Project 1: Linear Correlation and Regression Analysis Gross Revenue and TV advertising: Pfizer Inc‚ along with other pharmaceutical companies‚ has begun investing more promotion dollars into television advertising. Data collected over a two year period‚ shows the amount of money Pfizer spent on television advertising and the revenue generated‚ all on a monthly bases. |Month |TV advertising |Gross Revenue | |1 |17 |4.1 | |2
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This article considers the relationship between two variables in two ways: (1) by using regression analysis and (2) by computing the correlation coefficient. By using the regression model‚ we can evaluate the magnitude of change in one variable due to a certain change in another variable. For example‚ an economist can estimate the amount of change in food expenditure due to a certain change in the income of a household by using the regression model. A sociologist may want to estimate the increase
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2-24‚ 34‚ 36‚ 38 (you only have to do part A for these problems. You do not have to do the part B graphical solutions) and 3-10‚ 12 (parts B and C for Problem 12)‚ 28‚ 30. Please use Excel solver function. I posted an annotated solution to Problem 2-5. This problem is an examplar for both the chapter 2 and 3 problems. In this example I show how I typically set up a problem. First I set up the linear programming model and then develop a parallel set-up to use as input to the Solver add-in. I
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CHAPTER 4 – THE BASIS OF STATISTICAL TESTING * samples and populations * population – everyone in a specified target group rather than a specific region * sample – a selection of individuals from the population * sampling * simple random sampling – identify all the people in the target population and then randomly select the number that you need for your research * extremely difficult‚ time-consuming‚ expensive * cluster sampling – identify
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Linear Programming in Finance‚ Accounting and Economics Sijia Lu 7289928683 Abstract This article is literatures review about five articles‚ which apply linear programming to Finance‚ accounting and economics. The mathematical method is found of crucial importance in those fields. The paper shows how theoretical inference in linear programming throws light upon realistic practice‚ and how empirical evidence supports those theories. Keywords: finance; accounting; economics; linear programming; investment
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Q.1. What is a linear programming problem ? Discuss the steps and role of linear programming is solving management problems. Discuss and describe the role of liner programming in managerial decision-making bringing out limitations‚ if any. Ans : Linear Programming is a mathematical technique useful for allocation of scarce or limited resources to several competing activities on the basis of given criterion of optimality. The usefulness of linear programming as a tool for optimal decision-making
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Linear Regression & Best Line Analysis Linear regression is used to make predictions about a single value. Linear regression involves discovering the equation for a line that most nearly fits the given data. That linear equation is then used to predict values for the data. A popular method of using the Linear Regression is to construct Linear Regression Channel lines. Developed by Gilbert Raff‚ the channel is constructed by plotting two parallel‚ middle lines above and below a Linear Regression
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= −2 1 5 1 0 1 0 1 0 2 7 2 −3 2 1 0 1 −2 11 −2 21 −2 −7 12 −7 −3x2 = 21 x1 + 2 x2 = −2 x2 = −7 x1 = 12 x2 = −7 1 2 CHAPTER 1 • Linear Equations in Linear Algebra 3. The point of intersection satisfies the system of two linear equations: x1 + 5 x2 = 7 x1 − 2 x2 = −2 1 1 5 −2 7 −2 x1 + 5 x2 = 7 Replace R2 by R2 + (–1)R1 and obtain: Scale R2 by –1/7: Replace R1 by R1 + (–5)R2: The point of intersection is
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LINEAR REGRESSION MODELS W4315 HOMEWORK 2 ANSWERS February 15‚ 2010 Instructor: Frank Wood 1. (20 points) In the file ”problem1.txt”(accessible on professor’s website)‚ there are 500 pairs of data‚ where the first column is X and the second column is Y. The regression model is Y = β0 + β1 X + a. Draw 20 pairs of data randomly from this population of size 500. Use MATLAB to run a regression model specified as above and keep record of the estimations of both β0 and β1 . Do this 200 times. Thus you
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The linear probability model‚ ctd. When Y is binary‚ the linear regression model Yi = β0 + β1Xi + ui is called the linear probability model. • The predicted value is a probability: • E(Y|X=x) = Pr(Y=1|X=x) = prob. that Y = 1 given x • Yˆ = the predicted probability that Yi = 1‚ given X • β1 = change in probability that Y = 1 for a given ∆x: Pr(Y = 1 | X = x + ∆x ) − Pr(Y = 1 | X = x ) β1 = ∆x 5 Example: linear probability model‚ HMDA data Mortgage denial v. ratio of debt payments to income (P/I
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