3. We would expect the ENTER score to depend on the average number of hours of study per week. So we take the average hours of study as the independent variable and the ENTER score as the dependent variable. Plot the independent variable along the X-axis and the dependent variable along the Y-axis.

4. Notice that (26, 35) is an outlier. Apart from this score the scores lie within a band from bottom left to top right. The data has a positive correlation (or relationship). As the average number of hours studied per week increases, the ENTER score increases.

5. The relationship or correlation between the Enter score and average number of hours of study per week could be described as: Moderate, positive, linear correlation

7. A more precise way of measuring the correlation between 2 variables is to use a quantitative statistic called Pearson’s Product-Moment Correlation Coefficient, denoted by r. r gives a measure of the strength of the linear relationship between two variables. The value of r lies between -1 and 1. -1 ≤ r ≤ 1

9. The coefficient of determination r 2 is simply the square of the correlation coefficient r. It gives a further measure of the relationship between 2 variables. It is used to measure the strength of relationship between two variables by giving an estimation of how the variation in the dependent variable, y, can be explained by a similar variation in x. The coefficient of determination lies between 0 and 1. 0 ≤ r 2 ≤ 1