This document defines key concepts related to random variables including:
- A random variable is a numerical measure of outcomes from a random phenomenon.
- Probability distributions describe the probabilities associated with random variables.
- Expected value refers to the mean or weighted average of a probability distribution.
- As the number of trials increases, the actual mean approaches the true mean due to the Law of Large Numbers.
- Binomial and geometric distributions model situations with success/failure outcomes and independence between trials.
Chapter 4 part3- Means and Variances of Random Variablesnszakir
Statistics, study of probability, The Mean of a Random Variable, The Variance of a Random Variable, Rules for Means and Variances, The Law of Large Numbers,
: Random Variable, Discrete Random variable, Continuous random variable, Probability Distribution of Discrete Random variable, Mathematical Expectations and variance of a discrete random variable.
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
Chapter 4 part3- Means and Variances of Random Variablesnszakir
Statistics, study of probability, The Mean of a Random Variable, The Variance of a Random Variable, Rules for Means and Variances, The Law of Large Numbers,
: Random Variable, Discrete Random variable, Continuous random variable, Probability Distribution of Discrete Random variable, Mathematical Expectations and variance of a discrete random variable.
Determining the Mean, Variance, and Standard Deviation of a Discrete Random Variable
Visit the website for more services: https://cristinamontenegro92.wixsite.com/onevs
Chapter 5 part1- The Sampling Distribution of a Sample Meannszakir
Mathematics, Statistics, Population Distribution vs. Sampling Distribution, The Mean and Standard Deviation of the Sample Mean, Sampling Distribution of a Sample Mean, Central Limit Theorem
Probability Distribution (Discrete Random Variable)Cess011697
Learning Competencies:
- to find the possible values of a random variable.
illustrates a probability distribution for a discrete random variable and its properties.
- to compute probabilities corresponding to a given random variable.
There are some exercises for you to answer.
Random Variable (Discrete and Continuous)Cess011697
Learning Competencies
- to recall statistical experiment and sample space
- to illustrate a random variable (discrete and continuous).
- to distinguish between a discrete and a continuous random variable.
Chapter 5 part1- The Sampling Distribution of a Sample Meannszakir
Mathematics, Statistics, Population Distribution vs. Sampling Distribution, The Mean and Standard Deviation of the Sample Mean, Sampling Distribution of a Sample Mean, Central Limit Theorem
Probability Distribution (Discrete Random Variable)Cess011697
Learning Competencies:
- to find the possible values of a random variable.
illustrates a probability distribution for a discrete random variable and its properties.
- to compute probabilities corresponding to a given random variable.
There are some exercises for you to answer.
Random Variable (Discrete and Continuous)Cess011697
Learning Competencies
- to recall statistical experiment and sample space
- to illustrate a random variable (discrete and continuous).
- to distinguish between a discrete and a continuous random variable.
kelan nyo isubmit yung assignment no. 7 and 8 nyo nasa slides yun ng stats. isubmit nyo sa akin sa lunes during electromagnetism kasi kukulangin yung class participation nyo sa stats.
The concept of limit formalizes the notion of closeness of the function values to a certain value "near" a certain point. Limits behave well with respect to arithmetic--usually. Division by zero is always a problem, and we can't make conclusions about nonexistent limits!
A presentation on the first cosmic explosions and how the Universe started to make heavy elements, by Monash University's Professor Alexander Heger from the Faculty of Science, School of Mathematical Science.
The PPT covered the distinguish between discrete and continuous distribution. Detailed explanation of the types of discrete distributions such as binomial distribution, Poisson distribution & Hyper-geometric distribution.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
• The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
• Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
• Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
• Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
Whether you're a developer, tester, or QA professional, this webinar will give you valuable insights into how AI is shaping the future of software delivery.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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/
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
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👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
State of ICS and IoT Cyber Threat Landscape Report 2024 preview
Random variables
1. Random Variables
VOCABULARY
RANDOM VARIABLE
PROBABILITY DISTRIBUTION
EXPECTED VALUE
LAW OF LARGE NUMBERS
BINOMIAL DISTRIBUTION
BINOMIAL RANDOM VARIABLE
BINOMIAL COEFFICIENT
GEOMETRIC RANDOM VARIABLE
GEOMETRIC DISTRIBUTION
SIMULATION
2. Key Points
A random variable is a numerical measure(face up number of a
die) of the outcomes of a random phenomenon(rolling a die)
If X is a random variable and a and b are fixed numbers, then
μₐ₊ᵦₓ= a+βµₓ and Ợ²ₐ₊ᵦₓ=b²Ợ²x
If X and Y are random variables, then μₓ₊ᵧ= μₓ + μᵧ
If X and Y are independent random variables, then Ợ² ₓ₊ᵧ=
Ợ²ₓ + Ợ²ᵧ and Ợ² ₓ₋ᵧ= Ợ²ₓ + Ợ²ᵧ
As the number of trials in a binomial distribution gets
larger, the binomial distribution gets closer to a normal
distribution
5. Random Variable
A ______ ______ is a numerical measure of the
outcomes of a random phenomenon
The driving force behind many decisions in
science, business, and every day life is the
question, “What are the chances?”
Picking a student at random is a random
phenomenon.
The students grades, height, etc are random
variables that describe properties of the student.
8. Random Variable
The random variables can be categorical as well( top album, movies
watched, favorite artists, etc)
9. Random Variable- Probability distribution
A _______ ________ is a listing or graphing of
the probabilities associated with a random variable
10. Random Variable- Probability(or population)
distribution
The probability distribution can be used to answer
questions about the variable x( which in this case is the
number of tails obtained when a fair coin is tossed three
times)
Example: What is probability that there is at least one tails
in three tosses of the coin? This question is written as
P(X≥1)
P(X≥1)= P(X=1) + P(X=2)+ P(X=3)= 1/8 +3/8+3/8= 7/8
11. Random variable- discrete and continuous
_______ random variables takes a countable
number of values(# of votes a certain candidate
receives)
_______ random variables can take all the possible
values in a given range(the weight of animals in a
certain regions)
14. Random variable- expected value
The mean of the probability distribution is referred
to as the ______ ______, and is represented by
μₓ.
which just means that the mean(or expected value)
of a random variable is a weighted average
15. Random Variable- Expected Value
For this probability distribution, the
expected value is
= 0(1/8) + 1(3/8) + 2(3/8) + 3(1/8)= 12/8=
1.5
16. Law of Large Numbers
The _______ of _______ _______states that the
actual mean of many trials approaches the true mean
of the distribution as the number of trials increases
18. Binomial Distribution
________ ________ models situations with the
following conditions:
1. Each observation falls into one of just two categories(
success or failure)
2. The number of observations is the fixed number n
3. The n observations are all independent
4. The probability of success, p, is the same for each
observation
19. Binomial Distribution
For data produced with the binomial model, the
binomial random variable is the number of
successes, X.
The probability distribution of X is a binomial
distribution
When finding binomial probabilities, remember that
you are finding the probability of obtaining k successes
in n trials
25. Geometric Distribution
Each observation falls into one of two categories,
success or failure
The variable of interest (usually X) is the number of
trials required to obtain the first success
The n observations are all independent
The probability of success, p, is the same for each
observation
26. Geometric Distribution
Example: If one planned to roll a die until they got a 5, the random
variable X= the number of trials until the first 5 occurs.
Find the probability that it would take 8 rolls given that all the
conditions of the geometric model are met
27. Geometric Distribution
Expected Value of Geometric Distributions
If X is a geometric random variable with probability of success P
on each trial, then the mean or _______ _______ of the
random variable is μ= 1/p.