The document discusses finite differences and polynomial functions. It is shown that if the nth differences of a function are equal, the function is a polynomial of degree n. The document provides examples of using difference tables to determine if a function is polynomial and, if so, its degree. It is concluded that applying the polynomial difference theorem involves examining differences in a table to determine if the data represents a polynomial function.
1. The document discusses different probability distributions including the uniform, exponential, and gamma distributions. It provides the definitions and properties of each distribution.
2. Formulas are given for the probability density function (pdf), cumulative distribution function (cdf), expected value, and variance of the uniform and exponential distributions.
3. The gamma distribution is defined as the time elapsed until a certain number of Poisson-distributed events occur, and its pdf and moment generating function are provided.
This document summarizes Ji Li's dissertation defense on counting point-determining graphs and prime graphs using Joyal's theory of species. The defense took place on May 10th, 2007 at Brandeis University, with Professor Ira Gessel serving as Ji Li's thesis advisor. The dissertation outlines the use of species theory to define and enumerate point-determining graphs, bi-point-determining graphs, and point-determining 2-colored graphs, as well as applying species theory to study prime graphs.
Here are the key points about g given f:
- g represents the area under the curve of f over successive intervals of the x-axis
- As x increases over an interval, g will increase if f is positive over that interval and decrease if f is negative
- The concavity (convexity or concavity) of g will match the concavity of f over each interval
In summary, the area function g, as defined by the integral of f, will have properties that correspond directly to the sign and concavity of f over successive intervals of integration.
k-MLE: A fast algorithm for learning statistical mixture modelsFrank Nielsen
This document describes a fast algorithm called k-MLE for learning statistical mixture models. k-MLE is based on the connection between exponential family mixture models and Bregman divergences. It extends Lloyd's k-means clustering algorithm to optimize the complete log-likelihood of an exponential family mixture model using Bregman divergences. The algorithm iterates between assigning data points to clusters based on Bregman divergence, and updating the cluster parameters by taking the Bregman centroid of each cluster's assigned points. This provides a fast method for maximum likelihood estimation of exponential family mixture models.
The document discusses black hole solutions in gauged supergravity theories. It outlines how black holes in ungauged supergravity theories can be described as extremal, BPS solutions that preserve some supersymmetry. The dynamics of black holes in supergravity are governed by a bosonic action that includes terms for Einstein-Hilbert gravity, vector field kinetic terms, axionic couplings, and a non-linear sigma model. Gauged supergravity theories provide a landscape of anti-de Sitter backgrounds, and the document aims to develop a systematic approach to studying how these backgrounds may be destabilized by the presence of stable black hole solutions.
This document summarizes key concepts from Chapter 2 of a book on statistical methods for handling incomplete data. It introduces the likelihood-based approach and defines key terms like the likelihood function, maximum likelihood estimator, Fisher information, and missing at random. The chapter also provides examples of observed likelihood functions for censored regression and survival analysis models with missing data.
This document contains a mathematics exam with 4 problems (Themes A, B, C, D) involving functions, derivatives, monotonicity, convexity, extrema, asymptotes and limits.
Theme A involves properties of differentiable functions, the definition of the derivative, and Rolle's theorem. Theme B analyzes the monotonicity, convexity, asymptotes and graph of a given function.
Theme C proves properties of a continuous, monotonically increasing function and finds extrema of related functions. Theme D proves properties of a power function and its relation to a given line, defines a new function, and proves monotonicity and existence of a single real root for a polynomial equation.
better together? statistical learning in models made of modulesChristian Robert
The document discusses statistical models composed of modular components called modules. Each module may be developed independently and represent different data modalities or domains of knowledge. Joint Bayesian updating treats all modules simultaneously but misspecification of one module can impact the others. Alternative approaches are proposed to allow uncertainty propagation between modules while preventing feedback that could lead to misspecification. Candidate distributions for the modules are discussed, along with strategies for choosing among them based on predictive performance.
1. The document discusses different probability distributions including the uniform, exponential, and gamma distributions. It provides the definitions and properties of each distribution.
2. Formulas are given for the probability density function (pdf), cumulative distribution function (cdf), expected value, and variance of the uniform and exponential distributions.
3. The gamma distribution is defined as the time elapsed until a certain number of Poisson-distributed events occur, and its pdf and moment generating function are provided.
This document summarizes Ji Li's dissertation defense on counting point-determining graphs and prime graphs using Joyal's theory of species. The defense took place on May 10th, 2007 at Brandeis University, with Professor Ira Gessel serving as Ji Li's thesis advisor. The dissertation outlines the use of species theory to define and enumerate point-determining graphs, bi-point-determining graphs, and point-determining 2-colored graphs, as well as applying species theory to study prime graphs.
Here are the key points about g given f:
- g represents the area under the curve of f over successive intervals of the x-axis
- As x increases over an interval, g will increase if f is positive over that interval and decrease if f is negative
- The concavity (convexity or concavity) of g will match the concavity of f over each interval
In summary, the area function g, as defined by the integral of f, will have properties that correspond directly to the sign and concavity of f over successive intervals of integration.
k-MLE: A fast algorithm for learning statistical mixture modelsFrank Nielsen
This document describes a fast algorithm called k-MLE for learning statistical mixture models. k-MLE is based on the connection between exponential family mixture models and Bregman divergences. It extends Lloyd's k-means clustering algorithm to optimize the complete log-likelihood of an exponential family mixture model using Bregman divergences. The algorithm iterates between assigning data points to clusters based on Bregman divergence, and updating the cluster parameters by taking the Bregman centroid of each cluster's assigned points. This provides a fast method for maximum likelihood estimation of exponential family mixture models.
The document discusses black hole solutions in gauged supergravity theories. It outlines how black holes in ungauged supergravity theories can be described as extremal, BPS solutions that preserve some supersymmetry. The dynamics of black holes in supergravity are governed by a bosonic action that includes terms for Einstein-Hilbert gravity, vector field kinetic terms, axionic couplings, and a non-linear sigma model. Gauged supergravity theories provide a landscape of anti-de Sitter backgrounds, and the document aims to develop a systematic approach to studying how these backgrounds may be destabilized by the presence of stable black hole solutions.
This document summarizes key concepts from Chapter 2 of a book on statistical methods for handling incomplete data. It introduces the likelihood-based approach and defines key terms like the likelihood function, maximum likelihood estimator, Fisher information, and missing at random. The chapter also provides examples of observed likelihood functions for censored regression and survival analysis models with missing data.
This document contains a mathematics exam with 4 problems (Themes A, B, C, D) involving functions, derivatives, monotonicity, convexity, extrema, asymptotes and limits.
Theme A involves properties of differentiable functions, the definition of the derivative, and Rolle's theorem. Theme B analyzes the monotonicity, convexity, asymptotes and graph of a given function.
Theme C proves properties of a continuous, monotonically increasing function and finds extrema of related functions. Theme D proves properties of a power function and its relation to a given line, defines a new function, and proves monotonicity and existence of a single real root for a polynomial equation.
better together? statistical learning in models made of modulesChristian Robert
The document discusses statistical models composed of modular components called modules. Each module may be developed independently and represent different data modalities or domains of knowledge. Joint Bayesian updating treats all modules simultaneously but misspecification of one module can impact the others. Alternative approaches are proposed to allow uncertainty propagation between modules while preventing feedback that could lead to misspecification. Candidate distributions for the modules are discussed, along with strategies for choosing among them based on predictive performance.
This document defines inverse variation and provides examples of using inverse variation to solve problems. It gives the formula that if y is inversely proportional to x, then y = k/x, where k is a constant. It then works through several word problems that involve finding unknown values of x or y given one value and the inverse proportional relationship between the variables.
1. The document discusses inverse and inverse square variation functions and how to solve problems involving them.
2. Inverse variation occurs when one variable gets larger as the other gets smaller. Inverse square variation is when the independent variable is squared.
3. The inverse variation function is y = k/x and the inverse square variation function is y = k/x^2, where k is the constant of variation.
4. Two examples are provided to demonstrate solving inverse variation and inverse square variation word problems.
The document shows the step-by-step work to solve two inverse variation problems. In the first problem, it is given that y=563 when x=3, and the question is to find y when x=9. Through substituting values into the inverse variation equation y=kx^2 and solving for k, it is determined that y=567 when x=9. In the second part of the problem, it is asked to find y when x=5, which is determined to be 175. The second problem finds g when h=9, given that g=3 when h=6, determining that g=2 when h=9.
The document provides examples of direct and inverse variations and instructions on how to translate statements of direct and inverse variation into mathematical equations using a constant of variation k. It also gives problems to solve for the indicated variable in various scenarios involving direct and inverse variation.
The document discusses the concept of inverse variation through several examples:
- If 5 cats can catch 5 mice in 5 days, then 3 cats can catch 3 mice in 5 days (example 1)
- As one quantity such as time increases, the other such as velocity decreases in inverse variation examples involving a cyclist, runner, jogger and walker (examples 3-5)
- According to Ohm's Law, the current in a wire is inversely proportional to the resistance - as one increases, the other decreases (examples 6-8).
The document discusses how the time it takes for water to boil, m, varies inversely with temperature, t. Specifically, it states that as temperature, t, increases, the time to boil, m, decreases. It provides the formula m = k/t, where k is a constant of variation. As one variable (temperature) increases, the other (time to boil) decreases.
Signal Processing Course : Inverse Problems RegularizationGabriel Peyré
This document discusses regularization techniques for inverse problems. It introduces variational priors like Sobolev and total variation to regularize inverse problems. Gradient descent and proximal gradient methods are presented to minimize regularization functionals for problems like denoising. Conjugate gradient and projected gradient descent are discussed for solving the regularized inverse problems. Total variation priors are shown to better recover edges compared to Sobolev priors. Non-smooth optimization methods may be needed to handle non-differentiable total variation functionals.
The document discusses different types of variations:
1) Direct variation results in a straight line graph, while direct square variation results in a parabolic graph.
2) Inverse variation means that as one variable increases, the other decreases, maintaining a constant product. The graph of an inverse variation is a hyperbola.
3) Examples show inverse variations between variables like pressure and volume of a gas, or jobs completed and number of workers.
4) Quiz questions test understanding of direct, inverse, and their equation representations.
The document discusses direct and inverse variation. It provides examples of questions determining if a relationship demonstrates direct or inverse variation. It also shows how to write the equation for a direct or inverse variation relationship given data points. Direct variation follows the equation y=kx, where k is the constant rate of change. Inverse variation follows y=k/x. The document provides step-by-step workings for multiple examples of identifying and modeling direct and inverse variation relationships from sets of data points.
Mathematics 9 Lesson 4-C: Joint and Combined VariationJuan Miguel Palero
This powerpoint presentation discusses or talks about the topic or lesson Joint and Combined Variations. It also discusses and explains the rules, concepts, steps and examples of Joint and Combined Variation
The document discusses inverse variation and provides examples to illustrate the concept. It begins by showing two tables with values that demonstrate an inverse relationship between variables x and y. It then provides the definition of inverse variation as a situation where an increase in one variable causes a decrease in the other, such that their product is constant. Examples are given of relationships that demonstrate inverse variation, such as the number of people sharing a pizza relating inversely to the number of slices. The document also contains a word problem demonstrating how to set up and solve an equation using the inverse variation relationship.
The document defines and provides examples of angle relationships including adjacent angles, linear pairs, vertical angles, complementary angles, and supplementary angles. It defines each term and provides examples of identifying angle pairs that satisfy each relationship. It also includes examples of using properties of these angle relationships to solve problems, such as finding missing angle measures.
The document defines various terms related to angle measure including ray, angle, vertex, acute angle, obtuse angle, and angle bisector. It then provides examples measuring angles in a figure and solving an equation involving angle measures.
This document discusses finding points and midpoints on line segments. It defines midpoint as the point halfway between two endpoints and provides the formula to calculate it. Several examples are given to demonstrate how to find the midpoint of a segment, locate a point at a fractional distance from one endpoint, and find a point where the ratio of distances from the endpoints is a given ratio. The key concepts covered are calculating midpoints using averages of x- and y-coordinates, and setting up and solving equations to locate interior points using fractional or ratio distances along a segment.
This document defines key vocabulary terms related to line segments and distance. It defines a line segment as a portion of a line distinguished by endpoints, and defines betweenness of points and the term "between." It also defines congruent segments, constructions, distance, and irrational numbers. Several examples are provided to demonstrate calculating distances between points on a number line, using a ruler to measure segments, and applying the Pythagorean theorem.
This document defines key vocabulary terms related to line segments and distance, including line segment, betweenness of points, congruent segments, and distance formula. It provides examples of calculating distances between points on number lines and using the Pythagorean theorem to find distances between points graphed on a coordinate plane. Examples include measuring line segments with a ruler, finding distances by adding or subtracting measures, and applying the distance formula and Pythagorean theorem to solve for unknown distances.
This document introduces basic geometry concepts such as points, lines, planes, and their intersections. It defines a point as having no size or shape, a line as an infinite set of collinear points, and a plane as a flat surface extending indefinitely. Examples demonstrate identifying geometric shapes from real-world objects and graphing points and lines on a coordinate plane. The summary defines key terms and provides examples of geometric concepts and relationships.
The document discusses inverse functions and relations. It defines an inverse relation as one where the coordinates of a relation are switched, and an inverse function as one where the domain and range of a function are switched. It provides examples of finding the inverse of specific relations and functions by switching their coordinates or domain and range. It also discusses how to determine if two functions are inverses using their graphs and the horizontal line test.
The document discusses composition of functions. It defines composition of functions as using the output of one function as the input of another. It provides an example of composing two functions f and g, showing the steps of evaluating f(g(x)) and g(f(x)) at different values of x. Another example is given with two functions defined by sets of ordered pairs, finding the compositions f∘g and g∘f by evaluating them at different inputs and stating their domains and ranges.
The document discusses operations that can be performed on functions, including addition, subtraction, multiplication, and division. Definitions of each operation are provided, along with examples of applying the operations to specific functions. Addition of functions involves adding the outputs of each function, subtraction involves subtracting the outputs, multiplication involves multiplying the outputs, and division involves dividing the outputs given the denominator function is not equal to 0. Several examples are worked through applying the different operations to functions like f(x)=2x and g(x)=-x+5. The examples also demonstrate evaluating composite functions and restricting domains as needed.
The document discusses determining the number and type of roots of polynomial equations. It states that every polynomial with degree greater than zero has at least one root in the set of complex numbers according to the Fundamental Theorem of Algebra. Descartes' Rule of Signs is introduced, which relates the number of changes in sign of a polynomial's terms to its possible positive and negative real roots. An example problem is worked through applying these concepts to determine the possible number of positive, negative, and imaginary roots.
This document discusses synthetic division and the remainder and factor theorems. It provides examples of using synthetic division to evaluate functions, determine the number of terms in a sequence, and factor polynomials. The key steps of synthetic division are shown, along with checking the remainder and determining common factors. Three examples are worked through to demonstrate these concepts.
This document defines inverse variation and provides examples of using inverse variation to solve problems. It gives the formula that if y is inversely proportional to x, then y = k/x, where k is a constant. It then works through several word problems that involve finding unknown values of x or y given one value and the inverse proportional relationship between the variables.
1. The document discusses inverse and inverse square variation functions and how to solve problems involving them.
2. Inverse variation occurs when one variable gets larger as the other gets smaller. Inverse square variation is when the independent variable is squared.
3. The inverse variation function is y = k/x and the inverse square variation function is y = k/x^2, where k is the constant of variation.
4. Two examples are provided to demonstrate solving inverse variation and inverse square variation word problems.
The document shows the step-by-step work to solve two inverse variation problems. In the first problem, it is given that y=563 when x=3, and the question is to find y when x=9. Through substituting values into the inverse variation equation y=kx^2 and solving for k, it is determined that y=567 when x=9. In the second part of the problem, it is asked to find y when x=5, which is determined to be 175. The second problem finds g when h=9, given that g=3 when h=6, determining that g=2 when h=9.
The document provides examples of direct and inverse variations and instructions on how to translate statements of direct and inverse variation into mathematical equations using a constant of variation k. It also gives problems to solve for the indicated variable in various scenarios involving direct and inverse variation.
The document discusses the concept of inverse variation through several examples:
- If 5 cats can catch 5 mice in 5 days, then 3 cats can catch 3 mice in 5 days (example 1)
- As one quantity such as time increases, the other such as velocity decreases in inverse variation examples involving a cyclist, runner, jogger and walker (examples 3-5)
- According to Ohm's Law, the current in a wire is inversely proportional to the resistance - as one increases, the other decreases (examples 6-8).
The document discusses how the time it takes for water to boil, m, varies inversely with temperature, t. Specifically, it states that as temperature, t, increases, the time to boil, m, decreases. It provides the formula m = k/t, where k is a constant of variation. As one variable (temperature) increases, the other (time to boil) decreases.
Signal Processing Course : Inverse Problems RegularizationGabriel Peyré
This document discusses regularization techniques for inverse problems. It introduces variational priors like Sobolev and total variation to regularize inverse problems. Gradient descent and proximal gradient methods are presented to minimize regularization functionals for problems like denoising. Conjugate gradient and projected gradient descent are discussed for solving the regularized inverse problems. Total variation priors are shown to better recover edges compared to Sobolev priors. Non-smooth optimization methods may be needed to handle non-differentiable total variation functionals.
The document discusses different types of variations:
1) Direct variation results in a straight line graph, while direct square variation results in a parabolic graph.
2) Inverse variation means that as one variable increases, the other decreases, maintaining a constant product. The graph of an inverse variation is a hyperbola.
3) Examples show inverse variations between variables like pressure and volume of a gas, or jobs completed and number of workers.
4) Quiz questions test understanding of direct, inverse, and their equation representations.
The document discusses direct and inverse variation. It provides examples of questions determining if a relationship demonstrates direct or inverse variation. It also shows how to write the equation for a direct or inverse variation relationship given data points. Direct variation follows the equation y=kx, where k is the constant rate of change. Inverse variation follows y=k/x. The document provides step-by-step workings for multiple examples of identifying and modeling direct and inverse variation relationships from sets of data points.
Mathematics 9 Lesson 4-C: Joint and Combined VariationJuan Miguel Palero
This powerpoint presentation discusses or talks about the topic or lesson Joint and Combined Variations. It also discusses and explains the rules, concepts, steps and examples of Joint and Combined Variation
The document discusses inverse variation and provides examples to illustrate the concept. It begins by showing two tables with values that demonstrate an inverse relationship between variables x and y. It then provides the definition of inverse variation as a situation where an increase in one variable causes a decrease in the other, such that their product is constant. Examples are given of relationships that demonstrate inverse variation, such as the number of people sharing a pizza relating inversely to the number of slices. The document also contains a word problem demonstrating how to set up and solve an equation using the inverse variation relationship.
The document defines and provides examples of angle relationships including adjacent angles, linear pairs, vertical angles, complementary angles, and supplementary angles. It defines each term and provides examples of identifying angle pairs that satisfy each relationship. It also includes examples of using properties of these angle relationships to solve problems, such as finding missing angle measures.
The document defines various terms related to angle measure including ray, angle, vertex, acute angle, obtuse angle, and angle bisector. It then provides examples measuring angles in a figure and solving an equation involving angle measures.
This document discusses finding points and midpoints on line segments. It defines midpoint as the point halfway between two endpoints and provides the formula to calculate it. Several examples are given to demonstrate how to find the midpoint of a segment, locate a point at a fractional distance from one endpoint, and find a point where the ratio of distances from the endpoints is a given ratio. The key concepts covered are calculating midpoints using averages of x- and y-coordinates, and setting up and solving equations to locate interior points using fractional or ratio distances along a segment.
This document defines key vocabulary terms related to line segments and distance. It defines a line segment as a portion of a line distinguished by endpoints, and defines betweenness of points and the term "between." It also defines congruent segments, constructions, distance, and irrational numbers. Several examples are provided to demonstrate calculating distances between points on a number line, using a ruler to measure segments, and applying the Pythagorean theorem.
This document defines key vocabulary terms related to line segments and distance, including line segment, betweenness of points, congruent segments, and distance formula. It provides examples of calculating distances between points on number lines and using the Pythagorean theorem to find distances between points graphed on a coordinate plane. Examples include measuring line segments with a ruler, finding distances by adding or subtracting measures, and applying the distance formula and Pythagorean theorem to solve for unknown distances.
This document introduces basic geometry concepts such as points, lines, planes, and their intersections. It defines a point as having no size or shape, a line as an infinite set of collinear points, and a plane as a flat surface extending indefinitely. Examples demonstrate identifying geometric shapes from real-world objects and graphing points and lines on a coordinate plane. The summary defines key terms and provides examples of geometric concepts and relationships.
The document discusses inverse functions and relations. It defines an inverse relation as one where the coordinates of a relation are switched, and an inverse function as one where the domain and range of a function are switched. It provides examples of finding the inverse of specific relations and functions by switching their coordinates or domain and range. It also discusses how to determine if two functions are inverses using their graphs and the horizontal line test.
The document discusses composition of functions. It defines composition of functions as using the output of one function as the input of another. It provides an example of composing two functions f and g, showing the steps of evaluating f(g(x)) and g(f(x)) at different values of x. Another example is given with two functions defined by sets of ordered pairs, finding the compositions f∘g and g∘f by evaluating them at different inputs and stating their domains and ranges.
The document discusses operations that can be performed on functions, including addition, subtraction, multiplication, and division. Definitions of each operation are provided, along with examples of applying the operations to specific functions. Addition of functions involves adding the outputs of each function, subtraction involves subtracting the outputs, multiplication involves multiplying the outputs, and division involves dividing the outputs given the denominator function is not equal to 0. Several examples are worked through applying the different operations to functions like f(x)=2x and g(x)=-x+5. The examples also demonstrate evaluating composite functions and restricting domains as needed.
The document discusses determining the number and type of roots of polynomial equations. It states that every polynomial with degree greater than zero has at least one root in the set of complex numbers according to the Fundamental Theorem of Algebra. Descartes' Rule of Signs is introduced, which relates the number of changes in sign of a polynomial's terms to its possible positive and negative real roots. An example problem is worked through applying these concepts to determine the possible number of positive, negative, and imaginary roots.
This document discusses synthetic division and the remainder and factor theorems. It provides examples of using synthetic division to evaluate functions, determine the number of terms in a sequence, and factor polynomials. The key steps of synthetic division are shown, along with checking the remainder and determining common factors. Three examples are worked through to demonstrate these concepts.
This document discusses solving polynomial equations by factoring polynomials. It begins with essential questions and vocabulary about factoring polynomials and solving polynomial equations by factoring. It then provides the number of terms in a polynomial and the corresponding factoring technique that can be used. Examples of factoring various polynomials are also provided. The document aims to teach students how to factor polynomials and solve polynomial equations by factoring.
The document defines key terms and theorems related to trapezoids and kites. It provides definitions for trapezoid, bases, legs of a trapezoid, base angles, isosceles trapezoid, midsegment of a trapezoid, and kite. It also lists theorems about properties of isosceles trapezoids and kites. Two examples problems are included, one finding measures of an isosceles trapezoid and another showing a quadrilateral is a trapezoid.
The document discusses rhombi and squares. It defines a rhombus as a parallelogram with four congruent sides and gives its properties. A square is defined as a parallelogram with four right angles and four congruent sides. The document provides theorems for identifying rhombi and squares. It then gives examples of using the properties and theorems to determine if a shape is a rhombus, rectangle, or square.
The document discusses properties of rectangles. A rectangle is defined as a parallelogram with four right angles. The key properties are that opposite sides are parallel and congruent, opposite angles are congruent, and consecutive angles are supplementary. The diagonals of a rectangle bisect each other and are congruent. Theorems are presented regarding the diagonals of rectangles. Examples apply the properties of rectangles to find missing side lengths, angles, and diagonals. One example uses the distance formula and slope to determine if a quadrilateral is a rectangle.
The document discusses properties of parallelograms and provides examples of determining if a quadrilateral is a parallelogram. It defines four theorems for identifying parallelograms based on opposite sides, opposite angles, bisecting diagonals, and parallel/congruent sides. Examples solve systems of equations to find values of variables such that the quadrilaterals satisfy parallelogram properties. One example uses slopes of side segments to show a quadrilateral is a parallelogram due to parallel opposite sides.
The document discusses properties of parallelograms. It defines a parallelogram as a quadrilateral with two pairs of parallel sides. It then lists several properties of parallelograms: opposite sides are congruent, opposite angles are congruent, consecutive angles are supplementary, and if one angle is a right angle all angles are right angles. It also discusses properties of diagonals in parallelograms, including that diagonals bisect each other and divide the parallelogram into two congruent triangles. Several examples demonstrate using these properties to solve problems about parallelograms.
The document summarizes key concepts about polygons, including:
- The sum of the interior angles of a polygon with n sides is (n-2)180 degrees.
- The sum of the exterior angles of a polygon is 360 degrees.
- Examples are provided to demonstrate calculating sums of interior/exterior angles and finding missing angle measures using angle sums.
- Regular polygons are defined by their number of sides.
The document discusses analyzing graphs of polynomial functions. It provides examples of locating real zeros of polynomials using the location principle and estimating relative maxima and minima. Example 1 analyzes the polynomial f(x) = x^4 - x^3 - 4x^2 + 1 and locates its real zeros between consecutive integer values. Example 2 graphs the polynomial f(x) = x^3 - 3x^2 + 5 and estimates the x-coordinates of relative maxima and minima.
This document discusses polynomial functions. It defines key terms like polynomial in one variable, leading coefficient, and polynomial function. It provides examples of power functions of varying degrees like quadratic, cubic, quartic and quintic functions. The document also includes examples of evaluating polynomial functions, finding degrees and leading coefficients, graphing polynomial functions from tables of values, and describing properties of graphs.
How to Download & Install Module From the Odoo App Store in Odoo 17Celine George
Custom modules offer the flexibility to extend Odoo's capabilities, address unique requirements, and optimize workflows to align seamlessly with your organization's processes. By leveraging custom modules, businesses can unlock greater efficiency, productivity, and innovation, empowering them to stay competitive in today's dynamic market landscape. In this tutorial, we'll guide you step by step on how to easily download and install modules from the Odoo App Store.
Andreas Schleicher presents PISA 2022 Volume III - Creative Thinking - 18 Jun...EduSkills OECD
Andreas Schleicher, Director of Education and Skills at the OECD presents at the launch of PISA 2022 Volume III - Creative Minds, Creative Schools on 18 June 2024.
THE SACRIFICE HOW PRO-PALESTINE PROTESTS STUDENTS ARE SACRIFICING TO CHANGE T...indexPub
The recent surge in pro-Palestine student activism has prompted significant responses from universities, ranging from negotiations and divestment commitments to increased transparency about investments in companies supporting the war on Gaza. This activism has led to the cessation of student encampments but also highlighted the substantial sacrifices made by students, including academic disruptions and personal risks. The primary drivers of these protests are poor university administration, lack of transparency, and inadequate communication between officials and students. This study examines the profound emotional, psychological, and professional impacts on students engaged in pro-Palestine protests, focusing on Generation Z's (Gen-Z) activism dynamics. This paper explores the significant sacrifices made by these students and even the professors supporting the pro-Palestine movement, with a focus on recent global movements. Through an in-depth analysis of printed and electronic media, the study examines the impacts of these sacrifices on the academic and personal lives of those involved. The paper highlights examples from various universities, demonstrating student activism's long-term and short-term effects, including disciplinary actions, social backlash, and career implications. The researchers also explore the broader implications of student sacrifices. The findings reveal that these sacrifices are driven by a profound commitment to justice and human rights, and are influenced by the increasing availability of information, peer interactions, and personal convictions. The study also discusses the broader implications of this activism, comparing it to historical precedents and assessing its potential to influence policy and public opinion. The emotional and psychological toll on student activists is significant, but their sense of purpose and community support mitigates some of these challenges. However, the researchers call for acknowledging the broader Impact of these sacrifices on the future global movement of FreePalestine.
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
This presentation was provided by Racquel Jemison, Ph.D., Christina MacLaughlin, Ph.D., and Paulomi Majumder. Ph.D., all of the American Chemical Society, for the second session of NISO's 2024 Training Series "DEIA in the Scholarly Landscape." Session Two: 'Expanding Pathways to Publishing Careers,' was held June 13, 2024.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
Gender and Mental Health - Counselling and Family Therapy Applications and In...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
4. What was determined from
the In-Class Activity
If 1st differences are equal, you
have a linear equation.
Wednesday, March 25, 2009
5. What was determined from
the In-Class Activity
If 1st differences are equal, you
have a linear equation.
If 2nd differences are equal, you
have a quadratic equation.
Wednesday, March 25, 2009
6. What was determined from
the In-Class Activity
If 1st differences are equal, you
have a linear equation.
If 2nd differences are equal, you
have a quadratic equation.
If 3rd differences are equal, you
have a cubic equation.
Wednesday, March 25, 2009
8. Polynomial-Difference
Theorem
y = f(x) is a polynomial function of
degree n IFF for any arithmetic sequence
of independent variables, the n th
difference of the dependent variables
are equal and the (n-1)st differences
are not equal.
Wednesday, March 25, 2009
9. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
Wednesday, March 25, 2009
10. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4
Wednesday, March 25, 2009
11. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9
Wednesday, March 25, 2009
12. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16
Wednesday, March 25, 2009
13. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25
Wednesday, March 25, 2009
14. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36
Wednesday, March 25, 2009
15. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49
Wednesday, March 25, 2009
16. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
Wednesday, March 25, 2009
17. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5
Wednesday, March 25, 2009
18. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7
Wednesday, March 25, 2009
19. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9
Wednesday, March 25, 2009
20. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11
Wednesday, March 25, 2009
21. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13
Wednesday, March 25, 2009
22. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
Wednesday, March 25, 2009
23. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
2
Wednesday, March 25, 2009
24. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
2 2
Wednesday, March 25, 2009
25. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
2 2 2
Wednesday, March 25, 2009
26. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
2 2 2 2
Wednesday, March 25, 2009
27. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
2 2 2 2 2
Wednesday, March 25, 2009
28. Example 1
Consider the data in the table. Is f(n)
a polynomial function? Justify.
n 1 2 3 4 5 6 7 8
f(n) 1 5 14 30 55 91 140 204
4 9 16 25 36 49 64
5 7 9 11 13 15
3rd row 2 2 2 2 2
Wednesday, March 25, 2009
33. Method of Finite
Differences
When you apply the Polynomial-Difference
Theorem.
Wednesday, March 25, 2009
34. Method of Finite
Differences
When you apply the Polynomial-Difference
Theorem.
Examine the differences of the dependent
variables to determine if a set of data
represents a polynomial, where the
degree n will be the row where the equal
differences occur.
Wednesday, March 25, 2009
35. Example 2
A sequence is defined by
a = 1
1
2
an = (an − 1 ) − 10an − 1 + 8, for int. n ≥ 2
Is there an explicit polynomial formula
for this? Justify!
Wednesday, March 25, 2009
36. Example 2
A sequence is defined by
a = 1
1
2
an = (an − 1 ) − 10an − 1 + 8, for int. n ≥ 2
Is there an explicit polynomial formula
for this? Justify!
Create a table and examine the
differences.
Wednesday, March 25, 2009
54. n 1 2 3 4 5 6
an 1 -1 19 179 30259 915304499
-2 20 160 30080 915274240
22 140 29920 915244160
118 29780 915214240
29662 915184460
There is no common difference, so there
does not seem to be a polynomial formula
to represent the sequence.
Wednesday, March 25, 2009
55. Homework
p. 727 #1-23
“The only way of finding the limits of the possible is by going beyond them
into the impossible.” - Arthur C. Clarke
Wednesday, March 25, 2009