Correlation- an introduction and application of spearman rank correlation by...Gunjan Verma
this presentation contains the types of correlation, uses, limitations, introduction to spearman rank correlation, and its application. a numerical is also given in the presentation
Correlation- an introduction and application of spearman rank correlation by...Gunjan Verma
this presentation contains the types of correlation, uses, limitations, introduction to spearman rank correlation, and its application. a numerical is also given in the presentation
This is about the correlation analysis in statistics. It covers types, importance,Scatter diagram method
Karl pearson correlation coefficient
Spearman rank correlation coefficient
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
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'Criterion Variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.
Overviews non-parametric and parametric approaches to (bivariate) linear correlation. See also: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Correlation
Statistics - Simple Linear and Multiple Linear RegressionBryll Edison Par
Introduction to simple and multiple linear regression.
https://issuu.com/arbrylledisonparmodules/docs/archi203_par_report_multiple_and_simple_linear_reg
This is about the correlation analysis in statistics. It covers types, importance,Scatter diagram method
Karl pearson correlation coefficient
Spearman rank correlation coefficient
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
In statistics, regression analysis is a statistical process for estimating the relationships among variables. It includes many techniques for modeling and analyzing several variables, when the focus is on the relationship between a dependent variable and one or more independent variables. More specifically, regression analysis helps one understand how the typical value of the dependent variable (or 'Criterion Variable') changes when any one of the independent variables is varied, while the other independent variables are held fixed. Most commonly, regression analysis estimates the conditional expectation of the dependent variable given the independent variables – that is, the average value of the dependent variable when the independent variables are fixed. Less commonly, the focus is on a quantile, or other location parameter of the conditional distribution of the dependent variable given the independent variables. In all cases, the estimation target is a function of the independent variables called the regression function. In regression analysis, it is also of interest to characterize the variation of the dependent variable around the regression function which can be described by a probability distribution.
Overviews non-parametric and parametric approaches to (bivariate) linear correlation. See also: http://en.wikiversity.org/wiki/Survey_research_and_design_in_psychology/Lectures/Correlation
Statistics - Simple Linear and Multiple Linear RegressionBryll Edison Par
Introduction to simple and multiple linear regression.
https://issuu.com/arbrylledisonparmodules/docs/archi203_par_report_multiple_and_simple_linear_reg
Computer is an electronic device that is designed to work with Information. The term computer is derived from the Latin term ‘computare’, this means to calculate or programmable machine. Computer cannot do anything without a Program. It represents the decimal numbers through a string of binary digits. The Word 'Computer' usually refers to the Center Processor Unit plus Internal memory.
Charles Babbage is called the "Grand Father" of the computer. The First mechanical computer designed by Charles Babbage was called Analytical Engine. It uses read-only memory in the form of punch cards.
Computer is an advanced electronic device that takes raw data as input from the user and processes these data under the control of set of instructions (called program) and gives the result (output) and saves output for the future use. It can process both numerical and non-numerical (arithmetic and logical) calculations.
A computer is an electronic machine, capable of performing basic operations like addition, subtraction, multiplication, division, etc. The computer is also capable of storing information, which can be used later. It can process millions of instructions in a few seconds and at the same time with high accuracy. Hence a computer can be defined as an automatic electronic machine for performing calculations or controlling operations that are expressible in numerical or logical terms. Computers are very accurate and save time by performing the assigned task very fast. They don’t get bored.
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
Correlation Analysis for MSc in Development Finance .pdfErnestNgehTingum
• Correlation is another way of assessing the relationship between variables.
– it measures the extent of correspondence between the ordering of two random variables.
• There is a large amount of resemblance between regression and correlation but for their methods of interpretation of the relationship.
– For example, a scatter diagram is of tremendous help when trying to describe the type of relationship existing between two variables.
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
Unit-I, BP801T. BIOSTATISITCS AND RESEARCH METHODOLOGY (Theory)
Correlation: Definition, Karl Pearson’s coefficient of correlation, Multiple correlations -
Pharmaceuticals examples.
Correlation: is there a relationship between 2
variables.
Cancer Epidemiology, Risk factors for most common types, mortality, prevention and yeild of cancer prevention. gender, geography, infections, tobacco, environmental riskk factors.
Community diagnosis is vital in health planning, evaluation and needs assessment, several types of indicators are valid to be used for community diagnosis including Socio-economic, demographics, health system, and living arrangements.
Diagnostic, screening tests, differences and applications and their characteristics, four pillars of screening tests, sensitivity, specificity, predictive values and accuracy
Competency-based education in Public Health, a model of employing Hybrid-PBL educational method in building core Public Health competencies at the undergraduate medical education.
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
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/
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.
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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.
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
Ch03-Managing the Object-Oriented Information Systems Project a.pdf
Linear Correlation
1. 05/04/14 Dr Tarek Amin 1
Investigating the Relationship
between Two orMore Variables
(Correlation)
Professor Tarek Tawfik Amin
Public Health, Faculty of Medicine
Cairo University
amin55@myway.com
2. The Relationship Between Variables
Variables can be categorized into two types when investigating
their relationship:
Dependent:
A dependent variable is explained oraffected
by an independent variable. Age and height
Independent :
Two variables are independent if the pattern of
variation in the scores forone variable is not
related orassociated with variation in the scores
forthe othervariable.
The level of education in Ecuadorand the infant
mortality in Mali
3. Techniques used to Analyze the Relationship between Two
Variables
Method Examples
I- Tabularand graphical methods:
These present data in way that reveals a
possible relationship between two
variables.
II-Numerical methods:
Mathematical operations used to quantify,
in a single number, the strength of a
relationship (measures of association).
When both variables are measured at least
at the ordinal level they also indicate the
direction of the relationship.
Bivariate table for categorical data
(nominal/ordinal data)
Scatter plot for interval/ratio.
Lambda, Cramer’s V (nominal)
Gamma, Somer’s d, Kendall’s tau-b/c
(ordinal with few values)
Spearman’s rank order Co/Co.
(ordinal scales with many values)
Pearson’s product moment correlation
(Interval/ratio)
These techniques are called collectively as
Bi-variate descriptive statistics
4. Correlation: indications
o Correlational techniques are used to study
relationships.
o They may be used in exploratory studies in
which one to intent to determine whether
relationships exist,
o And in hypothesis testing about a particular
relationship.
5. Correlations techniques used to
assess
the existence,
the direction
and the strength
of association between
variables.
6. Pearson Correlation (Numeric, interval/ratio)
The Pearson product moment correlation coefficient (rorrho)
is the usual method by which the relation between two
variables is quantified.
Type of data required:
Interval/ratio sometimes ordinal data.
At least two measures on each subjects at the
interval/ratio level.
Assumptions:
The sample must be representative of the population.
The variables that are being correlated must be normally
distributed.
The relationship between variables must be LINEAR.
8. 05/04/14 Dr Tarek Amin 8
Relationships Measured with Correlation Coefficient
The correlation coefficient is the cross products
of the Z-scores.
[ ]( )nzXzYr ∑=
Where:
ZX= the z-score of variable X
ZY= the z-score of variable Y
N= number of observations
9. Because the means and standard deviations
of any given two sets of variables are
different, we cannot directly compare the
two scores.
However, we can, transform them from the
ordinary absolute figures to Z-scores with a
mean of 0 and SDof 1.
The correlation is the mean of the cross-
products of the Z-score foreach value
included, a measure of how much each pair
of observations (scores) varies together.
Tips
10. Correlation Coefficient (r)
The correlation coefficient r allows us to
state mathematically the relationship that
exists between two variables. The correlation
coefficient may range from +1.00 through 0.00 to – 1.00.
A + 1.00 indicates a perfect positive
relationship,
0.00 indicates no relationship,
and -1.00 indicates a perfect negative
relationship.
11. I-Strength of the Correlation Coefficient
How large r should forit to be useful?
In decision making at least 0.95 while those concerning
human behaviors 0.5 is fair.
The strengths of r are as follow:
0.00-0.25 little if any.
0.26 -0.49 LOW
0.50- 0.69 Moderate
0.70 - 0.89 High
0.90 – 1.00 Very high .
12. II-Significance of the Correlation
The level of statistical significance is greatly
affected by the sample size n.
If r is based on a sample of 1,000, there is much
greaterlikelihood that it represents the r of the
population than if it were based on 10 subjects.
13. ‘ With large sample sizes rs that are described as
demonstrating (little if any) relationship are
statistically significant’
Statistical significance implies that r
did not occurby chance, the
relationship is greaterthan zero.
14. - The correlation coefficient also tell us the type
of relation that exists; that is, whetheris
positive ornegative.
- The relationship between job satisfaction and job
turnoverhas been shown to be negative; an
inverse relationship exists between them.
When one variable increases, the other decreases.
- Those with highergrades have lowerdropout rates
(a positive relationship).
Increases in the score of one variable is accompanied by
increase in the other.
III- Direction of correlation
15. Relationships Measured by Correlation
Coefficients:
When using the formula with Z-scores, ris the
average of the corss-products of the Z-scores.
[ ]( )nzXzYr ∑=
A five subjects took a quiz X, on which the scores ranged from
6to 10 and an examination Y, on which the scores ranged form
82to 98.
Calculate r and determine the pattern of correlation?
16. 05/04/14 Dr Tarek Amin 16
Formula forcalculating correlation coefficient r.
[ ]( )nzXzYr ∑=
17. A perfect positive relationship between two variables.
Subjects X (quiz) Y
(examination
)
zX zY zX*zY
1
2
3
4
5
6
7
8
9
10
82
86
90
94
98
-1.42
-0.71
0.00
0.71
1.42
-1.42
0.71
0.00
0.71
1.42
2.0
0.5
0.0
0.5
2.0
mean X= 8, SD=1.41 mean Y= 90 sd=5.66 ∑zXzY= 5.00
r= ∑zXzY/n =
5.00/5 = +1
23. The following table is SPSS output describing the correlation between age, education in years,
smoking history, satisfaction with the current weight, and the overall state of health fora randomly
selected subjects.
Overall state
of health
Satisfaction
with current
weight
Smoking
history
Education in
years
Subject's
age
1.000
.
434
Subject's age
Pearson Correlation
Sig.(2 tailed)
N
.022
.649
419
Education in years
Pearson Correlation
Sig.(2 tailed)
N
-.108*
.026
423
.143**
.003
432
Smoking history
Pearson Correlation
Sig.(2 tailed)
N
-.009
.849
440
.033
.493
424
-.077
.109
432
Satisfaction with current
weight
Pearson Correlation
Sig.(2 tailed)
N
1.000
.
444
.370*
.000
443
-.200*
.000
441
.149**
.000
425
-.126**
.009
433
Overall state of health
Pearson Correlation
Sig.(2 tailed)
N
*Correlation is significant at the 0.05 level (2-tailed(.
** Correlation is significant at the 0.01 level (2-tailed).
24. Figure (1): Insulin resistance (HOMA-IR) in relation to
serum ferritin level among cases and controls.
Ferritin (log)
2.82.62.42.22.01.8
HOMA-RI
8
7
6
5
4
3
2
Controls
Sickle
Total Population
r=0.804, P=0.0001
25. Figure (2): 1,25 (OH) vitamin D in relation to body mass
index among obese and lean controls.
Body mass index
5040302010
VitaminDlevel
100
80
60
40
20
0
Lean
Obese
Total Population
r= -.166, P=0.036