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Jsss Programme 2011-12

Programme of events provided by Joint Schools' Social Sciences
(Thu 6 Oct 2011 - Wed 14 Mar 2012)

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Tue 13 Mar 2012 – Wed 14 Mar 2012

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Tuesday 13 March 2012

14:00
Module 10:Time Series Analysis (4 of 4) Finished 14:00 - 16:00 Phoenix Teaching Room

This module is part of the Social Science Research Methods Course programme which is a shared platform for providing research students with a broad range of quantitative and qualitative research methods skills that are relevant across the social sciences.

The module introduces time series techniques relevant to forecasting in social science research and computer implementation of the methods.

Module 4: Linear Regression (Series 2) (4 of 4) Finished 14:00 - 16:00 Titan Teaching Room 2

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

16:00
Module 4: Linear Regression (Series 3) (4 of 4) Finished 16:00 - 18:00 Titan Teaching Room 2

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

Module 4: Linear Regression (Series 1) (4 of 4) Finished 16:00 - 18:00 Titan Teaching Room 1, New Museums Site

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

Wednesday 14 March 2012

14:00
Module 4: Linear Regression for Judge students (4 of 4) Finished 14:00 - 16:00 Judge Business School, Computer Room

Module introduces students to one of the most fundamental statistical techniques, namely regression analysis. Students learn about assumptions underlying regression models, how to run regression analysis using SPSS and how to access and solve possible problems with a regression model.

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