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Theme: Basic Statistics Stream

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4 matching courses


Basic Quantitative Analysis (BQA 2) Mon 11 Nov 2019   10:00   [More dates...] Not bookable

This module follows on from Foundations in Applied statistics, and will teach you the basics of common bivariate techniques (that is, techniques that examine the associations between two variables). 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 apply these techniques to the analysis of real data.

Techniques to be covered include:

  • Cross-tabulations
  • Scatterplots
  • Covariance and correlation
  • Nonparametric methods
  • Two-sample t-tests
  • ANOVA
  • Ordinary Least Squares (OLS)

For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class.

4 other events...

Date Availability
Mon 11 Nov 2019 10:00 Not bookable
Wed 13 Nov 2019 10:00 Not bookable
Wed 13 Nov 2019 10:00 Not bookable
Wed 29 Jan 2020 09:00 Not bookable
Doing Multivariate Analysis (DMA-1) Mon 25 Nov 2019   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 27 Nov 2019 10:00 Not bookable
Wed 27 Nov 2019 10:00 Not bookable
Fri 14 Feb 2020 09:00 Not bookable
Foundations in Applied Statistics (FiAS-2) Mon 28 Oct 2019   10:00   [More dates...] 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

For best results, students should expect to do a few hours of private study and spend a little extra time in the computer labs, in addition to coming to class.

4 other events...

Date Availability
Mon 28 Oct 2019 10:00 Not bookable
Wed 30 Oct 2019 10:00 Not bookable
Wed 30 Oct 2019 10:00 Not bookable
Mon 27 Jan 2020 09:00 Not bookable
Further Topics in Multivariate Analysis (FTMA) Tue 11 Feb 2020   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.