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Theme: Basic Statistics Stream

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4 matching courses


Basic Quantitative Analysis (BQA 2) Mon 9 Nov 2020   10:00   [More dates...] Not bookable

Building upon the univariate techniques introduced in the Foundations in Applied Statistics (FiAS) module, these sessions aim to provide students with a thorough understanding of statistical methods designed to test associations between two variables (bivariate statistics). Students will learn about the assumptions underlying each test, and will receive practical instruction on how to generate and interpret bivariate results using Stata. It introduces students to four of the most commonly used statistical tests in the social sciences: correlation, chi-square tests, t-tests, and analysis of variance (ANOVA).

The module is divided between pre-recorded mini-lectures, in which you'll learn the relevant theory, and hands-on live practical sessions in Zoom, in which you will learn how to apply these techniques to analyse real data using the statistical package, Stata.

You will learn the following techniques:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA

As well as viewing the pre-recorded mini lectures via Moodle and attending the live lab sessions, students are expected to do a few hours of independent study each week.

4 other events...

Date Availability
Mon 9 Nov 2020 10:00 Not bookable
Wed 11 Nov 2020 10:00 Not bookable
Wed 11 Nov 2020 10:00 Not bookable
Wed 27 Jan 2021 09:00 Not bookable
Doing Multivariate Analysis (DMA-1) Mon 23 Nov 2020   10:00   [More dates...] Not bookable

This module will introduce you to the theory and practice of multivariate analysis, covering Ordinary Least Squares (OLS) and logistic regressions. You will learn how to read published results critically, to do simple multivariate modelling yourself, and to interpret and write about your results intelligently.

Half of the module is based in the lecture theatre, and covers the theory behind multivariate regression; the other half is lab-based, in which students will work through practical exercises using statistical software.

To get the most out of the course, you should also expect to spend some time between sessions having fun by building your own statistical models.

3 other events...

Date Availability
Wed 25 Nov 2020 10:00 Not bookable
Wed 25 Nov 2020 10:00 Not bookable
Fri 29 Jan 2021 09:00 Not bookable

This is an introductory course for students who have little or no prior training in statistics. The module is divided between lectures, in which you'll learn the relevant theory, and hands-on practical sessions, in which you will learn how to analyze real data using the statistical package Stata. You will learn:

  • The key features of quantitative analysis, and how it differs from other types of empirical analysis
  • Basic concepts: what is a variable? what is the distribution of a variable? and how can we best represent a distribution graphically?
  • Features of statistical distributions: measures of central tendency and dispersion
  • The normal distribution
  • The basics of formal hypothesis testing
  • Why statistical testing works
  • Statistical methods used to test simple hypotheses
  • How to use Stata

4 other events...

Date Availability
Mon 26 Oct 2020 10:00 Not bookable
Mon 26 Oct 2020 10:00 Not bookable
Wed 28 Oct 2020 10:00 Not bookable
Wed 28 Oct 2020 10:00 Not bookable
Further Topics in Multivariate Analysis (FTMA) 1 Tue 9 Feb 2021   14:00 Not bookable

This module is an extension of the three previous modules in the Basic Statistics stream, and introduces more complex and nuanced aspects of the theory and practice of mutivariate analysis. Students will learn the theory behind the methods covered, how to implement them in practice, how to interpret their results, and how to write intelligently about their findings. Half of the module is based in the lecture theatre; the other half is lab-based, in which students will work through practical exercises using the statistical software Stata.

Topics covered include:

  • Interaction effects in regression models: how to estimate these and how to interpret them
  • Marginal effects from interacted models
  • Ordered and categorical discrete dependent variable models (ordered and multinomial logit and probit)

To get the most out of the course, you should also expect to spend some time between sessions building your own statistical models.