# Semester 2: Weeks 1 to 11

Home / Semester 2: Weeks 1 to 11

## Semester 2: Weeks 1 to 11

2019/2020 Semester 2 Lectures

• Monday from 16:10 to 17:00
David Hume Tower LTs, Lecture Theatre C
• Tuesday from 14:10 to 15:00
Appleton Tower, Lecture Theatre 4
 Date Lecture Title Description 21 TBC Chi-square tests, the final pieces In this lecture we will recap chi-square tests of independence and discuss residuals, effect size, continuity corrections and what to do when data are dependent. 22 TBC One sample & Paired t-test In lectures 22 to 24 we discuss methods for testing the difference in means, collectively named t-test. In this lecture we consider comparing a single sample mean to a known value. 23 TBC Independent sample t-test In lectures 21 to 24 we discuss methods for testing the difference in means, collectively named t-test. In this lecture we consider testing the difference in means across independent groups. 24 TBC Mann-whitney and Wilcoxon tests Sometimes data do not meet the assumptions required for certain types of tests. When our t-test assumptions are not met, but we still wish to think about comparing averages across groups, we can use Mann-Whitney and Wilcoxon tests. 25 TBC Covariance and Correlation In lectures 25 and 26, we consider simple measures of association between two continuous variables. In this lecture we introduce the broad concept and discuss covariance. 26 TBC Spearman Rank Order Correlation In lectures 25 and 26, we consider simple measures of association between two continuous variables. In this lecture we introduce the correlation coefficient. 27 TBC Introduction: Linear Models For the remainder of the course (lectures 27-40) we will be discussing linear models (regression). In this lecture, we begin by introducing the core ideas and the types of questions we can approach. 28 TBC Simple linear models: continuous predictor Here we discuss a simple linear model with a single continuous predictor and show the links with the correlation coefficient. 29 TBC Simple linear models: binary predictor Here we discuss a simple linear model with a single binary predictor and show the links with the t-test. 30 TBC Linear Model Assumptions In this lecture we will talk about the assumptions of linear models (regression) 31 TBC Experimental Data and one-way ANOVA In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. 32 TBC One-way ANOVA: Example and calculations In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. 33 TBC One-way ANOVA: Contrasts and model evaluation In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. 34 TBC One-way ANOVA: Assumptions and checks In lectures 31-34 we will be discussing the use of ANOVA for studying the differences in means across multiple levels of a single grouping variable, commonly called one-way ANOVA. 35 TBC Factorial ANOVA: Introduction 2x2 In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. 36 TBC Factorial ANOVA: Interactions 2x2 In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. 37 TBC Factorial ANOVA: Assumptions and checks In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. 38 TBC Extending factorial ANOVA and interactions In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. 39 TBC Extending factorial ANOVA and interactions In lectures 35 to 39, we will be discussion extensions to the one-way ANOVA where participants are grouped based on multiple categorical variables, commonly called factorial ANOVA. In doing so, we will introduce a critical concept in statistics in psychological science, namely, the interaction. 40 TBC And breathe..... Final lecture of the year. This is simply an open Q&A about the course material. A specific exam revision lecture will be scheduled after the end of the course.

08/08/2019 - 1:05pm