Linear Regression (Intensive) Part 1

PrerequisitesNew

Bookings for this module open on THURSDAY, 30 OCTOBER at 10:00 am
For more information see: http://www.ssrmc.group.cam.ac.uk/ssrmc-modules/core/making/windows

This module is part of the Social Science Research Methods Centre training 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.

This course will cover the basics of conducting regression analyses in R. The course will teach both (a) theory and practice of regressions, and (b) how to execute regression analyses in R. The course covers the following topics: (a) correlations, (b) single predictor regressions, (c) multiple predictor regressions, and (d) categorical variables. Students interested in learning about interactions, mediations, and power analyses in regressions should also book for Linear Regression (Intensive) Part II.

• Mphil and PhD students from participating departments taking the Social Science Research Methods Centre training programme as part of their research degree

• A firm knowledge of covariance, correlation, and comparison of means
• A working knowledge of using R
• You must have a University Information Services (Computing) Desktop Services password (http://www.ucs.cam.ac.uk/linkpages/newcomers)
• You must have access to CamTools

Number of sessions: 4

• Session 1: Intro to Regressions and Single Predictor Models
• Session 2: Quadratic relationships and assumptions
• Session 3: Multiple predictors
• Session 4: Categorical Variables

• The objective is to learn the assumptions underlying regression models
• To run regression analysis using R
• To assess an solve possible problems with a regression model

• To learn fundamental statistical techniques - regression analysis

Presentations, demonstrations and practicals

R on MCS

• One final test (optional, dependent upon Department)

• To gain maximum benefits from the course it is important that students do not see this course in isolation from the other MPhil courses or research training they are taking.
• Responsibility lies with each student to consider the potential for their own research using methods common in fields of the social sciences that may seem remote. Ideally this task will be facilitated by integration of the SSRMC with discipline-specific courses in their departments and through reading and discussion.

12 hours in total / four sessions of three hours each

Once in Michaelmas 2014