Hi, i need to write analyze using the supermarket data provided in the scanned book.. thank you
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Project and Journal Article assignment The due date is on or before 2rd of August 2011 by noon. (A) Regression analysis (10%) -Use any software such as Excel to run a multiple regression analysis using a given set of data for supermarkets profits. -After running the multiple regression analysis, you should interpret the output in the same manner that Pavel Yakovlev and Linda Kinney did in the assigned article. **Note: Should not be more than 2 pages (single space) (B) Show an understanding of what Pavel Yakovlev and Linda Kinney wrote in their paper. (10%) By this, you should be able to identify : Statement of the problem The literatures reviewed The formulation of the model Data source and description What method was used in the estimation Hypothesis testing Interpretation of the results The limitations of the study and Possible Extensions **Note: Should not be more than 2 pages (single space)
Project and Journal Article assignment The due date is on or before 2rd of August 2011 by noon. (A) Regression analysis (10%) · -Use any software such as Excel to run a multiple regression analysis using a given set of data for supermarkets profits. · -After running the multiple regression analysis, you should interpret the output in the same manner that Pavel Yakovlev and Linda Kinney did in the assigned article. **Note: Should not be more than 2 pages (single space) (B) Show an understanding of what Pavel Yakovlev and Linda Kinney wrote in their paper. (10%) · By this, you should be able to identify : (a) Statement of the problem (b) The literatures reviewed (c) The formulation of the model (d) Data source and description (e) What method was used in the estimation (f) Hypothesis testing (g) Interpretation of the results (h) The limitations of the study and Possible Extensions **Note: Should not be more than 2 pages (single space) Atl Econ J (2008) 36:493^194 DOI 10.1007/sll293-008-9142-x Additional Evidence on the Effect of Class Attendance on Academic Performance Pavel Yakovlev • Linda Kinney Published online: 13 August 2008 © International Atlantic Economic Society 2008 JEL A20-C12 Professors often claim that students who attend their classes regularly receive better grades and learn more than those who do not. Romer (Journal of Economic Perspectives 7, 1993), Launius (College Student Journal 31, 1997), Van Blerkom (Journal of Psychology: Interdisciplinary and Applied 126, 1992), and Park and Kerr- (Journal of Economic Education 21, 1999) find evidence in support of this hypothesis. However, many studies on class attendance conducted by economists focus on the principles of economics courses in which students are drawn from a narrow range of majors like accounting, business, and economics. In contrast, this study analyzes how class attendance affects academic performance in the introductory economics course that all students at Shepherd University must complete. The students enrolled in this course represent a very diverse group with various academic skills and interests and a broad range of majors including the arts, humanities, sciences, and applied fields like nursing. Using a uniquely diverse sample of students from a one-semester introductory economics course, we show that attendance has a very large and statistically significant effect on academic performance even after factoring out the motivation component. We test the hypothesis that class attendance has a positive effect on academic performance. The model to be estimated can be expressed as a general production function that relates output in terms of academic performance to three key inputs such as ability, attendance, and motivation. All three independent variables are hypothesized to be P. Yakovlev (El) Duquesne University, 600 Forbes Avenue, Pittsburgh, PA 15282, USA e-mail:
[email protected] L. Kinney Shepherd University, P. 0. Box 3210, Shepherdstown, WV 25443, USA e-mail:
[email protected] Springer 494 P. Yakovlev, L. Kinney positively related to student academic performance, the dependent variable, hi this specification, ability can be thought of as a technological constant, while attendance and motivation can be thought of as variable inputs. We test our hypothesis using the data from three sections of the undergraduate Contemporary Economics course taught by the same professor during spring of 2001 that yields the sample of 70 students. Academic performance measured by the average course score (COURSE AV) was chosen as the dependent variable. The three independent variables are class attendance (ATTEND), ability proxied by SAT scores (SAT), and effort proxied by points for extra credit assignments (XCR). Class attendance was not mandatory and students were not penalized if they missed class. However, the professor informed the class that attendance would be considered if, at the end of the semester, a student was on the borderline between two grades. The results of two regressions are reported. The first regression results are shown below. COURSE AV = 17.87 + 0.69(ATTEND) + 0.05(SAT) + 0.42(XCR) (f = 3.26) (f = 5.89) (f = 2.25) R2 = 0.454 All three independent variables have the expected positive signs and are statistically significant at the five percent level. The attendance coefficient implies that each class attended increases course average by 0.69 points, ceteris paribus. However, it could be argued that both attendance and extra credit points might capture a significant level of motivation making the two variables highly correlated. A pair-wise correlation analysis is performed to investigate the degree of multicoilinearity among the three independent variables and shows a significant correlation coefficient (0.5) between attendance and extra credit points, hi order to more accurately access the effect of attendance on academic performance, the motivation effect is factored out from the attendance coefficient using the residual method from Park and Kerr (Journal of Economic Education 21, 1999). Extra credit points are then regressed against attendance and SAT scores to get a residual that reflects that portion of the extra credit points are not correlated with attendance or SAT. Then, this residual (XCR) is used as a proxy for motivation alongside attendance and SAT to generate the regression equation below. COURSE AV = 19.95 -f 0.90(ATTEND) + 0.04(SAT) + 0.42(XCR) (f = 4.18) (f==5.66) (f = 2.25) R2 = 0.454 In the second regression, the magnitude of the attendance coefficient increased from 0.69 to 0.90. This means that each class attended contributed, on average, 0.90 points to the course grade, which is almost twice the size of the SAT and extra credit coefficients combined. The second regression equation predicts that a student with average attendance, SATs, and residual can expect to earn 78 points for the course, a grade of C. A student with average SATs and extra credit (residual), but perfect attendance, could hope to receive 83 points of a grade of B. The statistical evidence from this and other studies shows that class attendance is highly correlated with academic performance even if attendance policy is not mandatory. €1 Springer