Final answer:
The pattern in a scatter plot indicates the type of relationship between variables, determining if they are suitable for linear regression. Linear patterns suggest a strong candidate for linear regression, while curved patterns suggest a non-linear model would be more fitting.
Step-by-step explanation:
When analyzing the pattern in a scatter plot, it is essential to determine the type of relationship between the X and Y variables. If the data points form a distinct linear pattern, either in a positive or negative direction, this suggests a linear relationship, making linear regression a suitable method for modeling the relationship. A positive correlation is indicated by an upward trend in the scatter plot, where as the X variable increases, so does the Y variable, such as the correlation between weight and height. Conversely, a negative correlation, as with tiredness and hours of sleep, shows a downward trend.
However, if the data points in the scatter plot follow a curved pattern, this indicates that a non-linear model would be more appropriate to describe the relationship. A correlation coefficient, such as r = .55, suggests a moderately strong relationship, but the suitability of a linear model must be confirmed by the actual pattern of data points on the scatter plot. If the data points closely align with a straight line, as in a direct relationship, linear regression is likely appropriate.
Therefore, it is crucial not merely to rely on the correlation coefficient but also to inspect the scatter plot to decide if the X and Y variables are good candidates for linear regression. If the scatter plot shows a linear pattern, the variables are suitable candidates; if not, alternative modeling approaches should be considered.