This presentation discusses correlation, rank correlation, bivariate analysis, and the chi-square test. Correlation measures the strength and direction of association between two variables. Rank correlation analyzes relationships between different rankings using Spearman's correlation coefficient. Bivariate analysis examines the empirical relationship between two variables. The chi-square test statistically tests if an observed distribution differs from an expected distribution using a chi-square distributed test statistic.
Statistical techniques for measuring the closeness of the relationship between variables.It measures the degree to which changes in one variable are associated with changes in another.It can only indicate the degree of association or covariance between variables. Covariance is a measure of the extent to which two variables are related.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
Statistical techniques for measuring the closeness of the relationship between variables.It measures the degree to which changes in one variable are associated with changes in another.It can only indicate the degree of association or covariance between variables. Covariance is a measure of the extent to which two variables are related.
HOW IS IT USEFUL IN FIELD OF FORENSIC SCIENCE AND IN THIS I HAVE SHOWN THE TYPES OF CORRELATION, SIGNIFICANCE , METHODS AND KARL PEARSON'S METHOD OF CORRELATION
This is about the correlation analysis in statistics. It covers types, importance,Scatter diagram method
Karl pearson correlation coefficient
Spearman rank correlation coefficient
Fundamental of Statistics and Types of CorrelationsRajesh Verma
Fundamental of Statistics and Types of Correlations. Pearson r, Point Biserial, Phi Coefficient, Biserial, Tetrachoric, Spearman Rank Difference, Kendall's tau, Inferential Statistics, Descriptive Statistics
Multiple Correlation Coefficient denoting a correlation of one variable with multiple other variables. The Multiple Correlation Coefficient, R, is a measure of the strength of the association between the independent (explanatory) variables and the one dependent (prediction) variable. This presentation explains the concept of multiple correlation and its computation process.
This is about the correlation analysis in statistics. It covers types, importance,Scatter diagram method
Karl pearson correlation coefficient
Spearman rank correlation coefficient
Fundamental of Statistics and Types of CorrelationsRajesh Verma
Fundamental of Statistics and Types of Correlations. Pearson r, Point Biserial, Phi Coefficient, Biserial, Tetrachoric, Spearman Rank Difference, Kendall's tau, Inferential Statistics, Descriptive Statistics
Multiple Correlation Coefficient denoting a correlation of one variable with multiple other variables. The Multiple Correlation Coefficient, R, is a measure of the strength of the association between the independent (explanatory) variables and the one dependent (prediction) variable. This presentation explains the concept of multiple correlation and its computation process.
This presentation covered the following topics:
1. Definition of Correlation and Regression
2. Meaning of Correlation and Regression
3. Types of Correlation and Regression
4. Karl Pearson's methods of correlation
5. Bivariate Grouped data method
6. Spearman's Rank correlation Method
7. Scattered diagram method
8. Interpretation of correlation coefficient
9. Lines of Regression
10. regression Equations
11. Difference between correlation and regression
12. Related examples
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
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
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
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.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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.
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/
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
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.
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
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.
Monitoring Java Application Security with JDK Tools and JFR Events
Sumit presentation
1. PRESENTATION ON
CORRELATION
RANK CORRELATION
BIVARITE ANANLSIS
&
CHI SQURE TEST
PRESENTED BY-SUMIT BHARTI
2. CORRELATION
Definition:
while studying two variables at the same time,if it
is found that the change in one variable is
reciprocated by a corresponding change in the
other variable either directly or inversely ,then the
two variables are known to be associated or
correlated.
In correlation analysis ,we must be careful about a
cause and effect relation between the two
variables.
3. example
If the quantities(X,Y) vary in such a way that
change in one variable corresponds to
change in the other variable then the variables
X and Y are correlated.
Types of Correlation:
The important ways of classifying the
correlation are:
1. Positive correlation
2. negative correlation
4. POSITIVE CORRELATION
If two variables move in the same direction i.e.an increase (or
decrease) on the part of one variable introduces an
increase(or decrease)on the part of the other variable, then
the two variablea are known to be positively correlated.
As for example,
profit and investment, Height and weight,yield and rainfall
etc are positively correlated.
5. NEgATIVE CORRELATION
on the other hand. If two variables move in the
opposite directions i.e.an increase (or a decrease ) on the part
of one variable result a decrease (or a
increase)
on the part of the other variable, then the two variables are
known to have a negative correlation .
EXP-
The price and demand of an item,the profit of insurance
company and the number of claims it has to meet etc. are
exp. of variables having a negative correlation.
6. RANK CORRELATION
“Rank correlation” is the study of relationships
between different rankings on the same set of items. It
deals with measuring correspondence between two
rankings, and assessing the significance of this
correspondence. Spearman’s correlation coefficient is
defined as:
r = 1-((6∑D2)/(N(N-1)2))
Where r , denotes rank coefficient of correlation and D
refers to the difference of rank relation between paired I
tems in two series.
7. TYPES OF RANK CORRELATION
In the rank correlation we may have two types of
problems:
• Where ranks are given
• Where ranks are not given
• Where repeated ranks occur
Note:
If r = 1 then there is a perfect Positive correlation
If r = 0 then the variables are uncorrelated
If r=-1 then there is a perfect Negative Correlation
12. • Step 5:
– Apply the formula:
r=
Where d= difference, n=no.of data
13. BIVARIATE ANALYSIS
• Bivariate analysis is one of the simplest forms
of the quantitative (statistical) analysis . It involves the
analysis of two variables (often denoted as X, Y), for
the purpose of determining the empirical
relationship between them.
• In order to see if the variables are related to one
another, it is common to measure how those two
variables simultaneously change together.
14. Bivariate analysis can be contrasted with
univariate analysis in which only one
variable is analysed. Furthermore, the
purpose of a univariate analysis is
descriptive. The major differentiating
point between univariate and bivariate
analysis, in univariate there is only one
variable is analysed. Where as bivariate
is the analysis of the relationship
between the two variables.
15. EXAMPLE
A businessman may be keen to know what
amount of investment Would yield a desired level
of pofite.
Student may want know whether performing
better in the selection test would enhance his
or her chance of doing well in final examination
16. CHI-SQUARE TEST
a chi square test is an statistical hypothesis test in
which the test statistic has a chi square distribution
when the null hypthepothesis is true, or any in which
the probability distribution of the test
statistic(assuming null hypothesis is true) can be
made to approximate a chi square
Distribution as closely as desired by making the sample
size large enough.
17. STEP OF CHI SQUARE TEST
(i)Calculate the expected frequency (E)
(ii)Compute the deviation (0-E)and then square
these deviation to obtain (O-E)2.
(iii)
divide the square deviation i.e. (O-E)2
by the corresponding expected frequency .
(O-E)2
E
18. .
(iv) Obtain the sum of all value
computed in the step (iii) to
compute
19. • This gives the value of X2 , if it is zero
multiplies that there is no
discrepancy between the observed
and the expected frequencies.
• The greater the value of X2 the
greater will be discrepancy between
the observed and expected
frequencies.
20. .
(v)Then check the degree of freedom =
n-1
(vi) Compare the calculated value of X 2
with table value if it is less than the
table value then it will be accepted if it
is more than table value then it will be
rejected.