1. The document contains mathematical formulas and calculations involving sets and their values.
2. An example calculation finds the marginal value of adding an element to a set.
3. The document also discusses permutations and combinations in calculating marginal values for different set arrangements.
1) The document presents definitions and formulas for the Shapley value and power indices used to measure influence in cooperative games.
2) The Shapley value ψi(v) is defined as the average over all orderings of players of marginal contributions of player i.
3) The Banzhaf power index ρi(v) is defined as the probability that player i is pivotal in a randomly selected coalition.
This document defines a set function (N, v) that assigns values to subsets of a set N. It proves several properties of such set functions, including:
1) The value of the entire set N is greater than the sum of individual element values
2) Translating a set function (N, v) to a new function (N, v') by scaling and shifting preserves properties like submodularity
3) A vector x dominates another y with respect to a set S if each element of x is greater than the corresponding element of y for all elements in S.
4) Translating vectors preserves dominance relationships defined by the original set function.
This document discusses submodular functions and the Shapley value. It begins by defining submodular functions and their properties. It then introduces the Shapley value as a way to assign importance values to elements in a cooperative game based on their marginal contributions. The Shapley value is defined as the average marginal contribution over all possible orderings of the elements. The document provides formulas for computing the Shapley value and discusses its properties.
The document defines a function ψ that maps vectors v in a vector space V to Rn. It provides the definition of ψi(v) for any i in N. It then generalizes this definition to cvR where c is a positive real number and R is a non-empty subset of N. The document concludes by stating properties that ψ must satisfy to be a valid representation.
The document presents a method for determining Shapley values (φi) for elements in a set based on their marginal contributions. It defines the Shapley value formula and calculates the Shapley values for a set of 3 elements where different coalitions are assigned values. The Shapley values are calculated to be φ1=130/6, φ2=250/6, φ3=220/6. The sums of the Shapley values equals the total value of the grand coalition, demonstrating it is a valid allocation.
The document discusses skew products of directed graphs. Specifically, it defines skew products as taking a directed graph E and group G, along with a function f from edges of E to G. The skew product graph E ×f G has vertices that are pairs of vertices in E and elements of G, and edges that are pairs of edges in E and elements of G where the range and source of the edge are modified by the group element associated to the edge by f. Several examples of skew products are given to illustrate this construction. The document also discusses inverse semigroups associated with directed graphs.
The document defines a set of vector functions v_R over subsets of a set N. It defines v_R for various subsets R of N, such as v_{1}, v_{2}, v_{1,2} etc. It also defines a mapping ψ from the vector functions to R^3. The vector functions are used to represent subsets of N.
1. The document contains mathematical formulas and calculations involving sets and their values.
2. An example calculation finds the marginal value of adding an element to a set.
3. The document also discusses permutations and combinations in calculating marginal values for different set arrangements.
1) The document presents definitions and formulas for the Shapley value and power indices used to measure influence in cooperative games.
2) The Shapley value ψi(v) is defined as the average over all orderings of players of marginal contributions of player i.
3) The Banzhaf power index ρi(v) is defined as the probability that player i is pivotal in a randomly selected coalition.
This document defines a set function (N, v) that assigns values to subsets of a set N. It proves several properties of such set functions, including:
1) The value of the entire set N is greater than the sum of individual element values
2) Translating a set function (N, v) to a new function (N, v') by scaling and shifting preserves properties like submodularity
3) A vector x dominates another y with respect to a set S if each element of x is greater than the corresponding element of y for all elements in S.
4) Translating vectors preserves dominance relationships defined by the original set function.
This document discusses submodular functions and the Shapley value. It begins by defining submodular functions and their properties. It then introduces the Shapley value as a way to assign importance values to elements in a cooperative game based on their marginal contributions. The Shapley value is defined as the average marginal contribution over all possible orderings of the elements. The document provides formulas for computing the Shapley value and discusses its properties.
The document defines a function ψ that maps vectors v in a vector space V to Rn. It provides the definition of ψi(v) for any i in N. It then generalizes this definition to cvR where c is a positive real number and R is a non-empty subset of N. The document concludes by stating properties that ψ must satisfy to be a valid representation.
The document presents a method for determining Shapley values (φi) for elements in a set based on their marginal contributions. It defines the Shapley value formula and calculates the Shapley values for a set of 3 elements where different coalitions are assigned values. The Shapley values are calculated to be φ1=130/6, φ2=250/6, φ3=220/6. The sums of the Shapley values equals the total value of the grand coalition, demonstrating it is a valid allocation.
The document discusses skew products of directed graphs. Specifically, it defines skew products as taking a directed graph E and group G, along with a function f from edges of E to G. The skew product graph E ×f G has vertices that are pairs of vertices in E and elements of G, and edges that are pairs of edges in E and elements of G where the range and source of the edge are modified by the group element associated to the edge by f. Several examples of skew products are given to illustrate this construction. The document also discusses inverse semigroups associated with directed graphs.
The document defines a set of vector functions v_R over subsets of a set N. It defines v_R for various subsets R of N, such as v_{1}, v_{2}, v_{1,2} etc. It also defines a mapping ψ from the vector functions to R^3. The vector functions are used to represent subsets of N.
This document defines and describes concepts related to fuzzy graphs and fuzzy digraphs. Key points include:
- A fuzzy graph is defined by two functions that assign membership values to vertices and edges.
- A fuzzy subgraph has lower or equal membership values for vertices and edges compared to the original graph.
- Effective edges and effective paths only include edges/paths where the membership equals the minimum vertex membership.
- Various graph measures are generalized to fuzzy graphs, such as vertex degree, order, size, and domination number.
- Fuzzy digraphs are defined similarly but with directed edges. Concepts like paths, independence, and domination are extended to fuzzy digraphs.
The document discusses the marginal value theorem and its properties. It begins by introducing the theorem and defining relevant terms. It then proves three key properties:
1) The marginal values are always nonnegative.
2) The sum of the marginal values equals the total value.
3) The marginal value of adding an element to a set is greater than or equal to the value of that element alone.
The document describes an optimization problem and its solution. It defines a set N, weights wi for each element i in N, and utility functions ui for each i. It defines a function v(S) that finds the maximum utility for a subset S of N, subject to capacity constraints based on the weights. It then describes the optimal solution (p*,{x*i}) that maximizes total utility minus costs, with x*i defined for each i.
1. The document describes a set of vectors (v{1}, v{2}, ..., v{1,2,3}) that represent coalitions over a set of agents N={1,2,3}. Each vector assigns a value (0 or 1) to each possible subset of N.
2. The document then defines a function ψ that maps each vector onto an imputation, which divides the total value among the agents. It provides the specific imputations corresponding to each coalition vector.
3. The document explains that any coalition structure can be represented as a linear combination of the base coalition vectors, where the coefficients are the values assigned to each coalition. It gives an example of one such coalition structure
The document defines Shapley value, a concept from game theory used to assign a value to each player in a cooperative game. It provides examples to illustrate how to calculate Shapley value based on the marginal contribution of a player to each possible order of entry into the game. Specifically, it shows examples with 3 players where the value of different coalitions is given and calculates the Shapley value for each player based on these values and all possible orderings of the players.
The document contains mathematical formulas and calculations related to submodular functions and the greedy algorithm. Specifically, it provides 3 definitions of submodular functions, discusses applying the greedy algorithm to maximize a submodular set function, and gives an example calculation with a set of 3 elements.
1) The document describes concepts and algorithms related to cooperative game theory and stable matchings. It presents definitions for cooperative games, the core of a game, and the stable matching problem.
2) Methods are introduced for finding solutions in the core for cooperative games and stable matchings for the stable matching problem using algorithms that assign values and make comparisons between players/options.
3) The document provides mathematical notation and context to formally define key concepts like cooperative games, the core, stable matchings, and algorithms for finding stable solutions.
The document presents information about submodular functions including:
1) It defines a submodular function v as a set function whose domain is the power set of a ground set N, and discusses properties of submodular functions.
2) It provides an example of a submodular function v with ground set N={1,2,3} and defines the polyhedron and base polyhedron associated with v.
3) It introduces the concept of a greedy algorithm for maximizing a submodular set function and outlines the steps of the greedy algorithm.
1. The document discusses using Z-transforms to solve linear difference equations with constant coefficients.
2. It provides the working procedure which involves taking the Z-transform of both sides of the difference equation, rearranging to isolate the Z-transform of the unknown function U(z), and then taking the inverse Z-transform to find the solution in terms of n.
3. As an example, it shows the step-by-step solution of the difference equation un+2 - 2un+1 + un = 3n + 5 using this method. The solution is found to be 1/2n(n-1)(n+3) + c0 + (c1-c0)n
El documento presenta las definiciones y propiedades básicas de los números enteros (Z), incluyendo la adición, sustracción, multiplicación y división. Explica que los enteros incluyen los números naturales y sus opuestos, y define operaciones como la suma, resta, producto y cociente de números enteros. También establece teoremas clave sobre las propiedades conmutativas, asociativas y distributivas de las operaciones con enteros.
The document discusses the composition of linear transformations. It defines the composition of two linear transformations T1 and T2 as the transformation T2 o T1, which maps an element X in the domain of T1 to the result of applying T2 to the output of T1. The key points made are:
1) The composition T2 o T1 is a linear transformation.
2) The matrix associated with the composition T2 o T1 is the product of the matrices associated with T1 and T2.
3) Examples are provided to illustrate finding the matrix of a composition and applying a composition to vectors and subspaces.
This document discusses theorems related to linear transformations between finite-dimensional vector spaces. It proves two main theorems:
1) A linear transformation T is invertible if and only if T maps a basis of the domain space V to a basis of the codomain space W.
2) A linear transformation T between vector spaces of equal dimension is invertible, injective, surjective, and maps bases to bases. These properties are shown to be equivalent.
The document provides a detailed proof of each theorem with examples to illustrate the concepts. It discusses key ideas such as linear independence, spanning sets, and the relationship between invertibility, injectivity and surjectivity of linear transformations.
This document discusses two theorems: Gauss divergence theorem and Stokes theorem. It provides an example problem to verify each theorem. For Gauss divergence theorem, it calculates the surface integral of a vector function over the surfaces of a rectangle parallelepiped and shows it equals the volume integral of the divergence of the function over the volume, verifying the theorem. For Stokes theorem, it similarly calculates line and surface integrals of a vector function over a rectangular region to verify the theorem holds.
This document provides information about an optimization problem. It defines a set function v over subsets of a ground set N, and a constraint set C(v) consisting of vectors x satisfying certain conditions defined in terms of v. It then formulates the optimization problem of minimizing the maximum imbalance e(S,x) over all S subsets of N and x in C(v). It provides an example instance of the problem with N={1,2,3} and defines v on subsets. It then derives properties of the optimal solution for this example instance.
1) The document discusses various geometric concepts in multi-variable calculus including the Cartesian plane R2, distance between points, midpoint of a line segment, circles, parabolas, ellipses, and hyperbolas.
2) It provides examples of solving problems related to these concepts, such as proving points are collinear, finding midpoints of diagonals of a quadrilateral, and graphing various equations.
3) The document concludes by listing two references used in teaching these multi-variable calculus topics.
1. The document compares the key properties of parabolas, ellipses, and hyperbolas. It provides equations and definitions for characteristics like eccentricity, foci, vertices, axes, directrix, and more.
2. The properties discussed include the standard and parametric forms of the equations, locations of foci and vertices, equations of axes and directrix, and formulas for lengths like latus rectum.
3. Methods for finding equations of tangents, normals, chords, and bisectors are also outlined, along with formulas for lengths of intercepts and areas of triangles formed by tangents.
La manera ideal de enseñar computación cuántica sería construir entre todos un ordenador cuántico. Dado que eso está fuera de nuestro alcance, intentaremos hacer lo segundo mejor: construir entre todos un simulador clásico de un ordenador cuántico. Es decir, un programa breve, que corra en un ordenador portátil y nos permita simular el comportamiento que creemos que tendrán los ordenadores cuánticos reales, cuando sean construidos. Utilizaremos el enfoque más prometedor en la actualidad, la computación cuántica adiabática (AQC), empleada, entre muchos otros, por D-Wave.
En el seminario no asumiremos ningún conocimiento de mecánica cuántica, tan sólo conocimientos moderados de programación y de álgebra lineal. Tras la charla habilitaremos una página web de la que descargar el código descrito durante la misma.
This document discusses the matrix associated with a linear transformation. It defines key concepts such as:
1) The matrix associated with a linear transformation T between vector spaces V and W depends on the chosen bases [v] of V and [w] of W.
2) The entries of the matrix are the coefficients that relate the images of the basis vectors of [v] under T in terms of the basis vectors of [w].
3) The matrix allows representing the transformation T of any vector in V in terms of a matrix multiplication between the vector's coordinates in [v] and the transformation matrix.
The document describes concepts and formulas related to cooperative game theory. It defines sets N and A(v) and the function v that assigns values to subsets of N. It introduces concepts like the excess e(S,x) of a coalition S under allocation x, and the stability threshold function θ(x). Formulas are provided for calculating sij(x), the maximum excess obtained by player i when deviating from x by joining player j. The document also describes how to construct an allocation y that is -better than x for players i and j.
1) The document defines a cooperative game model and solution concepts for allocating value to players. It provides formal definitions for the characteristic function v(S) representing the worth of coalition S, and the Shapley value sij(x) comparing players i and j.
2) An example cooperative game is presented with 3 players and characteristic function values defined for various coalitions. The set of feasible allocations C(v) is specified.
3) For the example game, expressions are derived for the Shapley values sij(x) comparing each pair of players over allocations in C(v).
The document discusses submodular functions and presents examples to test properties of submodular functions. It examines several set functions (v) and defines an associated set function (v') to test whether the submodular inequality is satisfied. Some examples satisfy the inequality while others do not, demonstrating the boundary conditions of a submodular function.
https://www.youtube.com/channel/UCEr2VZ6DI4gATRtVJw49ZFw
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Please join my community channel.
Learn about GameTheory and related topics.
This document defines and describes concepts related to fuzzy graphs and fuzzy digraphs. Key points include:
- A fuzzy graph is defined by two functions that assign membership values to vertices and edges.
- A fuzzy subgraph has lower or equal membership values for vertices and edges compared to the original graph.
- Effective edges and effective paths only include edges/paths where the membership equals the minimum vertex membership.
- Various graph measures are generalized to fuzzy graphs, such as vertex degree, order, size, and domination number.
- Fuzzy digraphs are defined similarly but with directed edges. Concepts like paths, independence, and domination are extended to fuzzy digraphs.
The document discusses the marginal value theorem and its properties. It begins by introducing the theorem and defining relevant terms. It then proves three key properties:
1) The marginal values are always nonnegative.
2) The sum of the marginal values equals the total value.
3) The marginal value of adding an element to a set is greater than or equal to the value of that element alone.
The document describes an optimization problem and its solution. It defines a set N, weights wi for each element i in N, and utility functions ui for each i. It defines a function v(S) that finds the maximum utility for a subset S of N, subject to capacity constraints based on the weights. It then describes the optimal solution (p*,{x*i}) that maximizes total utility minus costs, with x*i defined for each i.
1. The document describes a set of vectors (v{1}, v{2}, ..., v{1,2,3}) that represent coalitions over a set of agents N={1,2,3}. Each vector assigns a value (0 or 1) to each possible subset of N.
2. The document then defines a function ψ that maps each vector onto an imputation, which divides the total value among the agents. It provides the specific imputations corresponding to each coalition vector.
3. The document explains that any coalition structure can be represented as a linear combination of the base coalition vectors, where the coefficients are the values assigned to each coalition. It gives an example of one such coalition structure
The document defines Shapley value, a concept from game theory used to assign a value to each player in a cooperative game. It provides examples to illustrate how to calculate Shapley value based on the marginal contribution of a player to each possible order of entry into the game. Specifically, it shows examples with 3 players where the value of different coalitions is given and calculates the Shapley value for each player based on these values and all possible orderings of the players.
The document contains mathematical formulas and calculations related to submodular functions and the greedy algorithm. Specifically, it provides 3 definitions of submodular functions, discusses applying the greedy algorithm to maximize a submodular set function, and gives an example calculation with a set of 3 elements.
1) The document describes concepts and algorithms related to cooperative game theory and stable matchings. It presents definitions for cooperative games, the core of a game, and the stable matching problem.
2) Methods are introduced for finding solutions in the core for cooperative games and stable matchings for the stable matching problem using algorithms that assign values and make comparisons between players/options.
3) The document provides mathematical notation and context to formally define key concepts like cooperative games, the core, stable matchings, and algorithms for finding stable solutions.
The document presents information about submodular functions including:
1) It defines a submodular function v as a set function whose domain is the power set of a ground set N, and discusses properties of submodular functions.
2) It provides an example of a submodular function v with ground set N={1,2,3} and defines the polyhedron and base polyhedron associated with v.
3) It introduces the concept of a greedy algorithm for maximizing a submodular set function and outlines the steps of the greedy algorithm.
1. The document discusses using Z-transforms to solve linear difference equations with constant coefficients.
2. It provides the working procedure which involves taking the Z-transform of both sides of the difference equation, rearranging to isolate the Z-transform of the unknown function U(z), and then taking the inverse Z-transform to find the solution in terms of n.
3. As an example, it shows the step-by-step solution of the difference equation un+2 - 2un+1 + un = 3n + 5 using this method. The solution is found to be 1/2n(n-1)(n+3) + c0 + (c1-c0)n
El documento presenta las definiciones y propiedades básicas de los números enteros (Z), incluyendo la adición, sustracción, multiplicación y división. Explica que los enteros incluyen los números naturales y sus opuestos, y define operaciones como la suma, resta, producto y cociente de números enteros. También establece teoremas clave sobre las propiedades conmutativas, asociativas y distributivas de las operaciones con enteros.
The document discusses the composition of linear transformations. It defines the composition of two linear transformations T1 and T2 as the transformation T2 o T1, which maps an element X in the domain of T1 to the result of applying T2 to the output of T1. The key points made are:
1) The composition T2 o T1 is a linear transformation.
2) The matrix associated with the composition T2 o T1 is the product of the matrices associated with T1 and T2.
3) Examples are provided to illustrate finding the matrix of a composition and applying a composition to vectors and subspaces.
This document discusses theorems related to linear transformations between finite-dimensional vector spaces. It proves two main theorems:
1) A linear transformation T is invertible if and only if T maps a basis of the domain space V to a basis of the codomain space W.
2) A linear transformation T between vector spaces of equal dimension is invertible, injective, surjective, and maps bases to bases. These properties are shown to be equivalent.
The document provides a detailed proof of each theorem with examples to illustrate the concepts. It discusses key ideas such as linear independence, spanning sets, and the relationship between invertibility, injectivity and surjectivity of linear transformations.
This document discusses two theorems: Gauss divergence theorem and Stokes theorem. It provides an example problem to verify each theorem. For Gauss divergence theorem, it calculates the surface integral of a vector function over the surfaces of a rectangle parallelepiped and shows it equals the volume integral of the divergence of the function over the volume, verifying the theorem. For Stokes theorem, it similarly calculates line and surface integrals of a vector function over a rectangular region to verify the theorem holds.
This document provides information about an optimization problem. It defines a set function v over subsets of a ground set N, and a constraint set C(v) consisting of vectors x satisfying certain conditions defined in terms of v. It then formulates the optimization problem of minimizing the maximum imbalance e(S,x) over all S subsets of N and x in C(v). It provides an example instance of the problem with N={1,2,3} and defines v on subsets. It then derives properties of the optimal solution for this example instance.
1) The document discusses various geometric concepts in multi-variable calculus including the Cartesian plane R2, distance between points, midpoint of a line segment, circles, parabolas, ellipses, and hyperbolas.
2) It provides examples of solving problems related to these concepts, such as proving points are collinear, finding midpoints of diagonals of a quadrilateral, and graphing various equations.
3) The document concludes by listing two references used in teaching these multi-variable calculus topics.
1. The document compares the key properties of parabolas, ellipses, and hyperbolas. It provides equations and definitions for characteristics like eccentricity, foci, vertices, axes, directrix, and more.
2. The properties discussed include the standard and parametric forms of the equations, locations of foci and vertices, equations of axes and directrix, and formulas for lengths like latus rectum.
3. Methods for finding equations of tangents, normals, chords, and bisectors are also outlined, along with formulas for lengths of intercepts and areas of triangles formed by tangents.
La manera ideal de enseñar computación cuántica sería construir entre todos un ordenador cuántico. Dado que eso está fuera de nuestro alcance, intentaremos hacer lo segundo mejor: construir entre todos un simulador clásico de un ordenador cuántico. Es decir, un programa breve, que corra en un ordenador portátil y nos permita simular el comportamiento que creemos que tendrán los ordenadores cuánticos reales, cuando sean construidos. Utilizaremos el enfoque más prometedor en la actualidad, la computación cuántica adiabática (AQC), empleada, entre muchos otros, por D-Wave.
En el seminario no asumiremos ningún conocimiento de mecánica cuántica, tan sólo conocimientos moderados de programación y de álgebra lineal. Tras la charla habilitaremos una página web de la que descargar el código descrito durante la misma.
This document discusses the matrix associated with a linear transformation. It defines key concepts such as:
1) The matrix associated with a linear transformation T between vector spaces V and W depends on the chosen bases [v] of V and [w] of W.
2) The entries of the matrix are the coefficients that relate the images of the basis vectors of [v] under T in terms of the basis vectors of [w].
3) The matrix allows representing the transformation T of any vector in V in terms of a matrix multiplication between the vector's coordinates in [v] and the transformation matrix.
The document describes concepts and formulas related to cooperative game theory. It defines sets N and A(v) and the function v that assigns values to subsets of N. It introduces concepts like the excess e(S,x) of a coalition S under allocation x, and the stability threshold function θ(x). Formulas are provided for calculating sij(x), the maximum excess obtained by player i when deviating from x by joining player j. The document also describes how to construct an allocation y that is -better than x for players i and j.
1) The document defines a cooperative game model and solution concepts for allocating value to players. It provides formal definitions for the characteristic function v(S) representing the worth of coalition S, and the Shapley value sij(x) comparing players i and j.
2) An example cooperative game is presented with 3 players and characteristic function values defined for various coalitions. The set of feasible allocations C(v) is specified.
3) For the example game, expressions are derived for the Shapley values sij(x) comparing each pair of players over allocations in C(v).
The document discusses submodular functions and presents examples to test properties of submodular functions. It examines several set functions (v) and defines an associated set function (v') to test whether the submodular inequality is satisfied. Some examples satisfy the inequality while others do not, demonstrating the boundary conditions of a submodular function.
https://www.youtube.com/channel/UCEr2VZ6DI4gATRtVJw49ZFw
Thank you for watching my slide!
Please join my community channel.
Learn about GameTheory and related topics.
The document describes a method for summarizing the essential information of a document in 3 sentences or less. It begins by providing definitions for key terms used in the method such as sets, functions, and ordering relationships. It then provides an example application of the method to a specific problem instance, calculating an ordering relationship over subsets of a set based on a given valuation function.
The document defines a set function v over subsets of a set N={1,2,3} that assigns values to singleton and composite sets. It then defines a vector x=(x1,x2,x3) and constrains the values of x to be consistent with v through linear inequalities and equations. It solves for the values of x1, x2, x3 that satisfy all the constraints, finding the solution x1=20, x2=60, x3=50. The document also discusses some properties and definitions related to set functions.
The document discusses three linear programming problems to minimize the sum of variables x1, x2, and x3 subject to different constraints on the values of subsets. The first problem has the constraints v({1,2})=2, v({1,3})=3, v({2,3})=2 and has an optimal value of 7/2. The second problem changes the constraint v({1,3})=4 and has an optimal value of 4. The third problem changes the constraint v({1,2})=3 and has an optimal value of 9/2.
The document defines a set N={1,2,3} and a set function v that assigns values to subsets of N. It defines the core C(v) of v as the set of vectors that dominate all other vectors pointwise. The core is characterized as the set of vectors x in the affine space A(v) such that for all subsets S of N excluding the empty set, the sum of the components of x indexed by S is greater than or equal to v(S).
The document defines an ordering ≫ on the set A(v) based on the values of θ1(x), θ2(x),...,θ2n-4(x). For any x,y in A(v), x ≫ y if and only if there exists 1 ≤ k ≤ 2n-4 such that θl(x) = θl(y) for l = 1,...,k-1 and θk(x) < θk(y). It also defines the sets C(v) and properties of the functions v(S) and θl(x).
Mpc 006 - 02-01 product moment coefficient of correlationVasant Kothari
1.2 Correlation: Meaning and Interpretation
1.2.1 Scatter Diagram: Graphical Presentation of Relationship
1.2.2 Correlation: Linear and Non-Linear Relationship
1.2.3 Direction of Correlation: Positive and Negative
1.2.4 Correlation: The Strength of Relationship
1.2.5 Measurements of Correlation
1.2.6 Correlation and Causality
1.3 Pearson’s Product Moment Coefficient of Correlation
1.3.1 Variance and Covariance: Building Blocks of Correlations
1.3.2 Equations for Pearson’s Product Moment Coefficient of Correlation
1.3.3 Numerical Example
1.3.4 Significance Testing of Pearson’s Correlation Coefficient
1.3.5 Adjusted r
1.3.6 Assumptions for Significance Testing
1.3.7 Ramifications in the Interpretation of Pearson’s r
1.3.8 Restricted Range
1.4 Unreliability of Measurement
1.4.1 Outliers
1.4.2 Curvilinearity
1.5 Using Raw Score Method for Calculating r
1.5.1 Formulas for Raw Score
1.5.2 Solved Numerical for Raw Score Formula
The document defines a set function v and the concept of a choice set C(v). It then provides an example where v assigns values to subsets of {1,2,3}, and shows that the corresponding choice set C(v) is empty, as there is no vector x that satisfies all the constraints imposed by v. Finally, it notes that because C(v) is empty, there does not exist a vector that is a feasible choice for the given set function v.
The document provides solutions to recommended problems from a signals and systems textbook. It solves problems related to signal properties such as periodicity, even and odd signals, transformations of signals, and convolutions. Key steps and reasoning are shown for each part of each problem. Graphs and diagrams are included to illustrate signals and solutions.
This document presents two theorems that solve new types of nonlinear discrete inequalities involving the maximum of an unknown function over a past time interval. These inequalities are discrete generalizations of Bihari's inequality and can be used to study qualitative properties of solutions to nonlinear difference equations with maxima. Theorem 1 provides an upper bound for the solution in terms of inverse functions, while Theorem 2 uses a simpler integral function but a more complicated upper bound. The inequalities are applied to obtain bounds on solutions to difference equations with maxima.
1. The document presents an optimization problem for finding a minimum value M given a set function v defined on subsets of a ground set N. The objective is to minimize M subject to several inequality constraints involving M and a vector x.
2. An example is given with N={1,2,3}, v defined on the power set of N, and the goal is to find a minimum value of M and a corresponding vector x = (x1, x2, x3) that satisfies the constraints.
3. The constraints define upper and lower bounds on x1, x2, x3 involving M, and their sum must equal v(N) while remaining non-negative. Analysis shows the minimum
The document defines a model for coalition formation with three players (1, 2, 3). It specifies the values of different coalitions and defines the feasible set and payoff functions. It then calculates the payoff functions sij(x) for each pair of players i,j in terms of the values of the different coalitions and the feasible set constraints.
The document describes an optimization problem to allocate resources among a set of items in a way that minimizes excess amounts above predefined valuations for different combinations of items. The problem is formulated as linear constraints with a variable M representing the maximum excess. Solutions are obtained by solving the linear program for different valuations scenarios. The optimal allocation balances minimizing M while satisfying all constraints.
1) The document defines a set N and a valuation function v that assigns values to subsets of N. It defines an allocation as a vector x in the set A(v).
2) It introduces several notation for different concepts: e(S,x) as the excess of set S under allocation x, Tij as the sets relevant to compare i and j, sij(x) as the strength of preference of i over j under x.
3) It provides an example where N={1,2,3}, v assigns a value of 10 to some pairs but 0 to others, and the core allocation C(v) consists of the vector (10,0,0).
The document describes an algorithm for solving maximum weight independent set problems on graphs. It defines several notations and concepts used in the algorithm, including the graph (N, v), the set of feasible solutions A(v), and the optimal solution set C(v). It then provides an example graph with nodes 1, 2, 3 and sets constraints on the node weights and optimal solution. The algorithm works by starting from a feasible but non-optimal solution x, and iteratively finding neighboring solutions (y, S) and (z, T) that improve on x according to the problem constraints until an optimal solution is reached.
This document discusses different interpolation methods:
- Interpolation finds values of a function between known x-values where the function values are given.
- Newton's forward and backward interpolation formulas are presented along with examples.
- Newton's divided difference interpolation uses a formula involving differences to find interpolating polynomials.
- Langrange's interpolation formula expresses the interpolating polynomial as a linear combination of basis polynomials defined in terms of the x-values. An example computing an interpolated value is shown.
1. The document defines various functions and relations using set-builder and function notation.
2. Examples of linear, quadratic, and polynomial functions are provided with their domain and range restrictions.
3. Common transformations of basic quadratic functions like y=x^2 are demonstrated, such as shifting the graph left or right and changing the sign of coefficients.
1. The document discusses vector optimization problems and presents definitions and concepts related to nondominated solutions.
2. It introduces the concept of θ-ordering between solutions and defines what it means for one solution to be better than another based on their θ-ordering.
3. Formulas and properties are presented for calculating the θ-value of solutions based on the objective function values.
1. The document presents mathematical formulas and analysis involving probability distributions with parameters μ and ν.
2. Constraints on μ and ν are derived such that μ is less than 1/2 but greater than 1/3, while ν can be either less than or greater than 1/2.
3. The analysis examines optimal probability distributions for strategies in a game theoretical setting involving players with outcomes ma, mb, and parameters μ and ν.
1. The document discusses two organizations: the India-UN organization and the India-UN Society.
2. It provides details on their goals and activities, which include promoting cooperation between India and the UN, and education on global issues.
3. It also mentions the roles of the India-UN Society in collaborating with the India-UN organization and universities.
How to Manage Your Lost Opportunities in Odoo 17 CRMCeline George
Odoo 17 CRM allows us to track why we lose sales opportunities with "Lost Reasons." This helps analyze our sales process and identify areas for improvement. Here's how to configure lost reasons in Odoo 17 CRM
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.
বাংলাদেশের অর্থনৈতিক সমীক্ষা ২০২৪ [Bangladesh Economic Review 2024 Bangla.pdf] কম্পিউটার , ট্যাব ও স্মার্ট ফোন ভার্সন সহ সম্পূর্ণ বাংলা ই-বুক বা pdf বই " সুচিপত্র ...বুকমার্ক মেনু 🔖 ও হাইপার লিংক মেনু 📝👆 যুক্ত ..
আমাদের সবার জন্য খুব খুব গুরুত্বপূর্ণ একটি বই ..বিসিএস, ব্যাংক, ইউনিভার্সিটি ভর্তি ও যে কোন প্রতিযোগিতা মূলক পরীক্ষার জন্য এর খুব ইম্পরট্যান্ট একটি বিষয় ...তাছাড়া বাংলাদেশের সাম্প্রতিক যে কোন ডাটা বা তথ্য এই বইতে পাবেন ...
তাই একজন নাগরিক হিসাবে এই তথ্য গুলো আপনার জানা প্রয়োজন ...।
বিসিএস ও ব্যাংক এর লিখিত পরীক্ষা ...+এছাড়া মাধ্যমিক ও উচ্চমাধ্যমিকের স্টুডেন্টদের জন্য অনেক কাজে আসবে ...
How to Build a Module in Odoo 17 Using the Scaffold MethodCeline George
Odoo provides an option for creating a module by using a single line command. By using this command the user can make a whole structure of a module. It is very easy for a beginner to make a module. There is no need to make each file manually. This slide will show how to create a module using the scaffold method.
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Diana Rendina
Librarians are leading the way in creating future-ready citizens – now we need to update our spaces to match. In this session, attendees will get inspiration for transforming their library spaces. You’ll learn how to survey students and patrons, create a focus group, and use design thinking to brainstorm ideas for your space. We’ll discuss budget friendly ways to change your space as well as how to find funding. No matter where you’re at, you’ll find ideas for reimagining your space in this session.
A review of the growth of the Israel Genealogy Research Association Database Collection for the last 12 months. Our collection is now passed the 3 million mark and still growing. See which archives have contributed the most. See the different types of records we have, and which years have had records added. You can also see what we have for the future.
Chapter wise All Notes of First year Basic Civil Engineering.pptxDenish Jangid
Chapter wise All Notes of First year Basic Civil Engineering
Syllabus
Chapter-1
Introduction to objective, scope and outcome the subject
Chapter 2
Introduction: Scope and Specialization of Civil Engineering, Role of civil Engineer in Society, Impact of infrastructural development on economy of country.
Chapter 3
Surveying: Object Principles & Types of Surveying; Site Plans, Plans & Maps; Scales & Unit of different Measurements.
Linear Measurements: Instruments used. Linear Measurement by Tape, Ranging out Survey Lines and overcoming Obstructions; Measurements on sloping ground; Tape corrections, conventional symbols. Angular Measurements: Instruments used; Introduction to Compass Surveying, Bearings and Longitude & Latitude of a Line, Introduction to total station.
Levelling: Instrument used Object of levelling, Methods of levelling in brief, and Contour maps.
Chapter 4
Buildings: Selection of site for Buildings, Layout of Building Plan, Types of buildings, Plinth area, carpet area, floor space index, Introduction to building byelaws, concept of sun light & ventilation. Components of Buildings & their functions, Basic concept of R.C.C., Introduction to types of foundation
Chapter 5
Transportation: Introduction to Transportation Engineering; Traffic and Road Safety: Types and Characteristics of Various Modes of Transportation; Various Road Traffic Signs, Causes of Accidents and Road Safety Measures.
Chapter 6
Environmental Engineering: Environmental Pollution, Environmental Acts and Regulations, Functional Concepts of Ecology, Basics of Species, Biodiversity, Ecosystem, Hydrological Cycle; Chemical Cycles: Carbon, Nitrogen & Phosphorus; Energy Flow in Ecosystems.
Water Pollution: Water Quality standards, Introduction to Treatment & Disposal of Waste Water. Reuse and Saving of Water, Rain Water Harvesting. Solid Waste Management: Classification of Solid Waste, Collection, Transportation and Disposal of Solid. Recycling of Solid Waste: Energy Recovery, Sanitary Landfill, On-Site Sanitation. Air & Noise Pollution: Primary and Secondary air pollutants, Harmful effects of Air Pollution, Control of Air Pollution. . Noise Pollution Harmful Effects of noise pollution, control of noise pollution, Global warming & Climate Change, Ozone depletion, Greenhouse effect
Text Books:
1. Palancharmy, Basic Civil Engineering, McGraw Hill publishers.
2. Satheesh Gopi, Basic Civil Engineering, Pearson Publishers.
3. Ketki Rangwala Dalal, Essentials of Civil Engineering, Charotar Publishing House.
4. BCP, Surveying volume 1
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.
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
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