Top 5 tips on how to learn statistics more effectivelyStat Analytica
In this infographic, you will go through how to learn statistics more effectively than ever before. Have a look at this infographic to start learning statistics
7 excellent reasons why statistics are important statsworkStats Statswork
Statistics are used to analyze what's happening within the world around us. In this data-driven world, all activities of ours are monitored by someone else every time. Statistics help us to convert whatever occurs in the past can be used in predicting the future. Statswork Is A Premier Statistics Consulting Company That Spearheaded Online Statistics Consultancy Service With Clientele Ranging From Educational Institutions, Academics, Corporations And Ngos. We Provide End-To-End Service And Assistance For Your Statistical Research And Analytical Needs From Data Collection, Data Mining, Data Analysis To Research Framework And Research Methodology.
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Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
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UnitedKingdom: +44-1143520021
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Top 5 tips on how to learn statistics more effectivelyStat Analytica
In this infographic, you will go through how to learn statistics more effectively than ever before. Have a look at this infographic to start learning statistics
7 excellent reasons why statistics are important statsworkStats Statswork
Statistics are used to analyze what's happening within the world around us. In this data-driven world, all activities of ours are monitored by someone else every time. Statistics help us to convert whatever occurs in the past can be used in predicting the future. Statswork Is A Premier Statistics Consulting Company That Spearheaded Online Statistics Consultancy Service With Clientele Ranging From Educational Institutions, Academics, Corporations And Ngos. We Provide End-To-End Service And Assistance For Your Statistical Research And Analytical Needs From Data Collection, Data Mining, Data Analysis To Research Framework And Research Methodology.
Why Statswork?
Plagiarism Free | Unlimited Support | Prompt Turnaround Times | Subject Matter Expertise | Experienced Bio-statisticians & Statisticians | Statistics Across Methodologies | Wide Range Of Tools & Technologies Supports | Tutoring Services | 24/7 Email Support | Recommended by Universities
Contact Us:
Website: www.statswork.com/
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Analysis of the article "A Predictive Analytics Primer" by Thomas H. DavenportVaibhav Srivastav
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Delivered by Peter York, Founder and CEO of Algorhythm, at the 2016 Annual Community Meeting & Nonprofit Expo.
Attend our next event:
http://www.unitedwaynca.org/events/members
Basic statistical & pharmaceutical statistical applicationsYogitaKolekar1
This is knowledge sharing PPT specially designed for Non-statisticians to understand basic fundamentals regarding statistics & related to pharmaceutical statistics.
How statistics involve in daily life as well as pharmaceutical industry etc., not limited.
#WhatisMeanByStatistics? #WhyStatistics? #HowStatisticsEssentialtoEverydayLife? #StatisticalApplicationsinDailyLife #Toothpaste
#IndependentDependentVariables #Tea #TypesofData #ClassificationofDiscreteVariableContinuousVariables #TypesofDataMeasurementScale
#StatisticalMethodsforAnalyzingData #ConceptofPopulationSampleandPointEstimate
#DescriptiveStatistics #InferentialStatistics
#MeasuresofCentralTendency #MeasuresofDispersion #RealLifeApplications #DataPresentation #PictorialView
#PharmaceuticalStatistics #ResearchDevelopment #Statistician
Artificial Intelligence and Machine Learning for businessSteven Finlay
Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences.
This presentation, based on the #1 Amazon bestselling book, cuts through the technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people.
The focus is very much on practical application, and how to work with technical specialists (data scientists) to maximise the benefits of these technologies.
Analysis of the article "A Predictive Analytics Primer" by Thomas H. DavenportVaibhav Srivastav
This presentation gives analysis of the article "A Predictive Analytics Primer" by Thomas H. Davenport
Slide 1: A Predictive Analytics Primer by Thomas H. Davenport
Slide 2: Thomas H. Davenport
Slide 3: Powers of Predictive analytics
Slide 4: Predictive analytics refers to predicting future from the data of the past.
Slide 5: The quantitative analysis isn’t magic—but it is normally done with a lot of past data, a little statistical wizardry, and some important assumptions.
Slide 6: The Data: Lack of good data is the most common barrier to organizations seeking to employ predictive analytics.
Slide 7: The Statistics: Regression analysis in its various forms is the primary tool that organizations use for predictive analytics.
Slide 8: An analyst hypothesizes that a set of independent variables (say, gender, income, visits to a website) are statistically correlated with the purchase of a product for a sample of customers. The analyst performs a regression analysis to see just how correlated each variable is; this usually requires some iteration to find the right combination of variables and the best model.
Slide 9: The Assumptions: That brings us to the other key factor in any predictive model—the assumptions that underlie it. Every model has them, and it’s important to know what they are and monitor whether they are still true. The big assumption in predictive analytics is that the future will continue to be like the past.
Slide 10: What can make assumptions invalid?
Slide 11: The most common reason is time. If your model was created several years ago, it may no longer accurately predict current behavior. The greater the elapsed time, the more likely customer behavior has changed.
Slide 12: Another reason a predictive model’s assumptions may no longer be valid is if the analyst didn’t include a key variable in the model, and that variable has changed substantially over time.
Slide 13: Managers should always ask analysts what the key assumptions are, and what would have to happen for them to no longer be valid. And both managers and analysts should continually monitor the world to see if key factors involved in assumptions might have changed over time.
Slide 14: With these fundamentals in mind, here are a few good questions to ask your analysts:
Can you tell me something about the source of data you used in your analysis?
Are you sure the sample data are representative of the population?
Are there any outliers in your data distribution? How did they affect the results?
What assumptions are behind your analysis?
Are there any conditions that would make your assumptions invalid?
Slide 15: Thank You!
Delivered by Peter York, Founder and CEO of Algorhythm, at the 2016 Annual Community Meeting & Nonprofit Expo.
Attend our next event:
http://www.unitedwaynca.org/events/members
Basic statistical & pharmaceutical statistical applicationsYogitaKolekar1
This is knowledge sharing PPT specially designed for Non-statisticians to understand basic fundamentals regarding statistics & related to pharmaceutical statistics.
How statistics involve in daily life as well as pharmaceutical industry etc., not limited.
#WhatisMeanByStatistics? #WhyStatistics? #HowStatisticsEssentialtoEverydayLife? #StatisticalApplicationsinDailyLife #Toothpaste
#IndependentDependentVariables #Tea #TypesofData #ClassificationofDiscreteVariableContinuousVariables #TypesofDataMeasurementScale
#StatisticalMethodsforAnalyzingData #ConceptofPopulationSampleandPointEstimate
#DescriptiveStatistics #InferentialStatistics
#MeasuresofCentralTendency #MeasuresofDispersion #RealLifeApplications #DataPresentation #PictorialView
#PharmaceuticalStatistics #ResearchDevelopment #Statistician
Artificial Intelligence and Machine Learning for businessSteven Finlay
Artificial Intelligence (AI) and Machine Learning are now mainstream business tools. They are being applied across many industries to increase profits, reduce costs, save lives and improve customer experiences.
This presentation, based on the #1 Amazon bestselling book, cuts through the technical jargon that is often associated with these subjects. It delivers a simple and concise introduction for managers and business people.
The focus is very much on practical application, and how to work with technical specialists (data scientists) to maximise the benefits of these technologies.
Scenario You are a lieutenant in charge of an undercove.docxkenjordan97598
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.
Top 10 Uses Of Statistics In Our Day to Day Life Stat Analytica
Don't you know the uses of statistics is our daily life? If yes then check out this presentation you will learn a lot more about the use of statistics in our daily life.
There Is A 90% Probability That Your Son Is Pregnant: Predicting the Future ...Health Catalyst
In this webinar, which is geared for managers and executives, Dale Sanders provides a new version of a very popular lecture he presented at this year’s Health Analytics Summit in Salt Lake City. Attendees will gain an understanding of:
What to expect from predictive analytics as it relates to human behavior
A general overview of predictive analytics models, and the contexts in which those various models should and should not be used
The scenarios in which predictive models in healthcare are effective and when they are not, given that 80% of population health outcomes are determined by socio-econonic factors, not healthcare delivery
The relationship between predictive analytic accuracy and topics of data management such as data quality, data volume and patient outcomes data
The use of predictive analytics to identify patients who are on a trajectory for poor, as well as good, outcomes
How current predictive analytics strategies are overlookng the cost of intervention and “Return on Engagement”, ROE— the cost per unit of healthcare improvement for patient populations
The cultural, philosophical, and legal conundrums that predictive analytics will create for healthcare, notably healthcare rationing
The success of predictive analytics will not be defined by the simple risk stratification of patient populations for care management teams. Success will depend on the costs of intervention to reduce the risks that are identified by predictive analytics, which boils down to this two-part question: Now that we can predict a patient’s risk for a bad healthcare outcome, “What’s the probability of influencing this patient’s behavior towards a better outcome?” And, “How much effort and cost will be required for that influence?”
8 Management tools that improve Patient safetyImperago Ltd
In a post-Francis world, everybody is searching for the silver bullet to improve quality within the NHS.
The 1,782 page report by Robert Francis QC doesn't provide one bullet, but 290 recommendations.
But are we in danger of not seeing the wood from the trees?
There are some very basic - yet key - principles that still seem illusive for many trusts
Technology in healthcare delivery- CHEWS (Advantage Health Africa)Oluwadamilare Oyebode
A presentation of Technology Trends in Healthcare that Community Healthcare Workers (CHEWS) in Nigeria need to Take note of and the significance of these trends in their work.
Facilitated by Advantage Health Africa.
Different hats, same needs: Marketing compliance from doctor’s office to boar...Prolifiq Software
At the Marcus Evans 2011 Pharma Marketing Summit on May 4th, GPP blogger Jonathan Sackier presented a physician’s take on Good Promotional Practices for pharmaceutical and medical device sales, including four actionable strategies for marketing compliance.
3 Frequent Mistakes in Healthcare Data AnalyticsHealth Catalyst
Healthcare organizations are recognizing the value of healthcare analytics, especially in their Big Data, population health management, or accountable care initiatives. This is good because without analytics it is difficult to impossible to run these programs successfully. That said, analytics are not the magic bullet and proper process must be in place. The three most common mistakes health systems makes with their healthcare analytics are: 1. Analytics Whiplash- when the analytics goes from one project to another without being able to fully understand the data and what it’s saying. 2. Coloring the Truth- When analysts don’t feel like they can be completely forthcoming with information and only give leadership the news they want to hear. 3. Deceitful Visualizations- Manipulating charts, graphs, and the like to reflect what the analyst or leadership wants the data to say, rather than what it actually says.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
Biological screening of herbal drugs: Introduction and Need for
Phyto-Pharmacological Screening, New Strategies for evaluating
Natural Products, In vitro evaluation techniques for Antioxidants, Antimicrobial and Anticancer drugs. In vivo evaluation techniques
for Anti-inflammatory, Antiulcer, Anticancer, Wound healing, Antidiabetic, Hepatoprotective, Cardio protective, Diuretics and
Antifertility, Toxicity studies as per OECD guidelines
Delivering Micro-Credentials in Technical and Vocational Education and TrainingAG2 Design
Explore how micro-credentials are transforming Technical and Vocational Education and Training (TVET) with this comprehensive slide deck. Discover what micro-credentials are, their importance in TVET, the advantages they offer, and the insights from industry experts. Additionally, learn about the top software applications available for creating and managing micro-credentials. This presentation also includes valuable resources and a discussion on the future of these specialised certifications.
For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
MATATAG CURRICULUM: ASSESSING THE READINESS OF ELEM. PUBLIC SCHOOL TEACHERS I...NelTorrente
In this research, it concludes that while the readiness of teachers in Caloocan City to implement the MATATAG Curriculum is generally positive, targeted efforts in professional development, resource distribution, support networks, and comprehensive preparation can address the existing gaps and ensure successful curriculum implementation.
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
Let’s explore the intersection of technology and equity in the final session of our DEI series. Discover how AI tools, like ChatGPT, can be used to support and enhance your nonprofit's DEI initiatives. Participants will gain insights into practical AI applications and get tips for leveraging technology to advance their DEI goals.
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
2. What is Statistics?
•Statistics is the mathematics of collecting and
analyzing data to draw conclusions and make
predictions.
•In essence, it can help us separate the ‘signal’
from the ‘noise’.
3. Where do we use Statistics?
• Statistics is used everywhere, in just about any field
you can think of.
• On the following slides, I will give some examples, but
this list is in no way complete or meant to be a
representative sample…
• …just some places where Statistics is used to get you
thinking.
4. Stock Market and
the Economy
Stock Analysts use
statistical computer
models to forecast what is
happening in the
economy.
5. Quality Assurance
• Companies make thousands of products every day and each company
must make sure that a good quality item is sold.
• However, a company cannot test each and every item that they ship.
• Thus, a company uses statistics to test just a few, called a sample, of
what they make.
• If the sample passes quality tests, then the company assumes that all items made in
the group, called a batch, are good.
6. Consumer Goods
Retailers keep track of
everything they sell and
use statistics to calculate
what to ship to each store,
or warehouse, and when.
From analyzing their vast
store of information, Wal-
Mart decided that people
buy strawberry Pop Tarts
when a hurricane is
predicted in Florida…
…so, they increase their
shipment of this product to
Florida stores based upon
the weather forecast.
7. Insurance
You know that in order to
drive your care you are
required by law to have
car insurance;
The rate that an insurance
company charges you is
based upon statistics from
all drivers where you live.
9. Genetic
Engineering
Many people are afflicted
with diseases that come
from their genetic make-
up and these diseases can
be passed on to their
children.
Genetic Engineering offers
the possibility of cures for
disease and countless
material improvements to
daily life.
10. Medical Studies
• Scientists must show a statistically valid rate of effectiveness before any
drug can be prescribed.
• Statistics are behind every medical study you hear about.
11. Predicting Disease
• Often, statistics about disease are reported via various media.
• If the report simply shows the number of people who have the disease or
who have died from it, it’s an interesting fact but it might not mean much
to your life.
• When statistics become involved, you have a better idea of how that
disease may affect you.
• For example, studies have shown that 85 to 95 percent of lung cancers
are smoking related.
• The statistic should tell you that almost all lung cancers are related to
smoking and that if you want to have a good chance of avoiding lung
cancer, you shouldn’t smoke.
12. Weather
Forecasts
Do you watch the weather forecast sometime during
the day (or look it up on an app/website?)
Forecasters use statistics to create computer models
to compare prior weather conditions with current
weather to predict future weather.
13. Emergency
Preparedness
What happens if the
forecast indicates that a
hurricane is imminent or
that tornadoes are likely
to occur?
Emergency management
agencies move into high
gear to be ready to rescue
people.
Emergency teams rely on
statistics to tell them
when danger may occur.
20. Or, to simply be better consumers of information…
So that you can have a better
understanding of data that is
being presented to you…
allowing you to interpret and use
the data…
and allowing you to know when
you are being manipulated…
or misdirected.