the students of Praxis Business School. In his session he talked about how converting data into stories can benefit businesses and enable quick decision making. Furthermore, he shared approaches to create data stories along with some use cases and case studies we solved at Gramener to benefit our clients. Check out our initiative to teach data storytelling to data scientists and analysts so that they can think out of the box and create wonderful data stories for their stakeholders: https://gramener.com/data-storytelling-workshop
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Gramener's Shravan Kumar took a virtual session with the students of IIM Shillong. It was a fantastic interaction and the participants were keen to understand how data and storytelling are making a difference to new-age businesses. Check out the slides from the session
The value of storytelling through data
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This presentation was shared by Gramener's Kanishk Kumar Abhishek during his guest lecture session at School of Business Management, NMIMS Mumbai. Check out Gramener's data storytelling workshop for analysts and data scientists at https://gramener.com/data-storytelling-workshop
Humanizing Data Storytelling for Greater Business Impact
Humanizing Data Storytelling for Greater Business Impact
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Chief data and analytics officers (CDAO) Apex Winter 2020 conference in Orlando was an intimate gathering of analytics leaders from across the world. Gramener's Co-founder and COO, Naveen Gattu, spoke about the importance of storytelling on easy data consumption and impact on data-driven decision making for business users.
Storytelling for analytics | Naveen Gattu | CDAO Apex 2020
Storytelling for analytics | Naveen Gattu | CDAO Apex 2020
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Outline: - Fact checking – what is it and why do we need it? - False information online - Content-based automatic fact checking - Explainability – what is it and why do we need it? - Making the right predictions for the right reasons - Model training pipeline - Explainable fact checking – some first solutions - Rationale selection - Generating free-text explanations - Wrap-up
Towards Explainable Fact Checking (DIKU Business Club presentation)
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Isabelle Augenstein
50 Percent of Data Science Projects Fail at Consumption: Can Storytelling Be Your Game Changer Growth of Self Service BI has generated a lot of dashboards, but “lots” does not always mean “good” or “useful”. • While advances in AI/ML lead to deeper insights, business teams struggle with the adoption of algorithms and consumption • How can data officers and analytics leaders get better business ROI from their data science investments? • This session will show you how to unleash the power of data storytelling for business decision- making, using industry examples
Data Storytelling - Game changer for Analytics
Data Storytelling - Game changer for Analytics
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Plenary Talk by Danny Dorling held at the Royal Statistical Society Annual Conference, Brighton 17th September 2010
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Statistical Shenanigans: From Sweet Peas to Nobel Prizes, how much that we ta...
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Scenario: You are a lieutenant in charge of an undercover strike force team, charged with the responsibility of apprehending fugitives from justice. Your team has been criticized by the local media for some of its members' actions in carrying out their responsibilities, such as using questionable methods that could be seen as potential violation of some individual civil rights. Your team has been very effective in carrying out its assigned duties, resulting in an 80% apprehension rate. You have been advised by the chief that all he wants is results, not excuses. He wants you to use whatever means are necessary to apprehend fugitives because anything less would reflect badly on the department and his leadership. He reminds you that he has the firm backing of the mayor and city commission in how he runs the department. The next day, a news reporter informs you that he is working on a story regarding the apprehension of child rapist. Information he has gathered indicates that the arresting officers on the team, under your supervision, may have used questionable methods during the apprehension, which resulted in significant injuries to the individual. He asks for you to comment on the potential violation, and you inform him that you will look into the matter and get back with him later. Later that evening, you call a meeting of your team and advise the members of the allegations made. It is then brought to your attention that there was some force used in the apprehension that may have exceeded what was necessary. The next morning, you advise the chief of the inquiry by the media, and you tell him that based on your preliminary inquiry, there may be some validity to what the reporter told you. He reminds you of what he expects out of your team: results, not excuses. Ethics and Police Administration Respond to the given scenario in 500-600 words addressing the following 8 questions Due March 5th Primary Task Response: Write 500–600 words that respond to the following questions with your thoughts, ideas, and comments. Be substantive and clear, and use examples to reinforce your ideas: 1. What do you think are the legal issues involved in the scenario? Explain. 2. What do you think are the ethical issues involved in the scenario? Explain. 3. What are the possible consequences of not addressing these ethical issues? Explain. 4. Considering the directive given to you by your chief that he wants results and not excuses, what are some of the factors that you should take into consideration? 5. How would you respond to the follow-up questions from the reporter? Why? 6. What will most likely result from your responses, and how will you protect yourself and your career? Explain. 7. How significant is it to you that a superior officer is implying that you should make an unethical decision? Explain. 8. How did this affect what you would say to the reporter? Explain. *Must have a minimum of 2 reliable references with websit.
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Graphic Representation Grading Guide COM/TM541 Version 2 2 Individual Assignment: Graphic Representation Purpose of Assignment The purpose of this assignment reinforces the concept of how two different or opposing conclusions can be drawn from similar sets of data. Since data is often the basis for business decision making, you must think critically about how charts are created and where information presented originates. By telling two different stories in the creation of two charts or graphs, you will be more capable of analyzing and questioning graphics that you may encounter in your career. Another goal of the assignment is to have you locate raw data and convert it into a professional chart or graph using best design practices by paying attention to the use of colors, labels, sizing, and fonts. Resources Required · Misleading Statistics · Census data · Internet usage data · U. S. Energy stats Grading Guide Content Met Partially Met Not Met Comments: The student chooses a data source. The student creates a graph or table that illustrates the story behind the data. The student creates another graph or table of a contradicting story using the same data set. The student compares the results of the graphs and includes the source of the data. The student compares the results of the graphs and includes the story the data is telling the reader. The student compares the results of the graphs and includes the reasons behind the graphing techniques used. The student compares the results of the graphs and includes the contradicting story that was being told. The student compares the results of the graphs and includes ethical implications of altering the aesthetics of the graphic. The student compares the results of the graphs and includes how technology is used to create graphs and clarify business communication. The summary is no more than 525 words in length. Total Available Total Earned 8.5 #/8.5 Writing Guidelines Met Partially Met Not Met Comments: The paper—including tables and graphs, headings, title page, and reference page—is consistent with APA formatting guidelines and meets course-level requirements. Intellectual property is recognized with in-text citations and a reference page. Paragraph and sentence transitions are present, logical, and maintain the flow throughout the paper. Sentences are complete, clear, and concise. Rules of grammar and usage are followed including spelling and punctuation. Total Available Total Earned 3.5 #/3.5 Assignment Total # 12 #/12 Additional comments: Copyright © 2016 by University of Phoenix. All rights reserved. Misleading Statistics COM/TM541 Version 2 3Misleading Statistics When talking about misleading statistics, two references are most often cited: 1) The quote, “There are three kinds of lies: lies, damned lies, and statistics” (attributed to Mark Twain, Benjamin Disraeli, or someone else) 2) The book, How t ...
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Graphic Representation Grading GuideCOMTM541 Version 22.docx
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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 ...
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Статистика для журналистов
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Being Smart with
Statistics Tom Coupé KSE and KEI
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EBA Investment Attractiveness
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Causality vs Correlation
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Possible Explanations for
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