This document discusses various statistical tools used in decision making, including regression analysis, confidence intervals, comparison tests, and analysis of variance. It provides examples of how regression analysis can be used to determine correlations and unknown parameters. It also explains how confidence intervals are calculated and used to determine how reliable a sample statistic is in estimating an unknown population parameter. Comparison tests are outlined as a method to determine if one process or supplier is better than another.
Application of Statistical and mathematical equations in Chemistry Part 2Awad Albalwi
Application of Statistical and mathematical equations in Chemistry
Part 2
Accuracy
Precision
Propagation of Error
Confidence Limits
F-Test Values
Student’s t-test
Paired Sample t-test
Q test
Least Squares Method
correlation coefficient
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
Topic: Coefficient of Variance
Student Name: Shakeela
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
Application of Statistical and mathematical equations in Chemistry Part 2Awad Albalwi
Application of Statistical and mathematical equations in Chemistry
Part 2
Accuracy
Precision
Propagation of Error
Confidence Limits
F-Test Values
Student’s t-test
Paired Sample t-test
Q test
Least Squares Method
correlation coefficient
Measure of dispersion has two types Absolute measure and Graphical measure. There are other different types in there.
In this slide the discussed points are:
1. Dispersion & it's types
2. Definition
3. Use
4. Merits
5. Demerits
6. Formula & math
7. Graph and pictures
8. Real life application.
Topic: Coefficient of Variance
Student Name: Shakeela
Class: B.Ed. 2.5
Project Name: “Young Teachers' Professional Development (TPD)"
"Project Founder: Prof. Dr. Amjad Ali Arain
Faculty of Education, University of Sindh, Pakistan
I split the presentation for the unit into two, as I added so many slides to help with student questions and misconceptions. This one focuses on mathematical aspects of the unit.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.2: Two Means, Independent Samples
Application of Multivariate Regression Analysis and Analysis of VarianceKalaivanan Murthy
The work is done as part of graduate coursework at University of Florida. The author studied master's in environmental engineering sciences during the making of the presentation.
Test of significance (t-test, proportion test, chi-square test)Ramnath Takiar
The presentation discusses the concept of test of significance including the test of significance examples of t-test, proportion test and chi-square test.
I split the presentation for the unit into two, as I added so many slides to help with student questions and misconceptions. This one focuses on mathematical aspects of the unit.
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 9: Inferences from Two Samples
9.2: Two Means, Independent Samples
Application of Multivariate Regression Analysis and Analysis of VarianceKalaivanan Murthy
The work is done as part of graduate coursework at University of Florida. The author studied master's in environmental engineering sciences during the making of the presentation.
Test of significance (t-test, proportion test, chi-square test)Ramnath Takiar
The presentation discusses the concept of test of significance including the test of significance examples of t-test, proportion test and chi-square test.
Quantitative Analysis for Emperical ResearchAmit Kamble
Overview for Approach Methods for quantitative analysis; which includes
1) Planning of Experiments
2) Data Generation
3) presentation of report
some numerical approach methods; data modeling; hypothesis methods
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
Please Subscribe to this Channel for more solutions and lectures
http://www.youtube.com/onlineteaching
Chapter 7: Estimating Parameters and Determining Sample Sizes
7.3: Estimating a Population Standard Deviation or Variance
2.0.statistical methods and determination of sample sizesalummkata1
statistical methods and determination of sample size
These guidelines focus on the validation of the bioanalytical methods generating quantitative concentration data used for pharmacokinetic and toxicokinetic parameter determinations.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
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.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...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.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
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.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
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/
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.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
3. STATISTICAL TOOLS USED TO ASSIST DECISION MAKING Regression Analysis Determining Confidence Interval Comparison Tests Analysis Of Variance Design Of Experiments Linear & Non-linear Programming Queuing Theory
5. REGRESSION ANALYSIS No Correlation (R = 0) Strong Positive Correlation ( R = .995) Positive Linear correlation (r=0.85) Negative Linear Correlation (r=-0.85)
6. TYPES OF REGRESSION ANALYSIS (AMONG MANY) Exponential Y =AB X Geometric Y = AX B Logarithmic Y = A o + A 1 (logX) + A 2 (logX) 2 Linear Y = A o + A 1 X Linear Regression Is the Most Common
7. THE PURPOSE OF REGRESSION ANALYSIS Correlation r =1 = perfect correlation r = 0 = no correlation Determination Of Unknown Parameters r = (X i -X) (Y i - Y) (X i -X) 2 (Y i - Y) 2 1 = Y i (X i -X) n i =1 (X i -X) 2 n i =1 Y = 0 + 1 X ^ ^ ^ 0 = Y - ^ 1 X ^
9. Statistics Usually Do Not Represent Absolute Truth Very Often They Are A Good Guess How Good Of A Guess Is Explained By The Confidence Interval Understanding Confidence Intervals Will Allow You To Better Evaluate Critical Statistics WHY DO WE NEED CONFIDENCE INTERVALS
10. Problem: Commanding General Needs To Know Average Weight of Officers On The Base 1,000 Officers At The Base Six Officers Selected And Weighed Officer # 1: 68 kilos Officer # 2: 57 kilos Officer # 3: 72 kilos Officer # 4: 71 kilos Officer # 5: 100 kilos Officer # 6: 63 kilos Average Weight of Our Sample Is 71.8333 Kilos How Good Is This Statistic? A TYPICAL SITUATION
11.
12.
13. OR OR Single Sided Double-Sided Known Unknown CONFIDENCE INTERVAL EQUATIONS X - U n X + U n X - U n X + U n X - S t n X + S t n X - S t n X + S t n
14. : Population Average : Population Standard Deviation : 1 - The Desired Confidence Level S : The Sample Standard Deviation v : Degrees Of Freedom Or n - 1 t : Data Derived From t Distribution U: Data Derived From Normal Distribution n: The Number of Units in The Sample EQUATION HELP Single Sided : Trying to Determine If the Population Average ( ) Is Less Than or Greater Than the Sample Average ( X ) Double Sided : Trying To Determine The Upper& Lower Boundaries of the Population Average ( )
15. SOLUTION TO THE OFFICER WEIGHT PROBLEM X - S t n X + S t n 71.833 - 2.015 (14.878) 6 71.833 + 2.015 (14.878) 6
16. STANDARD DEVIATION CONFIDENCE INTERVAL Almost The Same But Different See Page 300 Of Implementing Six Sigma We Will Use The Chi Square Distribution ( 2 ) 2 /2; v 2 (1- /2; v) ( n - 1) s 2 ( n - 1) s 2 [ ] 1/2 [ ] 1/2
17. 9.999 31.0227 EXAMPLE USING SIX OFFICER WEIGHTS We Are 90% Confident That Standard Deviation Of All Officer Weights Is Between 10 Kilos & 31.0 Kilos 2 /2; v 2 (1- /2; v) ( n - 1) s 2 ( n - 1) s 2 [ ] 1/2 [ ] 1/2 11.07 1.15 (5) (14.878) 2 (5) (14.878) 2 [ ] 1/2 [ ] 1/2 (99.9796) 1/2 (962.4125) 1/2
19. Is Process B Better Than Process A? Is Supplier B Better Than Supplier A? These Questions Are Always Being Asked COMPARISON TESTS Comparison Tests Can Give The Right Answers
20. STEPS INVOLVED IN COMPARISON TESTING Define Precisely The Problem Objective Formulate A Null Hypothesis Evaluation By A One Or Two Tail Test Choose A Critical Value Of A Test Statistic Calculate A Test Statistic Make Inference About The Population Communicate The Findings
21. TYPICAL DECISIONS 1. A chemical batch process has yielded average of 802 tons of product for a long period. Production records for last five batches show following results: 803, 786, 806, 791, and 794. Can we predict with 95% confidence that the process is now at a lower average? 2. The average vial height from an injection molding process has been 5.00 inches with a standard deviation of .12”. A vendor claims to have a new material that will reduce the height variation. An experiment, conducted using the new material, yielded the following results: 5.10, 4.90, 4.92, 4.87, 5.09, 4.89, 4.95, 4.88. The average height of the eight vials is 4.95” and the standard deviation is .093”
22. Average vial height from an injection molding process has been 5.00 inches with a standard deviation of .12”. Vendor claims to have a new material that will reduce height variation. An experiment, conducted using new material yielded the following results: 5.10, 4.90, 4.92, 4.87, 5.09, 4.89, 4.95, 4.88. Average height of the eight vials is 4.95” and standard deviation is .093” Is the new material producing shorter vials with the existing molding machine set-up (with 95% confidence)? Is height variation actually less with the new material (with 95% confidence)
23. NULL HYPOTHESIS The Hypothesis To Be Tested A Null Hypothesis Can Only Be Rejected. It Cannot Be Accepted Because of a Lack of Evidence to Reject It Example: If A Claim Is That Process B Is Better Than Process A The Null Hypothesis Is That Process A = Process B H o : A = B
24. Table 19.1(page 322 Implementing Six Sigma ) Most Likely: 1 2 2 2 And Is Unknown 1. Calculate t 0 = 2. Calculate = COMPARISON METHODOLOGY X 1 - X 2 S 1 2 n 1 n 2 S 2 2 + S 1 2 n 1 ( ) S 2 2 n 2 ( ) + [ ] 2 (S 1 2 / n 1 ) 2 (S 2 2 / n 2 ) 2 n 1 + 1 n 2 + 1 +
25. COMPARISON METHODOLOGY (Continued) 3. Look Up Value Of t Using Table E Or Table D ( Implementing Six Sigma Pages 697 Or 698) 4. Reject The Null Hypothesis If t 0 Is Greater Than Than t