This document discusses different probability distributions including binomial, Poisson, geometric, and multinomial distributions. It provides examples of when each distribution would be used and how to calculate probabilities and expected values for experiments that follow each distribution. It also discusses how to identify which distribution applies to a given problem and how to solve for relevant values.
This slide presentation is a non-technical introduction to the concept of probability. The level of the presentation would be most suitable for college students majoring in business or a related field, but it could also be used in high school classes.
MATH 221 Final Exam/MATH221
Click Link Below To Buy:
https://hwaid.com/shop/math-221-final-exam-1-explain-the-difference-between-a-population-and-a-sample-in-which-of-these-is-it-important-to-distinguish-between-the-two-in-order-to-use-the-correct-formula-mean-median-mo/
Contact Us:
hwaidservices@gmail.com
This slide presentation is a non-technical introduction to the concept of probability. The level of the presentation would be most suitable for college students majoring in business or a related field, but it could also be used in high school classes.
MATH 221 Final Exam/MATH221
Click Link Below To Buy:
https://hwaid.com/shop/math-221-final-exam-1-explain-the-difference-between-a-population-and-a-sample-in-which-of-these-is-it-important-to-distinguish-between-the-two-in-order-to-use-the-correct-formula-mean-median-mo/
Contact Us:
hwaidservices@gmail.com
Chapter 5 part2- Sampling Distributions for Counts and Proportions (Binomial ...nszakir
Mathematics, Statistics, Sampling Distributions for Counts and Proportions, Binomial Distributions for Sample Counts,
Binomial Distributions in Statistical Sampling, Binomial Mean and Standard Deviation, Sample Proportions, Normal Approximation for Counts and Proportions, Binomial Formula
Why You Should Replace Windows 11 with Nitrux Linux 3.5.0 for enhanced perfor...SOFTTECHHUB
The choice of an operating system plays a pivotal role in shaping our computing experience. For decades, Microsoft's Windows has dominated the market, offering a familiar and widely adopted platform for personal and professional use. However, as technological advancements continue to push the boundaries of innovation, alternative operating systems have emerged, challenging the status quo and offering users a fresh perspective on computing.
One such alternative that has garnered significant attention and acclaim is Nitrux Linux 3.5.0, a sleek, powerful, and user-friendly Linux distribution that promises to redefine the way we interact with our devices. With its focus on performance, security, and customization, Nitrux Linux presents a compelling case for those seeking to break free from the constraints of proprietary software and embrace the freedom and flexibility of open-source computing.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
Generative AI Deep Dive: Advancing from Proof of Concept to ProductionAggregage
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
2. 22
Probability DistributionsProbability Distributions
A probability distribution is a statement ofA probability distribution is a statement of
a probability function that assigns all thea probability function that assigns all the
probabilities associated with a randomprobabilities associated with a random
variable.variable.
– A discrete probability distribution is aA discrete probability distribution is a
distribution of discrete random variables (thatdistribution of discrete random variables (that
is, random variables with a limited set ofis, random variables with a limited set of
values).values).
– A continuous probability distribution isA continuous probability distribution is
concerned with a random variable having anconcerned with a random variable having an
infinite set of values.infinite set of values.
3. 33
BinomialBinomial
The experiment must have only twoThe experiment must have only two
possible outcomes (success & failure)possible outcomes (success & failure)
The probability of success must beThe probability of success must be
constant from trial to trial (each memberconstant from trial to trial (each member
of the sample have sameof the sample have same pp))
Independence must be maintained (noIndependence must be maintained (no
trial’s outcome influences another)trial’s outcome influences another)
WeWe countcount the number ofthe number of successessuccesses inin nn
trialstrials
4. 44
Explain why the following areExplain why the following are
not Binomial experimentsnot Binomial experiments
I draw 3 cards from an ordinary deck and countI draw 3 cards from an ordinary deck and count
X, the number of aces. Drawing is done withoutX, the number of aces. Drawing is done without
replacement.replacement.
A couple decides to have children until a girl isA couple decides to have children until a girl is
born. Let X denote the number of children theborn. Let X denote the number of children the
couple will have.couple will have.
In a sample of 5000 individuals, I record theIn a sample of 5000 individuals, I record the
age of each person, denoted as X .age of each person, denoted as X .
A chemist repeats a solubility test ten times onA chemist repeats a solubility test ten times on
the same substance. Each test is conducted atthe same substance. Each test is conducted at
a temperature 10 degrees higher than thea temperature 10 degrees higher than the
previous test. Let X denote the number of timesprevious test. Let X denote the number of times
the substance dissolves completely.the substance dissolves completely.
5. 55
BinomialBinomial
In a small clinical trial with 20 patients, letIn a small clinical trial with 20 patients, let
X denote the number of patients thatX denote the number of patients that
respond to a new skin rash treatment. Therespond to a new skin rash treatment. The
physicians assume independence amongphysicians assume independence among
the patients. Here, X ~ bin (n = 20; p),the patients. Here, X ~ bin (n = 20; p),
where p denotes the probability ofwhere p denotes the probability of
response to the treatment. In a statisticsresponse to the treatment. In a statistics
problem, p might be an unknownproblem, p might be an unknown
parameter that we might want toparameter that we might want to
estimate. For this problem, we'll assumeestimate. For this problem, we'll assume
that p = 0.7. We want to compute (a). P(Xthat p = 0.7. We want to compute (a). P(X
= 15), (b) P(X≥ 15), and (c) P(X < 10).= 15), (b) P(X≥ 15), and (c) P(X < 10).
[0.1789; 0.416; 0.017][0.1789; 0.416; 0.017]
6. 66
PoissonPoisson
If BinomialIf Binomial pp is small andis small and nn is large,is large,
Poisson can be used as an approximationPoisson can be used as an approximation
((pp ≤ 0.05 and≤ 0.05 and nn ≥ 20;≥ 20; μμ ==nn**pp))
OtherwiseOtherwise, by itself, the Poisson, by itself, the Poisson countscounts
the number ofthe number of occurrencesoccurrences in an intervalin an interval
of time or space or volume. [Only mean isof time or space or volume. [Only mean is
given]. Examplegiven]. Example
– Number of accidents in a dayNumber of accidents in a day
– No. of tears (defects) in a sq metre of clothNo. of tears (defects) in a sq metre of cloth
– Number of customers arriving at a serviceNumber of customers arriving at a service
centre in a certain periodcentre in a certain period
7. 77
PoissonPoisson
It is useful for describingIt is useful for describing
– radioactive decay (number of particles emittedradioactive decay (number of particles emitted
in a fixed period of time);in a fixed period of time);
– the number of vacancies in the Supreme Courtthe number of vacancies in the Supreme Court
each year;each year;
– the numbers of dye molecules taken up bythe numbers of dye molecules taken up by
small particles;small particles;
– the sizes of colloidal particles;the sizes of colloidal particles;
– the number of accidents per unit timethe number of accidents per unit time
– the number of customers arriving at a facilitythe number of customers arriving at a facility
– The number of earthquakes in a certain areaThe number of earthquakes in a certain area
per yearper year
8. 88
PoissonPoisson
Phone calls arrive at a switchboard
according to a Poisson process, at a
rate of = 3 per minute.
– Find the probability that 8 or fewer calls
come in during a 5-minute span.
– What is the average number of calls in a
5-minute span?
– [0.037; 15]
9. 99
GeometricGeometric
Under the same conditions of theUnder the same conditions of the
Binomial, the Geometric counts theBinomial, the Geometric counts the
number of failuresnumber of failures beforebefore (until) the(until) the firstfirst
successsuccess – hence there is no sample size.– hence there is no sample size.
– Probability you take the course 3 times beforeProbability you take the course 3 times before
you pass (x = 3)you pass (x = 3)
– Probability the police will stop 10 cars beforeProbability the police will stop 10 cars before
they find the suspect (x = 10)they find the suspect (x = 10)
– Probability I screen 5 applicants before I findProbability I screen 5 applicants before I find
the first qualified (x = 5)the first qualified (x = 5)
10. 1010
MultinomialMultinomial
Similar to the Binomial except that:Similar to the Binomial except that:
The experiment will have more thanThe experiment will have more than
two possible outcomes (Xtwo possible outcomes (X11, X, X22, …, X, …, Xnn))
The probability of each outcome willThe probability of each outcome will
be given (pbe given (p11, p, p22, …, p, …, pnn).).
The sample will cover all theThe sample will cover all the
outcomesoutcomes
11. 1111
Identify the DistributionIdentify the Distribution
In the following examples:In the following examples:
– Identify the distributionIdentify the distribution
– Find the probabilityFind the probability
– What are the expected values?What are the expected values?
12. 1212
Identify the DistributionIdentify the Distribution
Fidelity sells a small SUV called theFidelity sells a small SUV called the
Nissan X-Trail. They believe thatNissan X-Trail. They believe that
they have 20% of the small SUVthey have 20% of the small SUV
market. Assume it is true. What ismarket. Assume it is true. What is
the probability that in a randomthe probability that in a random
sample of 15 small SUV owners, 5sample of 15 small SUV owners, 5
are X-Trails?are X-Trails?
How many do you expect to find?How many do you expect to find?
13. 1313
Identify the DistributionIdentify the Distribution
Fidelity sells a small SUV called theFidelity sells a small SUV called the
Nissan X-Trail. They believe thatNissan X-Trail. They believe that
they have 20% of the small SUVthey have 20% of the small SUV
market. Assume it is true. What ismarket. Assume it is true. What is
the probability that they would havethe probability that they would have
to interview 6 small SUV owners,to interview 6 small SUV owners,
(randomly selected) before they find(randomly selected) before they find
the first X-Trail owner?the first X-Trail owner?
How many do you expect to find?How many do you expect to find?
14. 1414
Identify the DistributionIdentify the Distribution
Assume that the small SUV market isAssume that the small SUV market is
divided as shown in the table. What is thedivided as shown in the table. What is the
probability that in a random sample of 40probability that in a random sample of 40
small SUV’s at the toll booth, 8 weresmall SUV’s at the toll booth, 8 were
Nissan; 10 were Honda; 9 were Toyota; 7Nissan; 10 were Honda; 9 were Toyota; 7
were Suzuki and 6 were other?were Suzuki and 6 were other?
Brand
Nissan
X-Trail
Honda
CRV
Toyota
Rav4
Suzuki
Vitara Other
% 0.12 0.27 0.22 0.25 0.14
15. 1515
Identify the DistributionIdentify the Distribution
Insurance companies keep track ofInsurance companies keep track of
accidents as part of their riskaccidents as part of their risk
management. Suppose that ladymanagement. Suppose that lady
drivers have a 2 percent chance ofdrivers have a 2 percent chance of
committing an accident in the year.committing an accident in the year.
A random sample of 1,000 ladiesA random sample of 1,000 ladies
was examined – what is thewas examined – what is the
probability that 10 of these ladiesprobability that 10 of these ladies
committed an accident?committed an accident?
16. 1616
Identify the DistributionIdentify the Distribution
A tailor was contracted to make suitsA tailor was contracted to make suits
for a wedding party. He discoveredfor a wedding party. He discovered
that the material chosen had athat the material chosen had a
reputationreputation of having 3 defects perof having 3 defects per
square metre.square metre.
– What is the probability that in a squareWhat is the probability that in a square
metre examined, 4 defects were seen?metre examined, 4 defects were seen?
– What is the probability that inWhat is the probability that in 1010 squaresquare
metres, 20 defects were found?metres, 20 defects were found?
17. 1717
Identify the DistributionIdentify the Distribution
A certain city has three television stations.A certain city has three television stations.
During prime time on Saturday nights,During prime time on Saturday nights,
Channel 12 has 50 percent of the viewingChannel 12 has 50 percent of the viewing
audience, Channel 10 has 30 percent ofaudience, Channel 10 has 30 percent of
the viewing audience, and Channel 3 hasthe viewing audience, and Channel 3 has
20 percent of the viewing audience. Find20 percent of the viewing audience. Find
the probability that among eight televisionthe probability that among eight television
views in that city, randomly chosen on aviews in that city, randomly chosen on a
Saturday night, five will be watchingSaturday night, five will be watching
Channel 12, two will be watching ChannelChannel 12, two will be watching Channel
10, and one will be watching Channel 310, and one will be watching Channel 3
18. 1818
Identify the DistributionIdentify the Distribution
My car has a dead battery and I needMy car has a dead battery and I need
some jumper cables. People stop to helpsome jumper cables. People stop to help
me but I have to refuse their help if theyme but I have to refuse their help if they
have no jumper cables. Suppose 10% ofhave no jumper cables. Suppose 10% of
the people driving on the road havethe people driving on the road have
jumper cables.jumper cables.
– What is the probability that the first personWhat is the probability that the first person
who can help me is the 8who can help me is the 8thth
person whoperson who
stopped?stopped?
– How many persons do you expect to stopHow many persons do you expect to stop
before I can find one who is able to help?before I can find one who is able to help?