IBDP Mathematics Applications & Interpretations SL Chapter 6 Notes

Modelling relationships: linear correlation of bivariate data

STUDY NOTES FOR MATHEMATICS – CHAPTER 6 – Modelling relationships: linear correlation of bivariate data

These notes have specially been curated by expert teachers to simplify and enlighten concepts given in IB Mathematics SL. The notes are comprehensive in nature and are sufficient to study the chapter in depth and one need not look for other resources beyond the notes provided on our website which can be accessed for free. The notes for Mathematics IBDP SL are available on our official website and can be downloaded for free. You are one click away from obtaining all that you need to score well in IB Mathematics SL.

All DP mathematics courses serve to accommodate the diverse range of needs, interests and abilities of students, and to fulfill the requirements of various university and career aspirations. The aims of these courses are to enable students to: develop mathematical knowledge, concepts, principles, logical, critical and creative thinking, employ and refine their powers of abstraction and generalization. Students are also encouraged to appreciate the international dimensions of mathematics and the multiplicity of its cultural and historical perspectives.

In this chapter we consider bivariate data, which means data has two variables recorded for each individual. In most real-world situations, there will not be an exact relationship between the two variables. Our goal is to find which model best fits the data and measure how strong the relationship between the variables is. We delve into topics such as correlation when using scatter plots by drawing the line of best fit to classify the strength between variables. However, as this method is subjective, we use Pearson’s product moment correlation coefficient r as it is a more precise measure of strength. In order to find the equation of the line which best fits the data, we use a method known as linear regression. This method minimises the distance between the line and the data points, known as a residual.