Final answer:
To evaluate the correlation of data from different cities, look for patterns or trends and assess data clustering and outliers. The presence of a linear relationship or close clustering indicates a strong correlation, while scattered data points suggest a weak one. Deciding whether to remove outliers requires considering their significance to the dataset.
Step-by-step explanation:
When analyzing the data plotted from various cities, it is important to consider whether the data has a correlation. A qualitative assessment determines if there's a consistent pattern indicating a relationship between datasets of different cities. If the data from one city correlates well with the data from other cities, you will notice a pattern where either the values trend in the same direction or there is a noticeable similarity in the way the data points are spread out.
To identify if the data correlates well, look for general trends such as a linear relationship on a scatter plot, which suggests a good correlation. If points closely follow a line or curve, the correlation is strong. If they are widely scattered, the correlation is weak. We also check for outliers, which are data points that deviate significantly from the other points. Deciding whether to remove outliers depends on if they represent errors or meaningful deviations.
A thorough understanding of the independent and dependent variables, least-squares line, and the correlation coefficient helps to interpret the data correctly. Making a box plot and calculating the interquartile range (IQR) are also strategies to identify outliers and assess data distribution.