and union membership. We will use the technique of linear regression and correlation. Regression analysis in this case should predict the value of the dependent variable (annual wages)‚ using independent variables (gender‚ occupation‚ industry‚ years of education‚ race‚ and years of work experience‚ marital status‚ and union membership). Regression Analysis Based on our initial findings from MegaStat‚ we built the following model for regression (coefficient factors are rounded to the nearest hundredth):
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Curve-Fitting Project – Linear Model: Average Sales Prices of new homes sold in the United States between 1964 and 2008 (LR-1) Purpose: To analyze the average sales prices of new homes sold in the United States from 1964 to 2008. Data: The prices were retrieved from http://www.census.gov/const/uspriceann.pdf. I chose to use the prices between 1964 and 2008 as they showed a huge increase (More data was available (see link)). Average sales prices of new homes sold in the US Year Time (seconds)
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Linearity: A linear correlation coefficient factor was obtained between the peak area used and the absorbance Verses concentrations of lamivudine‚ zidovudine and nevirapine. The calibration curves were linear for concentrations between 15-150 µg/ml. The linearity of the calibration curves was validated by the values of the correlation coefficients (r2). The correlation coefficients were 0.999 for lamivudine‚ 0.999 zidovudine and 0.999 for nevirapine. The results of the linearity experiment are listed
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Cox Regression Models Questions with Answers Worked Example An investigation is carried out into popularity of new cars being bought in the showroom of a Mercedes dealer. Data recorded for each car included colour‚ engine size and car type. A Cox proportional hazards model was fitted to the data and the results are given below: Write down the Cox hazard function according to this model. With regards to the model you have written down above state the following: • To which class of car does the
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Duality in Linear Programming 4 In the preceding chapter on sensitivity analysis‚ we saw that the shadow-price interpretation of the optimal simplex multipliers is a very useful concept. First‚ these shadow prices give us directly the marginal worth of an additional unit of any of the resources. Second‚ when an activity is ‘‘priced out’’ using these shadow prices‚ the opportunity cost of allocating resources to that activity relative to other activities is determined. Duality in linear programming
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ECO 561 Economics March 12‚ 2012 Abstract The following paper discusses a business proposal on hair extensions. The proposal will include profit maximization strategy‚ how to increase revenue‚ entry into the market. The proposal will also look at product differentiation and the mix of pricing and non-pricing strategies to increase revenue as well as pricing and non-pricing strategies for hair extensions under the current business cycle in the United
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Question 1: Run the regression Report your answer in the format of equation 5.8 (Chapter 5‚ p. 152) in the textbook including and the standard error of the regression (SER). Interpret the estimated slope parameter for LOT. In the interpretation‚ please note that PRICE is measured in thousands of dollars and LOT is measured in acres. Model 1: OLS estimates using the 832 observations 1-832 Dependent variable: price VARIABLE COEFFICIENT STDERROR T STAT P-VALUE
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Linear Programming History of linear programming goes back as far as 1940s. Main motivation for the need of linear programming goes back to the war time when they needed ways to solve many complex planning problems. The simplex method which is used to solve linear programming was developed by George B. Dantzig‚ in 1947. Dantzig‚ was one in who did a lot of work on linear programming‚ he was reconzied by several honours. Dantzig’s discovery was through his personal contribution‚ during WWII when Dantzig
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14: Correlation Introduction | Scatter Plot | The Correlational Coefficient | Hypothesis Test | Assumptions | An Additional Example Introduction Correlation quantifies the extent to which two quantitative variables‚ X and Y‚ “go together.” W hen high values of X are associated with high values of Y‚ a positive correlation exists. W hen high values of X are associated with low values of Y‚ a negative correlation exists. Illustrative data set. W e use the data set bicycle.sav to illustrate correlational
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2014/9/16 Linear Equations Ad Options Ads by Vidx Linear Equations A linear equation is an equation for a straight line These are all linear equations: y = 2x+1 5x = 6+3y y/2 = 3 x Let us look more closely at one example: Example: y = 2x+1 is a linear equation: The graph of y = 2x+1 is a straight line When x increases‚ y increases twice as fast‚ hence 2x When x is 0‚ y is already 1. Hence +1 is also needed So: y = 2x + 1 Here are some example values: http://www.mathsisfun.com/algebra/linear-equations
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