this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
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
Brief description of the concepts related to correlation analysis. Problem Sums related to Karl Pearson's Correlation, Spearman's Rank Correlation, Coefficient of Concurrent Deviation, Correlation of a grouped data.
this ppt gives you adequate information about Karl Pearsonscoefficient correlation and its calculation. its the widely used to calculate a relationship between two variables. The correlation shows a specific value of the degree of a linear relationship between the X and Y variables. it is also called as The Karl Pearson‘s product-moment correlation coefficient. the value of r is alwys lies between -1 to +1. + 0.1 shows Lower degree of +ve correlation, +0.8 shows Higher degree of +ve correlation.-0.1 shows Lower degree of -ve correlation. -0.8 shows Higher degree of -ve correlation.
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
Brief description of the concepts related to correlation analysis. Problem Sums related to Karl Pearson's Correlation, Spearman's Rank Correlation, Coefficient of Concurrent Deviation, Correlation of a grouped data.
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
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.
It is most useful for the students of BBA for the subject of "Data Analysis and Modeling"/
It has covered the content of chapter- Data regression Model
Visit for more on www.ramkumarshah.com.np/
The standard deviation is a measure of the spread of scores within a set of data. Usually, we are interested in the standard deviation of a population.
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.
To get a copy of the slides for free Email me at: japhethmuthama@gmail.com
You can also support my PhD studies by donating a 1 dollar to my PayPal.
PayPal ID is japhethmuthama@gmail.com
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
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
2. Contents
Meaning of Correlation
Types of correlation
Correlation coefficient
Range of correlation coefficient
Interpretation of Correlation Coefficient (r)
Meaning of Regression
Difference between Correlation & Regression
Lines of Regression
Why two lines of regression
Regression coefficient
3. Correlation
Correlation is a statistical tool that helps
to measure and analyse the degree of
relationship between two variables.
Correlation analysis deals with the
association between two or more
variables.
4. Cont…
The measure of correlation called the correlation
coefficient .
The degree of relationship is expressed by coefficient
which range from correlation ( -1 ≤ r ≥ +1)
The direction of change is indicated by a sign.
The correlation analysis enable us to have an idea
about the degree & direction of the relationship
between the two variables under study.
6. Cont…
Positive Correlation: The correlation is said to
be positive correlation if the values of two
variables changing with same direction.
Ex. Pub. Exp. & sales, Height & weight.
Negative Correlation: The correlation is said to
be negative correlation when the values of
variables change with opposite direction.
Ex. Price & qty. demanded.
7. Direction of the Correlation
Positive relationship – Variables change in the
same direction.
As X is increasing, Y is increasing
As X is decreasing, Y is decreasing
E.g., As height increases, so does weight.
Negative relationship – Variables change in
opposite directions.
As X is increasing, Y is decreasing
As X is decreasing, Y is increasing
E.g., As TV time increases, grades decrease
Indicated by
sign; (+) or (-).
8. More Examples
Positive relationshipsPositive relationships
water consumption
and temperature.
study time and
grades.
Negative relationshipsNegative relationships:
alcohol consumption
and driving ability.
Price & quantity
demanded
9. Karl Pearson's
Coefficient of Correlation
Pearson’s ‘r’ is the most common
correlation coefficient.
Karl Pearson’s Coefficient of Correlation
denoted by- ‘r’ The coefficient of
correlation ‘r’ measure the degree of linear
relationship between two variables say x
& y.
10. Karl Pearson's
Coefficient of Correlation
Karl Pearson’s Coefficient of
Correlation denoted by- r
-1 ≤ r ≤ +1
Degree of Correlation is expressed by a
value of Coefficient
Direction of change is Indicated by sign
( - ve) or ( + ve)
11. Interpretation of Correlation
Coefficient (r)
The value of correlation coefficient ‘r’ ranges
from -1 to +1
If r = +1, then the correlation between the two
variables is said to be perfect and positive
If r = -1, then the correlation between the two
variables is said to be perfect and negative
If r = 0, then there exists no correlation between
the variables
12. Regression Analysis
Regression Analysis is a very powerful
tool in the field of statistical analysis in
predicting the value of one variable, given
the value of another variable, when those
variables are related to each other.
13. Regression Analysis
Regression Analysis is mathematical measure of
average relationship between two or more
variables.
Regression analysis is a statistical tool used in
prediction of value of unknown variable from
known variable.
14. Advantages of Regression Analysis
Regression analysis provides estimates of
values of the dependent variables from the
values of independent variables.
Regression analysis also helps to obtain a
measure of the error involved in using the
regression line as a basis for estimations .
Regression analysis helps in obtaining a
measure of the degree of association or
correlation that exists between the two variable.
15. Regression line
Regression line is the line which gives the best
estimate of one variable from the value of any
other given variable.
The regression line gives the average
relationship between the two variables in
mathematical form.
16. Regression line
For two variables X and Y, there are always two
lines of regression –
Regression line of X on Y : gives the best
estimate for the value of X for any specific
given values of Y
X = a + b Y a = X - intercept
b = Slope of the line
X = Dependent variable
Y = Independent variable
17. Regression line
For two variables X and Y, there are always two
lines of regression –
Regression line of Y on X : gives the best
estimate for the value of Y for any specific given
values of X
Y = a + bx a = Y - intercept
b = Slope of the line
Y = Dependent variable
x= Independent variable
19. Properties of the Regression Coefficients
The coefficient of correlation is geometric mean of the two
regression coefficients. r = √ byx*bxy
If byx is positive than bxy should also be positive & vice
versa.
If one regression coefficient is greater than one the other
must be less than one.
The coefficient of correlation will have the same sign as
that our regression coefficient.
Arithmetic mean of byx&bxyis equal to or greater than
coefficient of correlation. byx+bxy/2≥r
Regression coefficient are independent of origin but not of
scale.
20. Correlation analysis vs.
Regression analysis.
Regression is the average relationship between two
variables
Correlation need not imply cause & effect
relationship between the variables understudy.- R A
clearly indicate the cause and effect relation ship
between the variables.
There may be non-sense correlation between two
variables.- There is no such thing like non-sense
regression.
21. Cont…
When the correlation coefficient is zero,
the two lines are perpendicular and when it
is ±1, then the regression lines are parallel
or coincide.