Students engage in exercises that involve plotting data points and drawing regression lines to best fit the data. They learn to interpret the slope and y-intercept of the regression line, understanding how these components describe the relationship between variables. These tasks help students visualise trends and make predictions based on the line of best fit, enhancing their data analysis skills.

This series of questions delves into the study of linear regression, emphasising its role in modeling the relationship between two variables by fitting a linear equation to observed data. Students learn to calculate and interpret the regression equation, understand the importance of the correlation coefficient, and evaluate the goodness of fit. Mastery of these concepts is essential for analysing relationships and trends across various fields, including economics, biology, and social sciences.

These exercises challenge students to apply their knowledge of linear regression to real-world situations. They work on problems that involve making predictions and analysing trends, such as projecting future sales based on past data, exploring the correlation between study hours and exam results, or determining the impact of advertising on consumer demand. These activities highlight the practical value of linear regression in decision-making and problem-solving across diverse industries and research disciplines.