| dc.contributor.author | Newton, Elizabeth | en_US |
| dc.coverage.temporal | Spring 2003 | en_US |
| dc.date.issued | 2003-06 | |
| dc.identifier | 15.075-Spring2003 | |
| dc.identifier | local: 15.075 | |
| dc.identifier | local: IMSCP-MD5-3b7fefac0fd6863a1a5fe7fe205f459f | |
| dc.identifier.uri | http://hdl.handle.net/1721.1/72947 | |
| dc.description.abstract | This course is an introduction to applied statistics and data analysis. Topics include collecting and exploring data, basic inference, simple and multiple linear regression, analysis of variance, nonparametric methods, and statistical computing. It is not a course in mathematical statistics, but provides a balance between statistical theory and application. Prerequisites are calculus, probability, and linear algebra. We would like to acknowledge the contributions that Prof. Roy Welsch (MIT), Prof. Gordon Kaufman (MIT), Prof. Jacqueline Telford (Johns Hopkins University), and Prof. Ramón León (University of Tennessee) have made to the course material. | en_US |
| dc.language | en-US | en_US |
| dc.relation | | en_US |
| dc.rights.uri | Usage Restrictions: This site (c) Massachusetts Institute of Technology 2012. Content within individual courses is (c) by the individual authors unless otherwise noted. The Massachusetts Institute of Technology is providing this Work (as defined below) under the terms of this Creative Commons public license ("CCPL" or "license") unless otherwise noted. The Work is protected by copyright and/or other applicable law. Any use of the work other than as authorized under this license is prohibited. By exercising any of the rights to the Work provided here, You (as defined below) accept and agree to be bound by the terms of this license. The Licensor, the Massachusetts Institute of Technology, grants You the rights contained here in consideration of Your acceptance of such terms and conditions. | en_US |
| dc.subject | data analysis | en_US |
| dc.subject | multiple regression | en_US |
| dc.subject | analysis of variance | en_US |
| dc.subject | multivariate analysis | en_US |
| dc.subject | data mining | en_US |
| dc.subject | probability | en_US |
| dc.subject | collecting data | en_US |
| dc.subject | sampling distributions | en_US |
| dc.subject | inference | en_US |
| dc.subject | linear regression | en_US |
| dc.subject | ANOVA | en_US |
| dc.subject | nonparametric methods | en_US |
| dc.subject | polls | en_US |
| dc.subject | surveys | en_US |
| dc.subject | statistics | en_US |
| dc.subject | management science | en_US |
| dc.subject | finance | en_US |
| dc.subject | statistical graphics | en_US |
| dc.subject | estimation | en_US |
| dc.subject | hypothesis testing | en_US |
| dc.subject | logistic regression | en_US |
| dc.subject | contingency tables | en_US |
| dc.subject | forecasting | en_US |
| dc.subject | factor analysis | en_US |
| dc.subject | Statistics | en_US |
| dc.title | 15.075 Applied Statistics, Spring 2003 | en_US |
| dc.title.alternative | Applied Statistics | en_US |
