In the previous lesson we discussed a measure of location known as the measure of central tendency. There are other measures of location which are useful in describing the distribution of the data set. These measures of location include the maximum, minimum, percentiles, deciles and quartiles. How to compute and interpret these measures are also discussed in this lesson.
In the previous lesson we discussed a measure of location known as the measure of central tendency. There are other measures of location which are useful in describing the distribution of the data set. These measures of location include the maximum, minimum, percentiles, deciles and quartiles. How to compute and interpret these measures are also discussed in this lesson.
Business statistics takes the data analysis tools from elementary statistics and applies them to business. For example, estimating the probability of a defect coming off a factory line, or seeing where sales are headed in the future. Many of the tools used in business statistics are built on ones youâve probably already come across in basic math: mean, mode and median, bar graphs and the bell curve, and basic probability. Hypothesis testing (where you test out an idea) and regression analysis (fitting data to an equation) builds on this foundation.
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In this chapter you learn:
Definition of Statistics & Identify variables in a statistics.
Types of Statistics
Distinguish b/w quantitative & qualitative variables.
Determine the 4 levels of measurement.
Identify populations & samples.
Distinguish different types of Sampling
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GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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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.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
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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/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more âmechanicalâ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
JMeter webinar - integration with InfluxDB and GrafanaRTTS
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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 Overviewâ
-------------------------------------------
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
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
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The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. Whatâs 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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
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Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as âpredictable inferenceâ.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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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.
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
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Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
2. Learning Objectives LO1 List ways statistics is used. LO2 Know the differences between descriptive and inferential statistics. LO3 Understand the differences between a sample and a population. LO4 Explain the difference between qualitative and quantitative variables. LO5 Compare the differences between discrete and continuous variables. LO6 Recognize the levels of measurement in data. 1-2
3. LO1 What is Statistics Statistics is the science of collecting, organizing, presenting, analyzing, and interpreting data to assist in making more effective decisions.
4. Uses of Statistics Statistics is one of the tools used to make decisions in business We apply statistical concepts in our lives As a student of business or economics, basic knowledge and skills to organize, analyze, and transform data and to present the information. LO1 List ways statistics is used. 1-4
5. Why Study Statistics? LO1 Numerical information is everywhere Statistical techniques are used to make decisions that affect our daily lives The knowledge of statistical methods will help you understand how decisions are made and give you a better understanding of how they affect you. No matter what line of work you select, you will find yourself faced with decisions where an understanding of data analysis is helpful. 1-5
6. Why Study Statistics? LO1 In order to make an informed decision, you will need to be able to: Determine if the existing data is adequate or if additional information is needed. Gather additional information, if needed, in such a way that it does not produce misleading results. Summarize the information in an informative manner Analyze the available information Draw conclusions and make inferences while assessing the risk of an incorrect conclusion.
7. Who Uses Statistics? LO1 Statistical techniques are used extensively by marketing, accounting, quality control, consumers, professional sports people, hospital administrators, educators, politicians, physicians, etc... How many of you use quantitative information in your job? 1-7
8. LO2 Know the differences between descriptive and inferential statistics. Types of Statistics â Descriptive Statistics and Inferential Statistics Descriptive Statistics - methods of organizing, summarizing, and presenting data in an informative way. EXAMPLE 1: The United States government reports the population of the United States was 179,323,000 in 1960; 203,302,000 in 1970; 226,542,000 in 1980; 248,709,000 in 1990, and 265,000,000 in 2000. EXAMPLE 2: According to the Bureau of Labor Statistics, the average hourly earnings of production workers was $17.90 for April 2008. 1-8
9. LO2 Types of Statistics â Descriptive Statistics and Inferential Statistics Inferential Statistics: A decision, estimate, prediction, or generalization about a population, based on a sample. Note: In statistics the word population and sample have a broader meaning. A population or sample may consist of individuals or objects 1-9
10. A populationis a collectionof all possible individuals, objects, or measurements of interest. Asampleis a portion, or part, of the population of interest Population versus Sample LO3 Understand the differences between a sample and a population. 1-10
11. LO3 Why take a sample instead of studying every member of the population? Prohibitive cost of census Destruction of item being studied may be required Not possible to test or inspect all members of a population being studied 1-11
12. Usefulness of a Sample in Learning about a Population Using a sample to learn something about a population is done extensively in business, agriculture, politics, and government. EXAMPLE: Television networks constantly monitor the popularity of their programs by hiring Nielsen and other organizations to sample the preferences of TV viewers. LO3 1-12
13. Types of Variables LO4 Explain the difference between qualitative and quantitative variables. A. Qualitative or Attribute variable - the characteristic being studied is nonnumeric. EXAMPLES: Gender, religious affiliation, type of automobile owned, state of birth, eye color are examples. B. Quantitative variable - information is reported numerically. EXAMPLES: balance in your checking account, minutes remaining in class, or number of children in a family. 1-13
14. LO5 Compare the differences between discrete and continuous variables. Quantitative Variables - Classifications Quantitative variables can be classified as either discrete or continuous. A. Discrete variables: can only assume certain values and there are usually âgapsâ between values. EXAMPLE: the number of bedrooms in a house, or the number of hammers sold at the local Home Depot (1,2,3,âŠ,etc). B. Continuous variable can assume any value within a specified range. EXAMPLE: The pressure in a tire, the weight of a pork chop, or the height of students in a class. 1-14
16. LO6 Recognize the levels of measurement in data. Four Levels of Measurement Interval level - similar to the ordinal level, with the additional property that meaningful amounts of differences between data values can be determined. There is no natural zero point. EXAMPLE: Temperature on the Fahrenheit scale. Ratio level - the interval level with an inherent zero starting point. Differences and ratios are meaningful for this level of measurement. EXAMPLES:Monthly income of surgeons, or distance traveled by manufacturerâs representatives per month. Nominal level - data that is classified into categories and cannot be arranged in any particular order. EXAMPLES: eye color, gender, religious affiliation. Ordinal level â data arranged in some order, but the differences between data values cannot be determined or are meaningless. EXAMPLE: During a taste test of 4 soft drinks, Mellow Yellow was ranked number 1, Sprite number 2, Seven-up number 3, and Orange Crush number 4. 1-16
17. Nominal-Level Data LO6 Properties: Observations of a qualitative variable can only be classified and counted. There is no particular order to the labels. 1-17
18. Ordinal-Level Data LO6 Properties: Data classifications are represented by sets of labels or names (high, medium, low) that have relative values. Because of the relative values, the data classified can be ranked or ordered. 1-18
19. Interval-Level Data LO6 Properties: Data classifications are ordered according to the amount of the characteristic they possess. Equal differences in the characteristic are represented by equal differences in the measurements. Example: Womenâs dress sizes listed on the table. 1-19
20. Ratio-Level Data Practically all quantitative data is recorded on the ratio level of measurement. Ratio level is the âhighestâ level of measurement. Properties: Data classifications are ordered according to the amount of the characteristics they possess. Equal differences in the characteristic are represented by equal differences in the numbers assigned to the classifications. The zero point is the absence of the characteristic and the ratio between two numbers is meaningful. LO6 1-20
21. Why Know the Level of Measurement of a Data? LO6 The level of measurement of the data dictates the calculations that can be done to summarize and present the data. To determine the statistical tests that should be performed on the data 1-21
22. LO6 Summary of the Characteristics for Levels of Measurement 1-22