50 17.5 3 140 50 16.50 14.0 4 190 190 14.50 21.0 5 130 90 17.00 15.5 6 160 60 16.00 14.5 7 200 140 13.00 21.5 8 150 110 18.00 18.0 9 210 200 12.00 18.5 10 190 100 15.50 20.0 a) Develop a regression model that determines the relationship between Sales and Selling Price. I. What is the estimated regression equation? y = α + β(x) Sales = 390.38 -14.26 (Selling Price) II. Is selling price of a significant determinant of sales? At what level(s) of significance? Yes‚ selling price indeed a significant
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2008 AACE INTERNATIONAL TRANSACTIONS EST.03 An Introduction to Parametric Estimating Mr. Larry R. Dysert‚ CCC A ACE International describes cost estimating as the “predictive process used to quantify‚ cost‚ and price the resources required by the scope of an asset investment option‚ activity‚ or project [1].” The methods and techniques used to prepare a cost estimate will typically vary based on the level of project definition available at the time the estimate is prepared [2‚ 3]. Early in a project’s
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and the pooled time regression method is used to analyze the data. Return model as well as Price model was used to determine the value relevance of financial statements’ information. It revealed that the value relevance of accounting information under the Price model has more explanatory power than Return Model. The empirical results of the study indicate that Earning Per Share (EPS) is the most value relevant variable in this study and it is significant at 0.01 level. Regression of earnings‚ book
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ETF2121/ETF5912 Data Analysis in Business Unit Information – Semester 1 2014 Coordinator and Lecturer - Weeks 7-12: Associate Professor Ann Maharaj Office: H5.86 Phone: (990)32236 Email: ann.maharaj@monash.edu Lecturer - Weeks 1-6: Mr Bruce Stephens Office: H5.64 Phone: (990)32062 Email: bruce.stephens@monash.edu Unit material: No prescribed textbook Unit Book: available on the Moodle site. Exercises: available on the Moodle site. Software: EXCEL. Recommended Reference Books Berenson
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a) Using any computer software‚ perform regression analysis from the data in the Table. The following is the regression analysis on data given by using Microsoft Excel: Regression Statistics Multiple R 0.986557829 R Square 0.973296349 Adjusted R Square 0.968845741 Standard Error 184.7121318 Observations 15 ANOVA df SS MS F Significance F Regression 2 14922670.21 7461335 218.68838 0.00000000036260 Residual 12 409422.8596 34118.6
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The data gained from employees of a textile enterprise in Tekirdag are analyzed by using factor analysis (Principal Components with Varimax Rotation) and a regression model. As a result‚ economics tools by employee is positively and significantly (p=0.001) related to the level of motivation increase perceived by the employee. Second regression model plays an important role in determining the level of job satisfaction except economic and psychosocial tools and has a positive effect on increasing employee
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cost function for only two periods‚ meaning it is less accurate. Simple Regression Using simpler regression to estimate the salary cost with available ton miles as the cost driver. These are the results: Coefficients Intercept X Variable 1 -682.643 0.551693 Standard deviation 282.6033 0.79698 Salary= 0.5517x available ton miles- 682.63 R2=0.5577‚ and the coefficients are larger than the deviations so it is valid. Regression analysis is more reliable at measuring cost behavior than other measurement
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Analysis and Prevention 38‚ pp. 1019–1027. [6] Kuhnert‚ P.M.‚ Do‚ K.A.‚ McClure‚ R.‚ (2000). Combining non-parametric models with logistic regression: an application to motor vehicle injury data [7] Pakgohar‚ A.‚ Tabrizi‚ R.S.‚ Khalilli‚ M.‚ Esmaeili‚ A.‚ (2010). The role of human factor in incidence and severity of road crashes based on the CART and LR regression: a data mining approach [8] Kashani‚ A.‚ Mohaymany‚ A.‚ Ranjbari‚ A.‚ (2011). A Data Mining Approach to Identify Key Factors of Traffic Injury
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International Research Journal of Finance and Economics ISSN 1450-2887 Issue 52 (2010) © EuroJournals Publishing‚ Inc. 2010 http://www.eurojournals.com/finance.htm Does Education Alleviate Poverty? Empirical Evidence from Pakistan Imran Sharif Chaudhry Associate Professor of Economics. Bahauddin Zakariya University Multan‚ Pakistan E-mail: imranchaudhry@bzu.edu.pk Shahnawaz Malik Professor of Economics‚ Bahauddin Zakariya University Multan‚ Pakistan E-mail: shahnawazmalik@bzu.edu.pk Abo ul Hassan
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CONSUMPTION PATTERN IN TAMILNADU A.KASIRAJAN(Asst.Prof) WITH REFERANCE TO PERMANENT INCOME Department of Economics HYPOTHESIS R.K.M.VivekanandaCollege‚ Mylapore‚Chennai600004. _______________________________________________________________________________ Introduction The central idea of the permanent income hypothesis‚ proposed by Milton Friedman in 1957‚ is that people base consumption on what they consider their “normal income”
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