Introduction to the concept of correlation

13-1: Explain reasons for creating a scatterplot for a set of data.

13-2: Describe three aspects of the relationship between variables.

13-3: Understand what the Pearson correlation coefficient (r) measures.

13-4: Describe the main features and characteristics of the Pearson r.

13-5: Describe the concept of covariance and the roles of covariance and variance in calculating the Pearson r.

Inferential statistics: Pearson correlation coefficient

13-6: Understand, in testing the Pearson r, what is implied by the null hypothesis (H0).and alternative (H1) hypothesis.

13-7: Calculate and interpret the degrees of freedom (df) and the Pearson correlation (r).

13-8: Understand what is represented by r² for the Pearson correlation.

Predicting one variable from another:  Linear regression

13-9: Describe the difference between ‘correlation’ and ‘regression’.

13-10: Understand the purpose of a linear regression equation.

13-11: Calculate and interpret the linear regression equation.

13-12: Understand how to draw a linear regression equation into a scatterplot.

Correlating two sets of ranks:  The Spearman rank order correlation

13-13: Describe research situations that may involve the use of ranked variables.

13-14: Understand similarities and differences between the Spearman rank order correlation (rs) and the Pearson correlation (r).

13-15: Calculate and interpret the Spearman rank order correlation (rs).

Correlational statistics vs. correlational research

13-16: Understand the differences between correlational statistics and correlational research.