Regresi linear
Download as docx, pdf0 likes577 views
This document contains data from an experiment with 6 data points measuring variables X and Y. It performs a linear regression analysis on the data to find the linear regression equation relating X and Y. The linear regression equation calculated is y = 11.37x - 4.676 with an R2 value of 0.884, indicating a strong linear relationship between the variables.
1 of 3
Download to read offline
Ad
Recommended
INVERSION OF MATRIX BY GAUSS ELIMINATION METHOD
INVERSION OF MATRIX BY GAUSS ELIMINATION METHODreach2arkaELECTRICAL
油
The document discusses inverting a matrix. It explains that to invert a matrix A, we let X be the inverse of A such that AX = I, where I is the identity matrix. We then form an augmented matrix of A and I and use Gaussian elimination to put it in upper triangular form. This allows us to solve for the elements of X, which are the entries of the inverse matrix. As an example, it shows inverting a 3x3 matrix A through this process and obtaining its inverse A-1.July16
July16fperickson
油
The document outlines topics to cover on July 16th including a quiz on Friday, homework due Monday, and graphing indifference curves using different utility functions. It discusses rescaling curves by raising them to a power or multiplying by a constant. Finally, it notes finding the optimum using workbooks, with the utility function U=XY^2, and mapping utility to create a 3D surface.Paul Bleau Calc III Project 2 - Basel Problem
Paul Bleau Calc III Project 2 - Basel ProblemPaul Bleau
油
The document discusses the history and solution of the Basel problem. It begins by outlining the problem's origins in 1644 and its consideration by Bernoulli brothers in 1689. It then notes that Leonhard Euler solved the problem in 1734 by proving the exact sum is 2/6 using trigonometry and factorials. Finally, it states that Paul Bleau successfully recreated Euler's solution using multivariable calculus for a class project in 2015.Gaussian elimination
Gaussian eliminationAwais Qureshi
油
Gaussian elimination is used to solve systems of linear equations by performing row operations on the corresponding augmented matrix. The examples demonstrate this process for systems with unique solutions and for an inconsistent system with no solutions. Row operations are performed simultaneously on the matrix and equations to transform the system into row echelon form from which the solution can be read directly.Numerical solution of system of linear equations
Numerical solution of system of linear equationsreach2arkaELECTRICAL
油
The document presents methods for solving systems of linear equations, specifically focusing on the LU factorization method and the Gauss elimination method. It describes the process of using triangular matrices to find solutions and illustrates the techniques with examples. The conclusion highlights the efficiency of triangular systems over more complex methods like matrix inversion.INTERPOLATION
INTERPOLATIONtirath prajapati
油
The document provides an overview of various interpolation methods, including Newton's forward and backward interpolation, Gauss interpolation, Lagrange interpolation, and Newton divided differential interpolation. Each method is described with corresponding formulas and examples for practical application in calculating function values using given data points. The focus is on solving problems related to civil engineering and mathematics, particularly in the context of interpolation techniques.Lesson 9: Gaussian Elimination
Lesson 9: Gaussian EliminationMatthew Leingang
油
This document provides an overview of Gaussian elimination for solving systems of linear equations. It begins with examples of single and multi-variable linear equations. It then introduces the concept of representing a system of equations using an augmented matrix and describes row operations that can be performed on the matrix without changing the solution. The document walks through using Gaussian elimination to solve a sample system of 3 equations in 3 variables. It concludes with more details on the elimination process, including performing a "backward pass" to solve for the variables.Relaxation method
Relaxation methodParinda Rajapaksha
油
The relaxation method is an iterative technique used to solve systems of linear equations by successively improving the solution vector to minimize the residual error. It involves calculating the residual vector and adjusting the approximation to reduce this residual to zero through a series of iterations. Although it is a fundamental component of linear algebra with applications in various fields, the relaxation method is typically slower than other solution methods and serves more as a preconditioning technique.Numerical solutions for linear system of equations
Numerical solutions for linear system of equationsMohamed Mohamed El-Sayed
油
This document discusses numerical methods for solving linear systems of equations. It begins by introducing linear systems in general and matrix forms, and classifying them as homogeneous or non-homogeneous. It then discusses checking for consistency. The main methods covered for obtaining solutions are: Gauss elimination, Gauss-Jordan elimination, using the inverse matrix if it exists, and iterative techniques. Specific examples are provided to demonstrate how to apply Gauss elimination, Gauss-Jordan elimination and using the inverse matrix to solve sample systems.Participaci坦n semana 13
Participaci坦n semana 13KimberlymoshaR
油
1) Solve the given differential equation if y(1) = 100. The value of y(3) is 40342.85.
2) Solve the given linear differential equation if y(0) = 1. The value of y(1) is 0.999 1.CALCULATING DEVICES
CALCULATING DEVICESshahzadebaujiti
油
This document provides examples of how to perform various calculations using a scientific calculator. It covers addition, subtraction, multiplication, division, powers, roots, trigonometric functions, logarithms, conversions between degrees and radians, and statistical calculations including mean, median, and standard deviation. The document includes example calculations and questions for the reader to practice these calculator functions. GAUSS ELIMINATION METHOD
GAUSS ELIMINATION METHODreach2arkaELECTRICAL
油
The Gauss-Elemination method is used to solve systems of linear equations by reducing the system to upper triangular form using elementary row operations. It works by first making the coefficients of the variables above the main diagonal equal to zero one by one, then back-substituting the solutions. The method is illustrated using a 3x3 system that is reduced to upper triangular form by subtracting appropriate multiples of rows from each other. The unique solution can then be found by back-substituting the values of z, y, and x.Gauss elimination & Gauss Jordan method
Gauss elimination & Gauss Jordan methodNaimesh Bhavsar
油
This document discusses methods for solving systems of linear equations, including the traditional method, matrix method, row echelon method, Gauss elimination method, and Gauss Jordan method. It provides examples working through solving systems of equations using Gauss elimination and Gauss Jordan. The key steps of each method like constructing the augmented matrix, row operations, and back substitution are demonstrated. Related fields where linear algebra is applied are also listed.Math
Mathayman_alawin
油
1. The document contains a series of calculations involving addition, subtraction, multiplication and division.
2. The numbers become increasingly larger as the calculations progress.
3. The final results are long strings of digits that appear to have no obvious meaning on their own. LU FACTORIZATION METHOD
LU FACTORIZATION METHODreach2arkaELECTRICAL
油
The LU factorization method decomposes a matrix A into the product of a lower triangular matrix L and an upper triangular matrix U. It can be used to solve systems of linear equations of the form Ax=b. The document provides an example of using LU factorization to solve the system of equations: 8x - 3y + 2z = 20, 4x + 11y - z = 33, 6x + 3y + 12z = 36. The matrices L and U are calculated from the original coefficient matrix A. Then the system Ly=b is solved for y, and Ux=y is solved for x to obtain the solution x=3, y=2, z=1.Term paper
Term paperAbhishek Kumar
油
The document discusses implementing the Gauss-Jacobi iterative method to solve systems of linear equations. It begins by providing an overview of the Gauss-Jacobi method and its application to solve a sample system of 3 equations with 3 unknowns. It then compares the Gauss-Jacobi method to the Gauss-Seidel method, noting that Gauss-Seidel uses updated values in the current iteration while Gauss-Jacobi uses values from the previous iteration. The document concludes by providing C code to implement the Gauss-Jacobi method and listing its main advantages as being iterative, and its disadvantages as being inflexible and requiring large set-up time.08 interpolation lagrange
08 interpolation lagrangeMohammad Tawfik
油
This document contains lecture slides from an ENEM602 engineering mathematics course taught by Dr. Eng. Mohammad Tawfik in Spring 2007. It discusses Lagrange interpolation, which is a method for interpolating a polynomial that passes through a set of points. The document provides examples of using Lagrange interpolation for 2, 3 and 4 points. It derives the general Lagrange interpolation formula and shows an example of applying it to find a 3rd order polynomial to interpolate 4 given data points. Students are assigned homework problems applying Lagrange interpolation.Numerical Methods: curve fitting and interpolation
Numerical Methods: curve fitting and interpolationNikolai Priezjev
油
This document discusses curve fitting and interpolation techniques. It covers linear regression using the least squares method to fit data to a straight line model. It also discusses fitting data to other functions like exponential, logarithmic and polynomial models. For polynomial regression, a second order polynomial is presented which requires solving a system of equations to determine the coefficients that minimize the residual errors between measured and modeled data. An example demonstrates applying these methods to find the coefficients for a straight line and second order polynomial fit to sample data sets.Jacobi method
Jacobi methodGrishma Maravia
油
The document discusses the solution of a system of linear equations using Jacobi's method, providing detailed iterations and approximations. It includes multiple examples with starting values, iterative results, and convergence to the final solution. The text also presents programming code to implement the Jacobi method for solving the equations.Penyajian Data Dalam Bentuk Tabel Mtk
Penyajian Data Dalam Bentuk Tabel Mtkgalih
油
The document discusses linear regression analysis performed on a dataset with variables X and Y. It shows the dataset with X and Y values, plots the data in polynomial, exponential, and linear graphs, and performs manual calculations to derive the linear regression equation. The calculations show setting up and solving simultaneous equations to find the coefficients a, b, and c, yielding the final regression equation Y = 0.7X^2 + 0.25X + 8.27.Bessel 1 div_3
Bessel 1 div_3Martin Gallardo
油
The document solves the Bessel equation with order v = 1/3. It finds two partial solutions by expanding the general solution as a power series and solving the characteristic equation. The first partial solution is a power series with terms of x^(2k+1/3) and the second partial solution is a power series with terms of x^(2k-1/3). The general solution is the linear combination of these two partial solutions.Iterative methods
Iterative methodsrubenarismendi
油
The document discusses two iterative methods for solving systems of linear equations: Jacobi and Gauss-Seidel. It provides an example of applying each method to solve a 3x3 matrix. Jacobi method calculates each variable based on the previous iteration while Gauss-Seidel uses the most recent value calculated. The example shows Gauss-Seidel converges to the solution in fewer iterations, making it more efficient than Jacobi method.Pr叩ctica 6 soluci坦n con anotaciones (1)
Pr叩ctica 6 soluci坦n con anotaciones (1)KimberlymoshaR
油
The document contains three problems related to calculus.
The first problem involves calculating the work required to pump water from a drainage pipe. It finds the work in both the cylindrical and conical sections of the pipe using integrals, getting a total work of 115569.07 J.
The second problem calculates the hydrostatic force on a triangular drainage plate in a pool using integrals, getting a force of 2142.6 N.
The third problem involves calculating areas under curves over various intervals using integrals, with answers of 1.44 for part (a) and infinity for part (b) of the first question, and 0.91 and infinity for the second question.Stats
Statsjatinder194jatin
油
Kushal should draw the following conclusion from the sample data:
The coefficient of correlation (R) between size (x) and price (y) is 0.6725. The coefficient of determination (R2) is 0.452. This indicates that the relationship between size and price is moderately correlated.2019 numbering systems decimal binary octal hexadecimal
2019 numbering systems decimal binary octal hexadecimalOsama Ghandour Geris
油
The 80 minute computer science class will include:
- A 5 minute warm up
- Two 10 minute student presentations
- Three 5 minute sessions on converting between decimal and other numbering systems (binary, octal, hexadecimal)
- A 10 minute session on converting between all numbering systems
- A 2 minute discussion of classroom normsCollege algebra in context 5th edition harshbarger solutions manual
College algebra in context 5th edition harshbarger solutions manualAnnuzzi19
油
The document discusses solutions to exercises from a College Algebra textbook. It provides the steps to solve 16 different math equations involving variables like x and t. The equations use concepts like linear models, properties of equality, and combining like terms. Solutions are found by applying division, multiplication, addition or subtraction properties, or graphing the equations to find the intersection point of the lines.Computational Dynamics edited
Computational Dynamics editedJesan Alexis Morales
油
This document summarizes computational dynamics methods for solving pendulum, particle, and rod problems numerically. It examines the forward and backward Euler methods, Newton's method, linear and nonlinear state-space representations, and the generalized alpha method. For the particle problem, it analyzes damping and applies the forward Euler, backward Euler, and Newton's methods. It compares error rates with different step sizes for the forward Euler method. The generalized alpha method is explored in detail, analyzing its properties and the effects of varying its alpha, beta, and gamma parameters.2019 numbering systems decimal binary octal hexadecimal
2019 numbering systems decimal binary octal hexadecimalOsama Ghandour Geris
油
The computer science teacher's 80-minute class will include:
- A 5-minute warm up
- Three 10-minute student presentations
- Five 5-minute sessions converting between decimal, binary, octal, and hexadecimal numbering systems
- Two 2-minute classroom norm and exit ticket activities
The class will cover converting between decimal and other numbering systems, with students splitting into groups to create tables for the different bases. Homework includes developing a flowchart and HTML page for number conversions.powerpoint Dokumen transaksi kelompok 5 xi ak 2
powerpoint Dokumen transaksi kelompok 5 xi ak 2Nenx Yeniie
油
Dokumen tersebut berisi ringkasan tentang tugas dokumen transaksi perusahaan PD. Masbuloh yang mencakup informasi perusahaan, dokumen transaksi, dan jurnal khusus.Hasil revisi makalah_tonni_limbong-libre
Hasil revisi makalah_tonni_limbong-libreNanang Harianto
油
Dokumen tersebut menjelaskan penerapan metode Simple Additive Weighting (SAW) untuk memilih pekerjaan bidang informatika yang sesuai berdasarkan nilai akademik alumni. Metode SAW melibatkan proses normalisasi matriks keputusan dan penjumlahan terbobot untuk menentukan skor setiap alternatif pekerjaan. Tujuannya agar alumni dapat memilih pekerjaan sesuai kemampuan masing-masing.More Related Content
What's hot (20)
Numerical solutions for linear system of equations
Numerical solutions for linear system of equationsMohamed Mohamed El-Sayed
油
This document discusses numerical methods for solving linear systems of equations. It begins by introducing linear systems in general and matrix forms, and classifying them as homogeneous or non-homogeneous. It then discusses checking for consistency. The main methods covered for obtaining solutions are: Gauss elimination, Gauss-Jordan elimination, using the inverse matrix if it exists, and iterative techniques. Specific examples are provided to demonstrate how to apply Gauss elimination, Gauss-Jordan elimination and using the inverse matrix to solve sample systems.Participaci坦n semana 13
Participaci坦n semana 13KimberlymoshaR
油
1) Solve the given differential equation if y(1) = 100. The value of y(3) is 40342.85.
2) Solve the given linear differential equation if y(0) = 1. The value of y(1) is 0.999 1.CALCULATING DEVICES
CALCULATING DEVICESshahzadebaujiti
油
This document provides examples of how to perform various calculations using a scientific calculator. It covers addition, subtraction, multiplication, division, powers, roots, trigonometric functions, logarithms, conversions between degrees and radians, and statistical calculations including mean, median, and standard deviation. The document includes example calculations and questions for the reader to practice these calculator functions. GAUSS ELIMINATION METHOD
GAUSS ELIMINATION METHODreach2arkaELECTRICAL
油
The Gauss-Elemination method is used to solve systems of linear equations by reducing the system to upper triangular form using elementary row operations. It works by first making the coefficients of the variables above the main diagonal equal to zero one by one, then back-substituting the solutions. The method is illustrated using a 3x3 system that is reduced to upper triangular form by subtracting appropriate multiples of rows from each other. The unique solution can then be found by back-substituting the values of z, y, and x.Gauss elimination & Gauss Jordan method
Gauss elimination & Gauss Jordan methodNaimesh Bhavsar
油
This document discusses methods for solving systems of linear equations, including the traditional method, matrix method, row echelon method, Gauss elimination method, and Gauss Jordan method. It provides examples working through solving systems of equations using Gauss elimination and Gauss Jordan. The key steps of each method like constructing the augmented matrix, row operations, and back substitution are demonstrated. Related fields where linear algebra is applied are also listed.Math
Mathayman_alawin
油
1. The document contains a series of calculations involving addition, subtraction, multiplication and division.
2. The numbers become increasingly larger as the calculations progress.
3. The final results are long strings of digits that appear to have no obvious meaning on their own. LU FACTORIZATION METHOD
LU FACTORIZATION METHODreach2arkaELECTRICAL
油
The LU factorization method decomposes a matrix A into the product of a lower triangular matrix L and an upper triangular matrix U. It can be used to solve systems of linear equations of the form Ax=b. The document provides an example of using LU factorization to solve the system of equations: 8x - 3y + 2z = 20, 4x + 11y - z = 33, 6x + 3y + 12z = 36. The matrices L and U are calculated from the original coefficient matrix A. Then the system Ly=b is solved for y, and Ux=y is solved for x to obtain the solution x=3, y=2, z=1.Term paper
Term paperAbhishek Kumar
油
The document discusses implementing the Gauss-Jacobi iterative method to solve systems of linear equations. It begins by providing an overview of the Gauss-Jacobi method and its application to solve a sample system of 3 equations with 3 unknowns. It then compares the Gauss-Jacobi method to the Gauss-Seidel method, noting that Gauss-Seidel uses updated values in the current iteration while Gauss-Jacobi uses values from the previous iteration. The document concludes by providing C code to implement the Gauss-Jacobi method and listing its main advantages as being iterative, and its disadvantages as being inflexible and requiring large set-up time.08 interpolation lagrange
08 interpolation lagrangeMohammad Tawfik
油
This document contains lecture slides from an ENEM602 engineering mathematics course taught by Dr. Eng. Mohammad Tawfik in Spring 2007. It discusses Lagrange interpolation, which is a method for interpolating a polynomial that passes through a set of points. The document provides examples of using Lagrange interpolation for 2, 3 and 4 points. It derives the general Lagrange interpolation formula and shows an example of applying it to find a 3rd order polynomial to interpolate 4 given data points. Students are assigned homework problems applying Lagrange interpolation.Numerical Methods: curve fitting and interpolation
Numerical Methods: curve fitting and interpolationNikolai Priezjev
油
This document discusses curve fitting and interpolation techniques. It covers linear regression using the least squares method to fit data to a straight line model. It also discusses fitting data to other functions like exponential, logarithmic and polynomial models. For polynomial regression, a second order polynomial is presented which requires solving a system of equations to determine the coefficients that minimize the residual errors between measured and modeled data. An example demonstrates applying these methods to find the coefficients for a straight line and second order polynomial fit to sample data sets.Jacobi method
Jacobi methodGrishma Maravia
油
The document discusses the solution of a system of linear equations using Jacobi's method, providing detailed iterations and approximations. It includes multiple examples with starting values, iterative results, and convergence to the final solution. The text also presents programming code to implement the Jacobi method for solving the equations.Penyajian Data Dalam Bentuk Tabel Mtk
Penyajian Data Dalam Bentuk Tabel Mtkgalih
油
The document discusses linear regression analysis performed on a dataset with variables X and Y. It shows the dataset with X and Y values, plots the data in polynomial, exponential, and linear graphs, and performs manual calculations to derive the linear regression equation. The calculations show setting up and solving simultaneous equations to find the coefficients a, b, and c, yielding the final regression equation Y = 0.7X^2 + 0.25X + 8.27.Bessel 1 div_3
Bessel 1 div_3Martin Gallardo
油
The document solves the Bessel equation with order v = 1/3. It finds two partial solutions by expanding the general solution as a power series and solving the characteristic equation. The first partial solution is a power series with terms of x^(2k+1/3) and the second partial solution is a power series with terms of x^(2k-1/3). The general solution is the linear combination of these two partial solutions.Iterative methods
Iterative methodsrubenarismendi
油
The document discusses two iterative methods for solving systems of linear equations: Jacobi and Gauss-Seidel. It provides an example of applying each method to solve a 3x3 matrix. Jacobi method calculates each variable based on the previous iteration while Gauss-Seidel uses the most recent value calculated. The example shows Gauss-Seidel converges to the solution in fewer iterations, making it more efficient than Jacobi method.Pr叩ctica 6 soluci坦n con anotaciones (1)
Pr叩ctica 6 soluci坦n con anotaciones (1)KimberlymoshaR
油
The document contains three problems related to calculus.
The first problem involves calculating the work required to pump water from a drainage pipe. It finds the work in both the cylindrical and conical sections of the pipe using integrals, getting a total work of 115569.07 J.
The second problem calculates the hydrostatic force on a triangular drainage plate in a pool using integrals, getting a force of 2142.6 N.
The third problem involves calculating areas under curves over various intervals using integrals, with answers of 1.44 for part (a) and infinity for part (b) of the first question, and 0.91 and infinity for the second question.Stats
Statsjatinder194jatin
油
Kushal should draw the following conclusion from the sample data:
The coefficient of correlation (R) between size (x) and price (y) is 0.6725. The coefficient of determination (R2) is 0.452. This indicates that the relationship between size and price is moderately correlated.2019 numbering systems decimal binary octal hexadecimal
2019 numbering systems decimal binary octal hexadecimalOsama Ghandour Geris
油
The 80 minute computer science class will include:
- A 5 minute warm up
- Two 10 minute student presentations
- Three 5 minute sessions on converting between decimal and other numbering systems (binary, octal, hexadecimal)
- A 10 minute session on converting between all numbering systems
- A 2 minute discussion of classroom normsCollege algebra in context 5th edition harshbarger solutions manual
College algebra in context 5th edition harshbarger solutions manualAnnuzzi19
油
The document discusses solutions to exercises from a College Algebra textbook. It provides the steps to solve 16 different math equations involving variables like x and t. The equations use concepts like linear models, properties of equality, and combining like terms. Solutions are found by applying division, multiplication, addition or subtraction properties, or graphing the equations to find the intersection point of the lines.Computational Dynamics edited
Computational Dynamics editedJesan Alexis Morales
油
This document summarizes computational dynamics methods for solving pendulum, particle, and rod problems numerically. It examines the forward and backward Euler methods, Newton's method, linear and nonlinear state-space representations, and the generalized alpha method. For the particle problem, it analyzes damping and applies the forward Euler, backward Euler, and Newton's methods. It compares error rates with different step sizes for the forward Euler method. The generalized alpha method is explored in detail, analyzing its properties and the effects of varying its alpha, beta, and gamma parameters.2019 numbering systems decimal binary octal hexadecimal
2019 numbering systems decimal binary octal hexadecimalOsama Ghandour Geris
油
The computer science teacher's 80-minute class will include:
- A 5-minute warm up
- Three 10-minute student presentations
- Five 5-minute sessions converting between decimal, binary, octal, and hexadecimal numbering systems
- Two 2-minute classroom norm and exit ticket activities
The class will cover converting between decimal and other numbering systems, with students splitting into groups to create tables for the different bases. Homework includes developing a flowchart and HTML page for number conversions.Viewers also liked (18)
powerpoint Dokumen transaksi kelompok 5 xi ak 2
powerpoint Dokumen transaksi kelompok 5 xi ak 2Nenx Yeniie
油
Dokumen tersebut berisi ringkasan tentang tugas dokumen transaksi perusahaan PD. Masbuloh yang mencakup informasi perusahaan, dokumen transaksi, dan jurnal khusus.Hasil revisi makalah_tonni_limbong-libre
Hasil revisi makalah_tonni_limbong-libreNanang Harianto
油
Dokumen tersebut menjelaskan penerapan metode Simple Additive Weighting (SAW) untuk memilih pekerjaan bidang informatika yang sesuai berdasarkan nilai akademik alumni. Metode SAW melibatkan proses normalisasi matriks keputusan dan penjumlahan terbobot untuk menentukan skor setiap alternatif pekerjaan. Tujuannya agar alumni dapat memilih pekerjaan sesuai kemampuan masing-masing.Prezent
PrezentVladimir Vorobyov
油
仂从仄亠仆 仗仂于亠仆 亠亠于仂亶 亞仂仂于仆仂亳 亟亠亠亶 从 从仂仍亠, 仗仂亟亠从亳于舒, 仂 于舒亢仆仂 仆亠 仂仍从仂 仆舒仍亳亳亠 亰仆舒仆亳亶, 仆仂 亳 仂于亠仆 亠亠于仂亞仂 舒亰于亳亳. 仆仂于仆亠 舒仗亠从, 仆舒 从仂仂亠 仍亠亟亠 仂弍舒舒 于仆亳仄舒仆亳亠 仂亟亳亠仍礆, 于从仍ﾑ舒ﾑ 仗舒于亳仍仆仂亠 仗仂亳亰仆仂亠仆亳亠, 亰于从仂于仂亶 舒仆舒仍亳亰, 仍仂于仂仂弍舒亰仂于舒仆亳亠 亳 亞舒仄仄舒亳从. 仂仄亠 仂亞仂, 亟仂从仄亠仆 亠从仂仄亠仆亟亠 仂弍舒舒 从 仍仂亞仂仗亠亟 于 仍舒亠 亟仆仂亠亶 于 亠亠于仂仄 舒亰于亳亳亳 亠弍亠仆从舒.Software Development Life Cycle (SDLC)
Software Development Life Cycle (SDLC)HASEEB MUGHAL
油
The document discusses the Software Development Life Cycle (SDLC), which is a process that consists of detailed planning for developing, maintaining, replacing, and enhancing software within an organization. The SDLC defines a methodology with phases including planning, analysis, design, implementation, and testing/maintenance. The planning phase involves feasibility studies and creating a project plan. The analysis phase breaks down requirements and gathers stakeholder needs. The design phase determines if development is internal or outsourced. The implementation phase builds, tests, and trains users on the new software. Testing and maintenance identifies and fixes bugs while accommodating new requirements.仂从亶 仂从 "亠亳仆舒 亰舒仗亳 亟仂弍仆 亳亠仍"
仂从亶 仂从 "亠亳仆舒 亰舒仗亳 亟仂弍仆 亳亠仍"Vladimir Vorobyov
油
仂从仄亠仆 仗亠亟舒于仍磳 仂弍仂亶 亠仆仂仍仂亞亳亠从 从舒 仂从舒 仄舒亠仄舒亳从亳 亟仍 5 从仍舒舒 仆舒 亠仄 束亟亠亳仆舒 亰舒仗亳 亟仂弍仆 亳亠仍損. 仆亠仄 仂亟亠亢舒 亰舒亟舒仆亳, 舒仍亞仂亳仄 亳 亳亰从仍仄亳仆从亳, 仆舒仗舒于仍亠仆仆亠 仆舒 舒亰于亳亳亠 仆舒于从仂于 舒弍仂 亟亠亳仆仄亳 亟仂弍礆亳. 仂仄亠 仂亞仂, 于从仍ﾑ亠仆 仗亳仄亠 亳 仗舒亢仆亠仆亳 亟仍 仗仂于亠从亳 于仂亠仆仆 亰仆舒仆亳亶.Pernyataan dan bukan pernyatan
Pernyataan dan bukan pernyatanNenx Yeniie
油
Dokumen ini membahas tentang jenis-jenis kalimat seperti kalimat berarti, tak berarti, deklaratif, deklaratif faktual, dan nondeklaratif. Kalimat didefinisikan sebagai rangkaian kata yang tersusun dengan aturan bahasa dan memiliki arti. Pertanyaan hanya bernilai benar atau salah, sedangkan bukan pernyataan tidak dapat ditentukan kebenarannya atau bersifat relatif.仗亠亰亠仆舒亳 从舒 仂从 仄仂从仂于从舒 仂弍仍舒
仗亠亰亠仆舒亳 从舒 仂从 仄仂从仂于从舒 仂弍仍舒Vladimir Vorobyov
油
仂仄仗仍亠从仆亶 亠弍仆亶 从 '仆仂于 亠仍亳亞亳仂亰仆 从仍 亳 于亠从仂亶 亳从亳' 仆舒仗舒于仍亠仆 仆舒 仂仄亳仂于舒仆亳亠 从仂仍仆亳从仂于 仗亠亟舒于仍亠仆亳亶 仂 仆舒于于亠仆仆 亳亟亠舒仍舒 亳 亠仆仆仂, 仂舒于仍ﾑ亳 仂仆仂于 仄仆仂亞仂仆舒亳仂仆舒仍仆仂亶 从仍 仂亳亳. 于从仍ﾑ舒亠 亠 仄仂亟仍亠亶, 仂于舒于舒ﾑ亳 仗舒于仂仍舒于仆, 亳仍舒仄从, 弍亟亟亳亶从 亳 亳亟亠亶从 从仍, 舒 舒从亢亠 于亠从 亳从, 亠仍 舒亰于亳亳 于舒亢亠仆亳 亳 仗仂仆亳仄舒仆亳 从仍仆 亳 亠仍亳亞亳仂亰仆 舒亟亳亳亶. 亠仗仂亟舒于舒仆亳亠 从舒 仆舒仆亠 2012 亞仂亟舒 于仂 于亠 仂弍舒亰仂于舒亠仍仆 亠亢亟亠仆亳 仂亳亳 亳 仗亠亟仗仂仍舒亞舒亠 仄亠亢仗亠亟仄亠仆亠 于磶亳 亟亞亳仄亳 亞仄舒仆亳舒仆仄亳 仗亠亟仄亠舒仄亳.Lecture 3-program-linier3
Lecture 3-program-linier3Nanang Harianto
油
Dokumen tersebut membahas tentang program linier yang merupakan salah satu teknik penyelesaian riset operasi untuk menyelesaikan masalah optimasi dengan fungsi tujuan dan kendala yang linier. Dibahas pula definisi, bentuk standar, formulasi, dan penyelesaian program linier menggunakan metode grafik dan matriks beserta contoh soalnya."亠亰亠仆舒亳 亟仍 舒仆亳从仂于 亅 2013
亠亰亠仆舒亳 亟仍 舒仆亳从仂于 亅 2013Vladimir Vorobyov
油
仂从仄亠仆 仂仗亳于舒亠 仗仂亟亞仂仂于亳亠仍仆亠 仄亠仂仗亳亳 亳 仗仂亠 仗仂于亠亟亠仆亳 亟亳仆仂亞仂 亞仂亟舒于亠仆仆仂亞仂 从亰舒仄亠仆舒 (亅) 亟仍 于仗从仆亳从仂于 舒亰仍亳仆 仂弍舒亰仂于舒亠仍仆 亠亢亟亠仆亳亶, 于从仍ﾑ舒 亠弍仂于舒仆亳 从 舒仆亳从舒仄, 仗仂亟仂仍亢亳亠仍仆仂 从亰舒仄亠仆仂于 仗仂 仗亠亟仄亠舒仄 亳 舒仗亳舒仆亳亠. 丕舒仆亳从亳 亟仂仍亢仆 仂弍亠仗亠亳 仆舒仍亳亳亠 仆亠仂弍仂亟亳仄 亟仂从仄亠仆仂于 亳 仍亠亟仂于舒 舒仆仂于仍亠仆仆仄 仗舒于亳仍舒仄 于仂 于亠仄 从亰舒仄亠仆舒, 于从仍ﾑ舒 亰舒仗亠亠仆亳亠 亳仗仂仍亰仂于舒仆亳 仄仂弍亳仍仆 仂亶于 亳 亟亞亳亠 仂亞舒仆亳亠仆亳. 丐舒从亢亠 仗亳于仂亟 亳仆从亳亳 仗仂 仗仂亟舒亠 舒仗亠仍仍亳亶 亳 仍仂于亳礆 仗仂于仂仆仂亶 亟舒亳 亅.亳仄亠仆亠仆亳亠 仄亠仂亟舒
亳仄亠仆亠仆亳亠 仄亠仂亟舒Vladimir Vorobyov
油
仂从仄亠仆 仂弍亢亟舒亠 仗亳仄亠仆亠仆亳亠 亠仆仂仍仂亞亳亳 舒亰于亳亳 从亳亳亠从仂亞仂 仄仍亠仆亳 亠亠亰 亠仆亳亠 亳 仗亳仄仂 于 仂从舒 仄舒亠仄舒亳从亳 从舒从 亠亟于仂 亠舒仍亳亰舒亳亳 个亠亟亠舒仍仆仂亞仂 亞仂亟舒于亠仆仆仂亞仂 仂弍舒亰仂于舒亠仍仆仂亞仂 舒仆亟舒舒 (个弌). 仆仂于仆仂亠 于仆亳仄舒仆亳亠 亟亠仍磳 亠仍礆, 亰舒亟舒舒仄 亳 仄亠仂亟舒仄, 仆舒仗舒于仍亠仆仆仄 仆舒 舒亰于亳亳亠 仄仍亳亠仍仆 仆舒于从仂于 舒亳, 舒从亳 从舒从 从亳亳亠从仂亠 仄仍亠仆亳亠, 仗舒仄 亳 舒仆舒仍亳亳亠从亳亠 仗仂仂弍仆仂亳. 仗亳于舒ﾑ 亳仗仂仍亰亠仄亠 亠仆仂仍仂亞亳亠从亳亠 仗亳亠仄 亳 从舒 仂从舒, 从仂仂亠 仗仂仂弍于ﾑ 舒从亳于仆仂仄 于仂于仍亠亠仆亳 舒亳 于 仗仂亠 仂弍亠仆亳.Ginanjar abdurrahman 2
Ginanjar abdurrahman 2Nanang Harianto
油
Skripsi ini membahas penerapan metode Tsukamoto (logika fuzzy) dalam sistem pendukung keputusan untuk menentukan jumlah produksi barang berdasarkan data persediaan dan permintaan. Penulis, Ginanjar Abdurrahman, menyampaikan terima kasih kepada berbagai pihak yang telah mendukung penyelesaian skripsi ini, yang disusun untuk memenuhi syarat gelar sarjana sains di Universitas Negeri Yogyakarta. Skripsi ini diharapkan dapat memberikan manfaat bagi para pembaca.Jenis jenis multimedia menurut imk
Jenis jenis multimedia menurut imkNanang Harianto
油
1. Dokumen tersebut membahas tentang storyboard, prototyping, realitas virtual, dan realitas tertambah. Storyboard digunakan untuk menggambarkan alur cerita secara visual, sedangkan prototyping merupakan metode pengembangan perangkat lunak interaktif yang memungkinkan pelanggan berinteraksi selama proses pembuatan sistem. Realitas virtual dan realitas tertambah merupakan teknologi yang memungkinkan pengguna berinteraksi dengan lingkungan simulasi dan lingkungan nyataSurat edaran
Surat edaranNenx Yeniie
油
Dokumen ini adalah surat edaran yang disusun sebagai tugas mata pelajaran bahasa Indonesia oleh kelompok siswa. Surat edaran berfungsi sebagai pemberitahuan tertulis yang ditujukan kepada banyak pihak dan terdapat beberapa macam serta bagian-bagian yang harus ada di dalamnya. Contoh surat edaran diakhiri dengan harapan agar informasi yang disampaikan bermanfaat bagi penerima.Tugas kelompok Bunga Majemuk dan Diskonto
Tugas kelompok Bunga Majemuk dan DiskontoNenx Yeniie
油
Dokumen ini merupakan tugas kelompok matematika tentang bunga majemuk dan diskonto untuk kelas XII akuntansi. Terdapat penjelasan mengenai rumus dan contoh perhitungan bunga majemuk, termasuk nilai akhir dan nilai tunai. Penulis mencantumkan nama-nama anggota kelompok yang terlibat dalam tugas ini.Tugas B.Indonesia PP Surat Pesanan
Tugas B.Indonesia PP Surat PesananNenx Yeniie
油
Dokumen ini adalah tugas tentang surat pesanan yang dibuat oleh kelompok 3 XII AK 2, menjelaskan pengertian, isi, dan cara menyusun surat pesanan. Contoh surat pesanan diberikan melalui skenario PD. Keep Smile yang memesan barang dari PT. Bintang Mas setelah menerima surat penawaran. Beberapa pertanyaan diskusi juga diajukan terkait proses pembuatan surat pesanan.Extension principle
Extension principleSavo Deli
油
The document discusses the extension principle for generalizing crisp mathematical concepts to fuzzy sets. It defines the extension principle for mappings from cartesian products to universes. An example is provided to illustrate defining a fuzzy set in the output universe based on fuzzy sets in the input universes and the mapping between them. Fuzzy numbers are defined to have specific properties including being a normal fuzzy set, closed intervals for membership levels, and bounded support. Positive and negative fuzzy numbers are distinguished based on their membership functions. Binary operations are classified as increasing or decreasing, and it is noted the extension principle can be used to define the fuzzy result of applying increasing or decreasing operations to fuzzy inputs. Notation for fuzzy number algebraic operations is introduced. Several theoreIsi Proposal Peringatan maulid nabi muhammad saw
Isi Proposal Peringatan maulid nabi muhammad sawNenx Yeniie
油
Dokumen tersebut merupakan proposal untuk peringatan Maulid Nabi di Desa Bojongsoang yang mencakup latar belakang pentingnya peringatan Maulid Nabi, tema, tujuan, waktu dan tempat pelaksanaan, sasaran, bentuk kegiatan, panitia, sumber dana, anggaran, dan penutup harapan.舒仂仆亶 亠仄亳仆舒 亟仍 仂亟亳亠仍亠亶
舒仂仆亶 亠仄亳仆舒 亟仍 仂亟亳亠仍亠亶Vladimir Vorobyov
油
仂从仄亠仆 仂弍亢亟舒亠 仂仂弍亠仆仆仂亳 仆从亳仂仆舒仍仆仂亶 仄亠亢仗仂仍舒仆仂亶 舒亳仄仄亠亳亳 仄仂亰亞舒 亳 亠 于仍亳礌亳亠 仆舒 仂弍亠仆亳亠 亟亠亠亶. 舒仄舒亳于舒ﾑ 舒亰仍亳亳 于 亟仂仄亳仆亳仂于舒仆亳亳 仗仂仍舒亳亶, 亳 仂仍 于 cognitiv仆 仗仂亠舒, 仄仂亳仂仆舒仍仆仂仄 于舒亢亠仆亳亳 亳 仗仂亟仂亟舒 从 仂弍亠仆亳. 仂亟亠从亳于舒亠 于舒亢仆仂 亳仆亟亳于亳亟舒仍亳亰亳仂于舒仆仆仂亞仂 仗仂亟仂亟舒 从 仂弍亠仆亳 亠仂仄 仄亠亢仗仂仍舒仆仂亶 舒亳仄仄亠亳亳 亟仍 仗亠亟仂于舒亠仆亳 仆亠于仂亰仂于 亳 亟仂亳亢亠仆亳 亠从亳于仆 亠弍仆 亠亰仍舒仂于.Ad
Regresi linear
- 1. Nama: Nanang harianto Nim :011101503125015 Regresi linear Jumlah data m=6 orde polynomial n=2 X1 = 0 Y1=2.1 X2 = 1 Y1=7.7 X3 = 2 Y1=13.6 X4 = 3 Y1= 17.2 X5 = 4 Y1= 40.9 X6 = 5 Y1=61.1 y = 11.37x - 4.676 R族 = 0.884 -10 0 10 20 30 40 50 60 70 0 1 2 3 4 5 6 sumbuy sumbu x regresi linear Series1 Linear (Series1)
- 2. NO X Y 1 0 2.1 2 1 7.7 3 2 13.6 4 3 17.2 5 4 40.9 6 5 61.1 15 142.6 m mxii=1 a0 myii=1 = mxii=1 mxi2 i=1 a1 mxi*yii=1 6 15 55 a0 142.6 15 55 225 a1 = 555.6 55 225 979 a2 2398.8 6a0 + 15a1 +55a2 = 142.6 15a0+55a1 +225a2 = 555.6 55a0+225a1+979 2398.8 _