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    Regression Analysis

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    variance are independent then the two lines will cut each other at a right angle. In this case the two lines will be ║to x and y axis. The Graph is given below:- We restrict our discussion to linear relationships only that is the equations to be considered are 1- y=a+bx 2- x=a+by In equation first x is called the independent variable and y the dependent variable. Conditional on the x

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    regression program available on your computer. 2. Interpret the coefficients and calculate the price elasticity of soft drink demand 3. Omit price from the regression equation and observe the bias introduced into the parameter estimate for income. 4. Now omit both price and temperature from the regression equation. Should a marketing plan for soft drinks be designed that relocates most canned drink machines into low income neighborhoods? Why or Why not? DATA SET 2 The data are the

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    the size of a human brain reaches a maximum or minimum. The goal was to chart previous data collected by experts to support my hypothesis as well as predict and test the size of human brains in the future if the trend continued and develop a linear equation to represent the findings. I began by collecting 12 points of data of

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    SET A: 10897143‚ 10977570‚ 10879587‚ 10870342‚ 10873279‚ 10857133‚ 10661867‚ 10878440‚ 11090782‚ 10770518‚ 10893091 Three electric power plants with capacities of 25‚ 40‚ and 30 million KWh supply electricity to three cities. The maximum demands at the three cities are estimated at 30‚ 35 and 25 million KWh. The price per million KWh at the three cities is given in the table below. [pic] During the month of August‚ there is a 20% increase in demand at each of the three cities‚ which can

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    Stpm

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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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    OLS is blue

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    to use the law of iterated expectations in proving the BLUE. With Assumptions (B)‚ the BLUE is given conditionally on Let us use Assumptions (A). The Gauss-Markov Theorem is stated below Under Assumptions (A)‚ the OLS estimators‚ are the Best Linear Unbiased Estimator (BLUE)‚ that is 1. Unbias : 2. Best : Real data seldomly satisfy Assumptions (A) or Assumptions (B). Accordingly we should think that the Gauss-Markov theorem only holds in the never-never land. However‚ it is important

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    Tora Software

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    packages‚ while the tutorial mode keeps on giving step-wise information about the methodology and solution. TORA tutorial software deals with the following algorithms: •Solution of simultaneous linear equationsLinear programming •Transportation model •Integer programming •Network models •Project analysis by CPM/PERT •Poisson queuing models

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    Chemical Reactions I. Purpose – The purpose of this lab was to observe different type of chemical reactions to write and balance chemical equations. II. Hypothesis: If you mix two chemicals together‚ then they will change color and/or bubble/fix. III. Procedure - Workstation 1: 1. Light the Bunsen Burner 2. Add 5 – 8 mL of HCL to a test tube that’s in the test tube rack 3. Drop a 2 – cm piece of Mg ribbon into the test tube 4. Record Observations 5. Clean Workstation

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    value‚ rational expressions‚ equations (linear‚ quadratic‚ radical‚ rational)‚ systems of equations‚ inequalities‚ functions‚ graphs of quadratic and linear equations and inequalities in two variables‚ complex numbers and applications. 2. Learning Outcomes Upon successful completion of this course‚ students will be able to: 1. perform operations involving polynomials and factoring polynomials 2. solve and graph equations and inequalities such as linear‚ absolute value‚ quadratic‚ rational

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    Regression and Correlation

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    regression are concerned with measuring the linear relationship between two variables. 1.1 Scattergram It is not a graph at all‚ it looks at first glance like a series of dots placed haphazardly on a sheet of graph paper. The purpose of scattergram is to illustrate diagrammatically any relationship between two variables. (a) If the variables are related‚ what kind of relationship it is‚ linear or nonlinear ? (b) If the relationship is linear‚ the scattergram will show whether

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