Economics 203 Syllabus APLIAEconomic Statistics II Sections AL1‚ BL1 Fall 2013 Instructor: Office: Phone: e-mail: Office hours: Lecture hours: Lecture Section: Lecture Location: Professor Joseph A. Petry 116 David Kinley Hall 333-4260 jpetry@illinois.edu Wed 10:15 – 11:15 M/W 3:00 – 3:50 (AL1); M/W 4:00 – 4:50 (BL1) AL1‚ BL1 141 Wohlers Hall Lab Time: Lab Days: Lab Location: TA Office Hours: TA Contacts: Head TA Varies by TA section Thursday / Friday 901 W. Oregon
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2015 Version 1.0.5 Date 2014-12-27 Title Extremely Randomized Trees (ExtraTrees) Method for Classification and Regression Author Jaak Simm‚ Ildefons Magrans de Abril Maintainer Jaak Simm <jaak.simm@gmail.com> Description Classification and regression based on an ensemble of decision trees. The package also provides extensions of ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method. Depends R (>= 2.7.0)‚ rJava (>= 0.5-0) Suggests testthat‚ Matrix SystemRequirements
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Regression Analysis: Predicting for Detroit Tigers Game Managerial Economics BSNS 6130 December 13‚ 2012 By: Morgan Thomas Chad Goodrich Jake Dodson Austin Burris Brittany Lutz Abstract As there are many who invest in athletic events‚ the ability to better predict attendance to such events‚ such as the Detroit Tigers games‚ could benefit many. The benefits include being able to better stock concessions stands‚ allocate advertising budgets‚ and staff security. Therefore‚ the aim
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Package ‘randomForest’ February 20‚ 2015 Title Breiman and Cutler ’s random forests for classification and regression Version 4.6-10 Date 2014-07-17 Depends R (>= 2.5.0)‚ stats Suggests RColorBrewer‚ MASS Author Fortran original by Leo Breiman and Adele Cutler‚ R port by Andy Liaw and Matthew Wiener. Description Classification and regression based on a forest of trees using random inputs. Maintainer Andy Liaw <andy_liaw@merck.com> License GPL (>= 2) URL http://stat-www.berkeley.edu/users/breiman/RandomForests
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MATH533: Applied Managerial Statistics PROJECT PART C: Regression and Correlation Analysis Using MINITAB perform the regression and correlation analysis for the data on SALES (Y) and CALLS (X)‚ by answering the following questions: 1. Generate a scatterplot for SALES vs. CALLS‚ including the graph of the "best fit" line. Interpret. After interpreting the scatter plot‚ it is evident that the slope of the ‘best fit’ line is positive‚ which indicates that sales amount varies directly
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Tiffany Camp ECO-250 Volker Grzimek Regression Analysis of Work Hours in Relation to GPA This research investigated the affects of working extra hours in a labor position on students’ GPAs each semester at Berea College. It was my belief that students who worked more hours were more likely to have lower GPAs due to their studying abilities and opportunities being compromised as a result of working too long (a negative correlation or trend between GPAs and hours worked each week). For
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au/webapps/portal/frameset.jsp?tab=courses&url=/bin/common/course.pl?course_id=_111213_1&frame=top • You assignment must be in a Word doc format – no pdfs! • When answering questions‚ wherever required‚ you should cut and paste the Excel output (eg‚ plots‚ regression output etc) to show your working on your assignment. • You are required to keep a hard copy and an electronic copy of your submitted assignment to re-submit‚ in case the original submission is lost for some reason. Important Notice:
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The Springfield R-12 School District‚ located in Springfield‚ Missouri‚ is the largest‚ fully accredited school district in the state with a student population of over 24‚000. The majority of their students attend one of their thirty-six elementary schools which house grades K-5‚ one of their nine middle schools which are grades 6-8 or one of their five high schools. Students also have the opportunity to attend the Phelps Center for Gifted Education and an Early Childhood Center. Springfield Public
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ESSAYS ON POVERTY‚ MICROFINANCE AND LABOR ECONOMICS by SANDARADURA INDUNIL UDAYANGA DE SILVA‚ B.Sc.‚ M.A. A DISSERTATION IN ECONOMICS Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of DOCTOR OF PHILOSOPHY Approved Masha Rahnama Chairperson of the Committee Thomas Steinmeier Robert McComb Accepted John Borrelli Dean of the Graduate School August‚ 2006 Copyright 2006‚ Sandaradura Indunil Udayanga De Silva ACKNOWLEDGEMENTS
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2008: H0: The variables will predict whether or not a team will make the playoffs. H1: The variables will not predict whether or not a team will make the playoffs. After running the regressions‚ it’s clear that all of the variables are insignificant at the 5% level. The only one that may have some significance is the rush rank‚ yet even that variable is not a great indicator of whether or not a team will make the playoffs. The relationship between rush rank and making the playoffs is negative
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