If you observe fewer runs than expected, it may be a coincidence of random sampling or it may mean that your data deviate systematically from a straight line. If you observe more runs than expected, the P value will be higher than 0.50. If the runs test reports a low P value, conclude that the data don't really follow a straight line, and consider using nonlinear regression to fit a curve. Comparing slopes and intercepts Prism can test whether the slopes and intercepts of two or more data sets are significantly different. It compares linear regression lines using the method explained in Chapter 18 of J Zar, Biostatistical Analysis , 2nd edition, Prentice-Hall, 1984.
Prism compares slopes first.
If the P value is less than 0.05, Prism concludes that the lines are significantly different. In that case, there is no point in comparing the intercepts. Now the question is whether the lines are parallel or identical. Prism calculates a second P value testing the null hypothesis that the lines are identical. If this second P value is high, there is no compelling evidence that the lines are different.