blind (or group-blind) linear multiuser detection‚ the detector is estimated from the received signals‚ with the prior knowledge of only the signature waveform of the desired user (or the signature waveforms of some but not all users). The performance of a number of such estimated linear detectors‚ including the direct-matrix-inversion (DMI) blind linear minimum mean square error (MMSE) detector‚ the subspace blind linear MMSE detector‚ and the form-I and form-II group-blind linear hybrid detectors‚ are
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Linear Regression Models 1 SPSS for Windows® Intermediate & Advanced Applied Statistics Zayed University Office of Research SPSS for Windows® Workshop Series Presented by Dr. Maher Khelifa Associate Professor Department of Humanities and Social Sciences College of Arts and Sciences © Dr. Maher Khelifa 2 Bi-variate Linear Regression (Simple Linear Regression) © Dr. Maher Khelifa Understanding Bivariate Linear Regression 3 Many statistical indices summarize information about
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Be Studied By Residual 1. The regression function is not linear. 2. The error terms do not have constant variance. 3. The error terms are not independent. 4. The model fits all but one or few outliers‚ 5. The error terms are not normally distributed. 6. One or several important predictor(s) have been omitted from the model. Diagnostic For Residuals Six diagnostic plots to judge departure from the simple linear regression model * Plot of residuals against predictor variable
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5. INTRODUCTION TO LINEAR PROGRAMMING (LP) Learning Objectives 1. Obtain an overview of the kinds of problems linear programming has been used to solve. 2. Learn how to develop linear programming models for simple problems. 3. Be able to identify the special features of a model that make it a linear programming model. 4. Learn how to solve two variable linear programming models by the graphical solution procedure. 5. Understand the importance of extreme points in
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from those of linear programming. Integer programming is concerned with optimization problems in which some of the variables are required to take on discrete values. Rather than allow a variable to assume all real values in a given range‚ only predetermined discrete values within the range are permitted. In most cases‚ these values are the integers‚ giving rise to the name of this class of models. Models with integer variables are very useful. Situations that cannot be modeled by linear programming
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the demand for the product. The consultant should also describe the methodology of a multiple linear regression and its purpose in estimating a demand function. The consultant should then run a multiple linear regression in linear and multiplicative forms based on the data provided by the company and report on the estimated result. They will have to evaluate the estimated demand equations both in linear and multiplicative forms‚ select the one‚ which can best describe the consumption. The consultant
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Math Exam Notes Unit 1 The Method of Substitution -Solving a linear system by substituting for one variable from one equation into the other equation -To solve a linear system by substitution: Step 1: Solve one of the equations for one variable in terms of the other variable Step 2: Substitute the expression from step 1 into the other equation and solve for the remaining variable Step 3: Substitute back into one of the original equations to find the value of the other variable Step
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Invertible matrix 1 Invertible matrix In linear algebra an n-by-n (square) matrix A is called invertible or nonsingular or nondegenerate‚ if there exists an n-by-n matrix B such that where I n denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication. If this is the case‚ then the matrix B is uniquely determined by A and is called the inverse of A‚ denoted by A −1 . It follows from the theory of matrices that if for finite square matrices A and B
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Program & Batch: | PGDM 2013-15 | Term: | I | Course Name: | BUSINESS STATISTICS | Name of the faculty: | GUNJAN MALHOTRA | Topic/ Title : | THE IMPACT OF INFLATION RATE AND INTEREST RATE ON REAL GROSS DOMESTIC PRODUCT OF INDIA | Original or Revised Write-up: | ORIGINAL | Group Number: | 8 | Contact No. and email of Group Coordinator: | 9999864740ft13himanshuarora@imt.ac.in | Group Members: | Sl. | Roll No. | Name | | 1 | 1301-528 | HIMANSHU ARORA | | 2 | 1301-058 | CHITTRESH
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Assignment on Operations Research Transportation Model INTRODUCTION Many practical problems in operations research can be broadly formulated as linear programming problems‚ for which the simplex this is a general method and cannot be used for specific types of problems like‚ (i)transportation models‚ (ii)transshipment models and (iii) the assignment models. The above models are also basically allocation models
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