The document discusses correlation and the correlation coefficient r. It explains that r is a measure of the strength and direction of the linear relationship between two variables. An r value closer to 1 indicates a stronger positive linear relationship, while a value closer to -1 reflects a stronger negative linear relationship. The formula for computing r is also provided, which calculates r as the covariance of the two variables divided by the product of their standard deviations. Scatter plots with varying r values are presented to demonstrate how correlation indicates linear association between two variables.
Statistical learning is a method for using variables like TV, radio and newspaper advertising spending (features) to predict sales (response). It builds a statistical model, Sales = f(TV, radio, newspaper), to understand the relationship between the features and response. This allows one to make sales predictions based on advertising spending and determine which features have the most influence on sales.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
The document discusses various types of analyses for association, including correlation analysis, perfect positive and negative linear association, positive and negative linear association, no linear association, nonlinear association, and the Pearson product moment correlation coefficient. It also discusses simple linear regression analysis, including its objectives, data and scatter diagrams, the regression equation, and calculating simple regression. Finally, it discusses correlation from ranks using the Spearman rank-difference method and chi-square tests for independence using expected frequencies and a test statistic.
This document discusses Pearson's Correlation Coefficient (r), which is a measure of the strength and direction of a linear relationship between two variables. It provides an example calculation of r using sample data points. While the example initially shows a strong negative correlation, adding additional non-linear data points results in r equaling 0, indicating no relationship due to the non-linearity between the variables. Pearson's r only describes linear relationships.
The document discusses assessing the adequacy of phylogenetic trait models by comparing observed trait data to simulated trait data generated under the fitted model. It proposes building a "unit tree" by rescaling branch lengths based on the expected covariance structure implied by the model, so that contrasts on this tree should be independent and normally distributed if the model is adequate. A variety of test statistics can then be computed on the contrasts of the observed data and compared to the distribution of those statistics on simulated contrasts to evaluate the model.
The treatment of large structural systems may be simplified by dividing the system into
smaller systems called components. The components are related through the
displacement, and force conditions at their junction points. Each component is represented
by mode shapes (or functions).
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document presents new 5x5 stiffness matrices for stability and dynamic analyses of line continua. The matrices were derived using energy variational principles and a five-term polynomial shape function. Analyses of four line continua and a portal frame using the new 5x5 matrices found results very close to exact solutions, with average percentage differences of 2.55% for stability and 0.14% for vibration. In contrast, analyses using traditional 4x4 matrices differed greatly from exact results, demonstrating the 5x5 matrices are better suited for stability and dynamic analyses of frame structures.
Statistical learning is a method for using variables like TV, radio and newspaper advertising spending (features) to predict sales (response). It builds a statistical model, Sales = f(TV, radio, newspaper), to understand the relationship between the features and response. This allows one to make sales predictions based on advertising spending and determine which features have the most influence on sales.
This document provides a summary of spatial data modeling and analysis techniques. It begins with an outline of the topics to be covered, including additive statistical models for spatial data, spatial covariance functions, the multivariate normal distribution, kriging for prediction and uncertainty, and the likelihood function for parameter estimation. It then introduces the key concepts and equations for modeling spatial processes as Gaussian random fields with specified covariance functions. Examples are given of commonly used covariance functions and the types of random surfaces they generate. Kriging is described as a best linear unbiased prediction technique that uses a spatial covariance function and observations to make predictions at unknown locations. The document concludes with examples of parameter estimation via maximum likelihood and using the fitted model to make predictions and conditional simulations
The document discusses various types of analyses for association, including correlation analysis, perfect positive and negative linear association, positive and negative linear association, no linear association, nonlinear association, and the Pearson product moment correlation coefficient. It also discusses simple linear regression analysis, including its objectives, data and scatter diagrams, the regression equation, and calculating simple regression. Finally, it discusses correlation from ranks using the Spearman rank-difference method and chi-square tests for independence using expected frequencies and a test statistic.
This document discusses Pearson's Correlation Coefficient (r), which is a measure of the strength and direction of a linear relationship between two variables. It provides an example calculation of r using sample data points. While the example initially shows a strong negative correlation, adding additional non-linear data points results in r equaling 0, indicating no relationship due to the non-linearity between the variables. Pearson's r only describes linear relationships.
The document discusses assessing the adequacy of phylogenetic trait models by comparing observed trait data to simulated trait data generated under the fitted model. It proposes building a "unit tree" by rescaling branch lengths based on the expected covariance structure implied by the model, so that contrasts on this tree should be independent and normally distributed if the model is adequate. A variety of test statistics can then be computed on the contrasts of the observed data and compared to the distribution of those statistics on simulated contrasts to evaluate the model.
The treatment of large structural systems may be simplified by dividing the system into
smaller systems called components. The components are related through the
displacement, and force conditions at their junction points. Each component is represented
by mode shapes (or functions).
Welcome to International Journal of Engineering Research and Development (IJERD)IJERD Editor
The document presents new 5x5 stiffness matrices for stability and dynamic analyses of line continua. The matrices were derived using energy variational principles and a five-term polynomial shape function. Analyses of four line continua and a portal frame using the new 5x5 matrices found results very close to exact solutions, with average percentage differences of 2.55% for stability and 0.14% for vibration. In contrast, analyses using traditional 4x4 matrices differed greatly from exact results, demonstrating the 5x5 matrices are better suited for stability and dynamic analyses of frame structures.
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
This document discusses edge-neighbor-rupture degree, a measure of vulnerability and reliability in communication networks. It defines edge-neighbor-rupture degree mathematically and provides examples of calculating it for different graphs. The main results are theorems about how edge-neighbor-rupture degree changes under common graph operations like joining graphs, adding vertices (thorny graphs), and taking unions of graphs. Edge-neighbor-rupture degree either increases or stays the same under these operations.
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
Vulnerability and reliability parameters measure the resistance of the network to disruption of operation
after the failure of certain stations or communication links in a communication network. An edge
subversion strategy of a graph , say , is a set of edge(s) in whose adjacent vertices which is incident
with the removal edge(s) are removed from . The survival subgraph is denoted by −
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
Vulnerability and reliability parameters measure the resistance of the network to disruption of operation
after the failure of certain stations or communication links in a communication network.
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
Vulnerability and reliability parameters measure the resistance of the network to disruption of operation
after the failure of certain stations or communication links in a communication network. An edge
subversion strategy of a graph G, say , is a set of edge(s) in G whose adjacent vertices which is incident
with the removal edge(s) are removed from G.
12 x1 t01 03 integrating derivative on function (2013)Nigel Simmons
The document discusses integrating derivatives of functions. It states that the integral of the derivative of a function f(x) is equal to the natural log of f(x) plus a constant. It then provides examples of integrating several derivatives: (i) ∫(1/(7-3x)) dx = -1/3 log(7-3x) + c, (ii) ∫(1/(8x+5)) dx = 1/8 log(8x+5) + c, and (iii) ∫(x5/(x-2)) dx = 1/6 log(x6-2) + c. It also discusses techniques for integrating fractions by polynomial long division and finds
This document describes the modeling and linearization of a cart-double pendulum system. Equations of motion are derived for the cart position xc, and pendulum angles θ1 and θ2 using Lagrangian mechanics. This results in six nonlinear differential equations describing the system state X in terms of xc, θ1, θ2, and their time derivatives. The system is then linearized around the equilibrium point xc=0, θ1=0, θ2=0 to obtain linear state space models with matrices A, B, C, D.
The document is a lesson plan for an algebra course covering functions. It includes definitions of key function concepts like domain, range, and determining if a relation represents a function. Examples are provided to demonstrate calculating the domain and range of functions given their rules of correspondence. The lesson also covers specific types of functions like real functions of real variables and their domains.
The document discusses logarithms and their properties. Logarithms are defined as the inverse of exponentials. If y = ax, then x = loga y. The natural logarithm is log base e, written as ln. Properties of logarithms include: loga m + loga n = loga mn; loga m - loga n = loga(m/n); loga mn = n loga m; loga 1 = 0; loga a = 1. Examples of evaluating logarithmic expressions are provided.
The document describes splay trees, a type of self-adjusting binary search tree. Splay trees differ from other balanced binary search trees in that they do not explicitly rebalance after each insertion or deletion, but instead perform a process called "splaying" in which nodes are rotated to the root. This splaying process helps ensure search, insert, and delete operations take O(log n) amortized time. The document explains splaying operations like zig, zig-zig, and zig-zag that rotate nodes up the tree, and analyzes how these operations affect the tree's balance over time through a concept called the "rank" of the tree.
This document contains 4 math exercises involving matrix operations and linear algebra calculations. Exercise 1 involves multiplying and adding matrices. Exercise 2 calculates determinants and inverses of matrices. Exercise 3 solves systems of linear equations. Exercise 4 shows that the determinant of AB is equal to the determinant of A times the determinant of B.
The Queue Length of a GI M 1 Queue with Set Up Period and Bernoulli Working V...YogeshIJTSRD
Consider a GI M 1 queue with set up period and working vacations. During the working vacation period, customers can be served at a lower rate, if there are customers at a service completion instant, the vacation can be interrupted and the server will come back to a set up period with probability or continue the working vacation with probability , and when the set up period ends, the server will switch to the normal working level. Using the matrix analytic method, we obtain the steady state distributions for the queue length at arrival epochs. Li Tao "The Queue Length of a GI/M/1 Queue with Set-Up Period and Bernoulli Working Vacation Interruption" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43743.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/43743/the-queue-length-of-a-gim1-queue-with-setup-period-and-bernoulli-working-vacation-interruption/li-tao
The document describes Karnaugh maps (K-maps), a technique for minimizing Boolean logic expressions. K-maps provide an alternate representation of truth tables where adjacent squares have a distance of 1. Logical minimization involves grouping adjacent 1s in the K-map without including any 0s to find essential prime implicants, then expressing these groups as product terms summed to obtain a minimum sum-of-products expression. An example minimizes a 2-variable function from 8 gates to 4 gates using a K-map. Don't care conditions can also be utilized for further minimization. The document concludes with a design example of a hexadecimal to 7-segment display decoder circuit minimized with K-maps.
The document discusses several OpenGL functions and concepts related to setting up the coordinate system and rendering 3D objects. It explains how functions like glOrtho, glMatrixMode, glTranslate, glRotate, and glScale are used to apply transformations to the modelview and projection matrices. It also covers setting the viewport and world window. Finally, it provides details on functions like glutSolidSphere and glutWireCube that can be used to render basic 3D shapes.
This document describes the matrix method for analyzing beams with two degrees of freedom per node: vertical displacement and rotation. It presents the process for deriving the global stiffness matrix for a beam element with two nodes and four degrees of freedom. It also explains how to account for loads applied to the beam by calculating the equivalent fixed end forces and moments and including them as nodal loads in the analysis. The solution process involves assembling the global stiffness matrix, applying nodal loads, and solving for the nodal displacements and reactions.
The document defines and discusses random variables. It begins by defining a random variable as a function that assigns a real number to each outcome of a random experiment. It then discusses the conditions for a function to be considered a random variable. The document outlines the key types of random variables as discrete, continuous, and mixed and introduces the cumulative distribution function (CDF) and probability density function (PDF) as ways to describe the distribution of a random variable. It provides examples of CDFs and PDFs for discrete random variables and discusses properties of distribution and density functions. The document also introduces important continuous random variables like the Gaussian random variable.
The document provides an introduction to Gaussian processes. It explains that Gaussian processes allow modeling any function directly and estimating uncertainty for predictions. It demonstrates how two random variables can be jointly distributed as a multivariate Gaussian distribution, and how the conditional distribution of one variable given the other can be derived from the joint distribution. Gaussian processes use these properties to perform nonparametric machine learning by modeling relationships between variables without assuming a specific function form.
This document discusses exponents and surds. It covers exponent or index notation, exponent or index laws, zero and negative indices, standard form, properties of surds, multiplication of surds, and division by surds. Examples are provided to illustrate exponent notation, evaluating exponents, writing numbers as products of prime factors, the laws of exponents, evaluating expressions with negative bases, and using a calculator to evaluate exponents.
This document provides instructions and examples for using the Casio fx-82SX/fx-250HC calculator. It covers basic calculations, constants, memory, fractions, percentages, scientific functions, statistics, and conversions. The examples show how to perform calculations in various modes, use special functions like logarithms and trigonometry, work with fractions and decimals, generate random numbers, and convert between units and coordinate systems.
This document discusses various 3D transformations including translation, rotation, scaling, reflection, and shearing. It provides the transformation matrices for each type of 3D transformation. It also discusses combining multiple transformations through composite transformations by multiplying the matrices in sequence from right to left.
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
This document discusses edge-neighbor-rupture degree, a measure of vulnerability and reliability in communication networks. It defines edge-neighbor-rupture degree mathematically and provides examples of calculating it for different graphs. The main results are theorems about how edge-neighbor-rupture degree changes under common graph operations like joining graphs, adding vertices (thorny graphs), and taking unions of graphs. Edge-neighbor-rupture degree either increases or stays the same under these operations.
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
Vulnerability and reliability parameters measure the resistance of the network to disruption of operation
after the failure of certain stations or communication links in a communication network. An edge
subversion strategy of a graph , say , is a set of edge(s) in whose adjacent vertices which is incident
with the removal edge(s) are removed from . The survival subgraph is denoted by −
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
Vulnerability and reliability parameters measure the resistance of the network to disruption of operation
after the failure of certain stations or communication links in a communication network.
EDGE-NEIGHBOR RUPTURE DEGREE ON GRAPH OPERATIONSmathsjournal
Vulnerability and reliability parameters measure the resistance of the network to disruption of operation
after the failure of certain stations or communication links in a communication network. An edge
subversion strategy of a graph G, say , is a set of edge(s) in G whose adjacent vertices which is incident
with the removal edge(s) are removed from G.
12 x1 t01 03 integrating derivative on function (2013)Nigel Simmons
The document discusses integrating derivatives of functions. It states that the integral of the derivative of a function f(x) is equal to the natural log of f(x) plus a constant. It then provides examples of integrating several derivatives: (i) ∫(1/(7-3x)) dx = -1/3 log(7-3x) + c, (ii) ∫(1/(8x+5)) dx = 1/8 log(8x+5) + c, and (iii) ∫(x5/(x-2)) dx = 1/6 log(x6-2) + c. It also discusses techniques for integrating fractions by polynomial long division and finds
This document describes the modeling and linearization of a cart-double pendulum system. Equations of motion are derived for the cart position xc, and pendulum angles θ1 and θ2 using Lagrangian mechanics. This results in six nonlinear differential equations describing the system state X in terms of xc, θ1, θ2, and their time derivatives. The system is then linearized around the equilibrium point xc=0, θ1=0, θ2=0 to obtain linear state space models with matrices A, B, C, D.
The document is a lesson plan for an algebra course covering functions. It includes definitions of key function concepts like domain, range, and determining if a relation represents a function. Examples are provided to demonstrate calculating the domain and range of functions given their rules of correspondence. The lesson also covers specific types of functions like real functions of real variables and their domains.
The document discusses logarithms and their properties. Logarithms are defined as the inverse of exponentials. If y = ax, then x = loga y. The natural logarithm is log base e, written as ln. Properties of logarithms include: loga m + loga n = loga mn; loga m - loga n = loga(m/n); loga mn = n loga m; loga 1 = 0; loga a = 1. Examples of evaluating logarithmic expressions are provided.
The document describes splay trees, a type of self-adjusting binary search tree. Splay trees differ from other balanced binary search trees in that they do not explicitly rebalance after each insertion or deletion, but instead perform a process called "splaying" in which nodes are rotated to the root. This splaying process helps ensure search, insert, and delete operations take O(log n) amortized time. The document explains splaying operations like zig, zig-zig, and zig-zag that rotate nodes up the tree, and analyzes how these operations affect the tree's balance over time through a concept called the "rank" of the tree.
This document contains 4 math exercises involving matrix operations and linear algebra calculations. Exercise 1 involves multiplying and adding matrices. Exercise 2 calculates determinants and inverses of matrices. Exercise 3 solves systems of linear equations. Exercise 4 shows that the determinant of AB is equal to the determinant of A times the determinant of B.
The Queue Length of a GI M 1 Queue with Set Up Period and Bernoulli Working V...YogeshIJTSRD
Consider a GI M 1 queue with set up period and working vacations. During the working vacation period, customers can be served at a lower rate, if there are customers at a service completion instant, the vacation can be interrupted and the server will come back to a set up period with probability or continue the working vacation with probability , and when the set up period ends, the server will switch to the normal working level. Using the matrix analytic method, we obtain the steady state distributions for the queue length at arrival epochs. Li Tao "The Queue Length of a GI/M/1 Queue with Set-Up Period and Bernoulli Working Vacation Interruption" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-5 , August 2021, URL: https://www.ijtsrd.com/papers/ijtsrd43743.pdf Paper URL: https://www.ijtsrd.com/mathemetics/other/43743/the-queue-length-of-a-gim1-queue-with-setup-period-and-bernoulli-working-vacation-interruption/li-tao
The document describes Karnaugh maps (K-maps), a technique for minimizing Boolean logic expressions. K-maps provide an alternate representation of truth tables where adjacent squares have a distance of 1. Logical minimization involves grouping adjacent 1s in the K-map without including any 0s to find essential prime implicants, then expressing these groups as product terms summed to obtain a minimum sum-of-products expression. An example minimizes a 2-variable function from 8 gates to 4 gates using a K-map. Don't care conditions can also be utilized for further minimization. The document concludes with a design example of a hexadecimal to 7-segment display decoder circuit minimized with K-maps.
The document discusses several OpenGL functions and concepts related to setting up the coordinate system and rendering 3D objects. It explains how functions like glOrtho, glMatrixMode, glTranslate, glRotate, and glScale are used to apply transformations to the modelview and projection matrices. It also covers setting the viewport and world window. Finally, it provides details on functions like glutSolidSphere and glutWireCube that can be used to render basic 3D shapes.
This document describes the matrix method for analyzing beams with two degrees of freedom per node: vertical displacement and rotation. It presents the process for deriving the global stiffness matrix for a beam element with two nodes and four degrees of freedom. It also explains how to account for loads applied to the beam by calculating the equivalent fixed end forces and moments and including them as nodal loads in the analysis. The solution process involves assembling the global stiffness matrix, applying nodal loads, and solving for the nodal displacements and reactions.
The document defines and discusses random variables. It begins by defining a random variable as a function that assigns a real number to each outcome of a random experiment. It then discusses the conditions for a function to be considered a random variable. The document outlines the key types of random variables as discrete, continuous, and mixed and introduces the cumulative distribution function (CDF) and probability density function (PDF) as ways to describe the distribution of a random variable. It provides examples of CDFs and PDFs for discrete random variables and discusses properties of distribution and density functions. The document also introduces important continuous random variables like the Gaussian random variable.
The document provides an introduction to Gaussian processes. It explains that Gaussian processes allow modeling any function directly and estimating uncertainty for predictions. It demonstrates how two random variables can be jointly distributed as a multivariate Gaussian distribution, and how the conditional distribution of one variable given the other can be derived from the joint distribution. Gaussian processes use these properties to perform nonparametric machine learning by modeling relationships between variables without assuming a specific function form.
This document discusses exponents and surds. It covers exponent or index notation, exponent or index laws, zero and negative indices, standard form, properties of surds, multiplication of surds, and division by surds. Examples are provided to illustrate exponent notation, evaluating exponents, writing numbers as products of prime factors, the laws of exponents, evaluating expressions with negative bases, and using a calculator to evaluate exponents.
This document provides instructions and examples for using the Casio fx-82SX/fx-250HC calculator. It covers basic calculations, constants, memory, fractions, percentages, scientific functions, statistics, and conversions. The examples show how to perform calculations in various modes, use special functions like logarithms and trigonometry, work with fractions and decimals, generate random numbers, and convert between units and coordinate systems.
This document discusses various 3D transformations including translation, rotation, scaling, reflection, and shearing. It provides the transformation matrices for each type of 3D transformation. It also discusses combining multiple transformations through composite transformations by multiplying the matrices in sequence from right to left.
Strategies for Effective Upskilling is a presentation by Chinwendu Peace in a Your Skill Boost Masterclass organisation by the Excellence Foundation for South Sudan on 08th and 09th June 2024 from 1 PM to 3 PM on each day.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
How to Add Chatter in the odoo 17 ERP ModuleCeline George
In Odoo, the chatter is like a chat tool that helps you work together on records. You can leave notes and track things, making it easier to talk with your team and partners. Inside chatter, all communication history, activity, and changes will be displayed.
ISO/IEC 27001, ISO/IEC 42001, and GDPR: Best Practices for Implementation and...PECB
Denis is a dynamic and results-driven Chief Information Officer (CIO) with a distinguished career spanning information systems analysis and technical project management. With a proven track record of spearheading the design and delivery of cutting-edge Information Management solutions, he has consistently elevated business operations, streamlined reporting functions, and maximized process efficiency.
Certified as an ISO/IEC 27001: Information Security Management Systems (ISMS) Lead Implementer, Data Protection Officer, and Cyber Risks Analyst, Denis brings a heightened focus on data security, privacy, and cyber resilience to every endeavor.
His expertise extends across a diverse spectrum of reporting, database, and web development applications, underpinned by an exceptional grasp of data storage and virtualization technologies. His proficiency in application testing, database administration, and data cleansing ensures seamless execution of complex projects.
What sets Denis apart is his comprehensive understanding of Business and Systems Analysis technologies, honed through involvement in all phases of the Software Development Lifecycle (SDLC). From meticulous requirements gathering to precise analysis, innovative design, rigorous development, thorough testing, and successful implementation, he has consistently delivered exceptional results.
Throughout his career, he has taken on multifaceted roles, from leading technical project management teams to owning solutions that drive operational excellence. His conscientious and proactive approach is unwavering, whether he is working independently or collaboratively within a team. His ability to connect with colleagues on a personal level underscores his commitment to fostering a harmonious and productive workplace environment.
Date: May 29, 2024
Tags: Information Security, ISO/IEC 27001, ISO/IEC 42001, Artificial Intelligence, GDPR
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it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
This presentation includes basic of PCOS their pathology and treatment and also Ayurveda correlation of PCOS and Ayurvedic line of treatment mentioned in classics.
This slide is special for master students (MIBS & MIFB) in UUM. Also useful for readers who are interested in the topic of contemporary Islamic banking.
How to Setup Warehouse & Location in Odoo 17 InventoryCeline George
In this slide, we'll explore how to set up warehouses and locations in Odoo 17 Inventory. This will help us manage our stock effectively, track inventory levels, and streamline warehouse operations.
A workshop hosted by the South African Journal of Science aimed at postgraduate students and early career researchers with little or no experience in writing and publishing journal articles.
1. STAT 22000 Lecture Slides
Correlation
Yibi Huang
Department of Statistics
University of Chicago
2. Recall when we introduced scatter plots in Chapter 1, we assessed
the strength of the association between two variables by eyeballs.
Weak Association Strong Association
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3. Recall when we introduced scatter plots in Chapter 1, we assessed
the strength of the association between two variables by eyeballs.
Weak Association Strong Association
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when X is
fixed
Large spread of Y Small spread of Y
when X is known when X is known
1
4. Correlation = Correlation Coefficient, r
Correlation r is a numerical measure of the direction and strength
of the linear relationship between two numerical variables.
“r” always lies between −1 and 1; the strength increases as you
move away from 0 to either −1 or 1.
• r > 0: positive association
• r < 0: negative association
• r ≈ 0: very weak linear relationship
• large |r|: strong linear relationship
• r = −1 or r = 1: only when all the data points on the
scatterplot lie exactly along a straight line
−1 0 1
Neg. Assoc. Pos. Assoc.
Strong Weak No Assoc Weak Strong
Perfect Perfect 2
7. Formula for Computing the Correlation Coefficient “r”
(x1, y1)
(x2, y2)
(x3, y3)
.
.
.
(xn, yn)
The correlation coefficient r
(or simply, correlation) is defined as:
r =
1
n − 1
n
X
i=1
xi − x̄
sx
!
| {z }
z-score of xi
yi − ȳ
sy
!
| {z }
z-score of yi
.
where sx and sy are respectively the sample SD of X
and of Y.
Usually, we find the correlation using softwares
rather than by manual computation.
5
8. Why r Measures the Strength of a Linear Relationship?
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−3 −2 −1 0 1 2 3
−3
−2
−1
0
1
2
3
x
y
+
−
+ −
x
y
xi −
− x >
> 0
xi −
− x <
< 0
xi −
− x >
> 0
xi −
− x <
< 0
yi −
− y >
> 0
yi −
− y <
< 0
yi −
− y >
> 0
yi −
− y <
< 0
What is the sign of
xi − x̄
sx
!
yi − ȳ
sy
!
??
Here r > 0;
more positive
contributions than
negative.
What kind of points
have large
contributions to the
correlation?
6
9. Correlation r Has No Unit
r =
1
n − 1
n
X
i=1
xi − x̄
sx
!
| {z }
z-score of xi
yi − ȳ
sy
!
| {z }
z-score of yi
.
After standardization, the z-score of neither xi nor yi has a unit.
• So r is unit-free.
• So we can compare r between data sets, where variables are
measured in different units or when variables are different.
E.g. we may compare the
r between [swim time and pulse],
with the
r between [swim time and breathing rate].
7
10. Correlation r Has No Unit (2)
Changing the units of variables does not change the correlation
coefficient r, because we get rid of all the units when we
standardize them (get z-scores).
E.g., no matter the temperatures are recorded in ◦F, or ◦C, the
correlations obtained are equal because
C =
5
9
(F − 32).
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25 30 35 40 45 50 55
45
55
65
75
HIGH
Daily low (F)
Daily
high
(F)
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−5 0 5 10
10
15
20
25
HIGH
Daily low (C)
Daily
high
(C)
8
11. “r” Does Not Distinguish x & y
Sometimes one use the X variable to predict the Y variable. In this
case, X is called the explanatory variable, and Y the response.
The correlation coefficient r does not distinguish between the two.
It treats x and y symmetrically.
r =
1
n − 1
n
X
i=1
xi − x̄
sx
!
yi − ȳ
sy
!
Swapping the x-, y-axes doesn’t change r (both r = 0.74.)
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20 30 40 50
50
60
70
80
Daily low (F)
Daily
high
(F)
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50 60 70 80
20
30
40
50
Daily high (F)
Daily
low
(F)
9
12. Correlation r Describes Linear Relationships Only
The scatter plot below shows a perfect nonlinear association. All
points fall on the quadratic curve y = 1 − 4(x − 0.5)2.
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0.0 0.4 0.8
0.0
0.4
0.8
1.2
x
y
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r of all black dots = 0.803,
r of all dots = −0.019.
(black + white)
No matter how strong the association,
the r of a curved relationship is NEVER 1 or −1.
It can even be 0, like the plot above. 10
13. Correlation Is VERY Sensitive to Outliers
Sometimes a single outlier can change r drastically.
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For the plot on the left,
r =
0.0031 with the outlier
0.6895 without the outlier
Outliers that may remarkably change the form of associations
when removed are called influential points.
Remark: Not all outliers are influential points.
11
14. When Data Points Are Clustered ...
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In the plot above, each of the two
clusters exhibits a weak negative
association (r = −0.336 and −0.323).
But the whole diagram shows a
moderately strong positive association
(r = 0.849).
• This is an example of the Simpson’s paradox.
• An overall r can be misleading when data points are clustered.
• Cluster-wise r’s should be reported as well.
12
15. Always Check the Scatter Plots (1)
The 4 data sets below have identical x, y, sx, sy, and r.
Dataset 1 Dataset 2 Dataset 3 Dataset 4
x y x y x y x y
10 8.04 10 9.14 10 7.46 8 6.58
8 6.96 8 8.14 8 6.77 8 5.76
13 7.58 13 8.75 13 12.76 8 7.71
9 8.81 9 8.77 9 7.11 8 8.84
11 8.33 11 9.26 11 7.81 8 8.47
14 9.96 14 8.10 14 8.84 8 7.04
6 7.24 6 6.13 6 6.08 8 5.25
4 4.26 4 3.10 4 5.36 19 12.50
12 10.84 12 9.13 12 8.15 8 5.56
7 4.82 7 7.26 7 6.42 8 7.91
5 5.68 5 4.74 5 5.73 8 6.89
Ave 9 7.5 9 7.5 9 7.5 9 7.5
SD 3.16 1.94 3.16 1.94 3.16 1.94 3.16 1.94
r 0.82 0.82 0.82 0.82 13
16. Always Check the Scatter Plots (2)
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0 5 10 15 20
0
5
10
15
Dataset 1
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0 5 10 15 20
0
5
10
15
Dataset 2
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0 5 10 15 20
0
5
10
15
Dataset 3
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0 5 10 15 20
0
5
10
15
Dataset 4
• In Dataset 2, y can be predicted exactly from x. But r < 1,
because r only measures linear association.
• In Dataset 3, r would be 1 instead of 0.82 if the outlier were
actually on the line.
The correlation coefficient can be misleading in the presence of
outliers, multiple clusters, or nonlinear association.
14
18. Questions
• Why do both variables have to be numerical when computing
their correlation coefficient?
• If the law requires women to marry only men 2 years older
than themselves, what is the correlation of the ages between
husbands and wives?
16