To analyse whether there is a significant relationship between the number of rental cars owned by an agency and its annual income, the appropriate statistical technique is the Pearson product-moment correlation coefficient (r). Correlation measures the direction and strength of the relationship between two quantitative variables. In this study, the two variables are the number of rental cars owned by the agency (X) and its annual income (Y). The Pearson correlation coefficient is calculated by using the formula: [ r=\frac{N\sum XY-(\sum X)(\sum Y)} {\sqrt{[N\sum X^2-(\sum X)^2][N\sum Y^2-(\sum Y)^2]}} ] where (N) represents the number of observations, (X) represents the number of rental cars, and (Y) represents the annual income. To compute the correlation coefficient from the given table, the values of X and Y for every agency are first taken. The required additional columns (X^2), (Y^2), and (XY) are then calculated. The resulting table can be arranged as follows: | Agency | Number of Rental Cars (X) | Annual Income (Y) | (X^2) | (Y^2) | (XY) | | --------- | ------------------------: | ----------------: | ---------: | ---------: | --------: | | 1 | (X_1) | (Y_1) | (X_1^2) | (Y_1^2) | (X_1Y_1) | | 2 | (X_2) | (Y_2) | (X_2^2) | (Y_2^2) | (X_2Y_2) | | 3 | (X_3) | (Y_3) | (X_3^2) | (Y_3^2) | (X_3Y_3) | | … | … | … | … | … | … | | N | (X_N) | (Y_N) | (X_N^2) | (Y_N^2) | (X_NY_N) | | Total | (\sum X) | (\sum Y) |