Final answer:
In a scatter plot, connecting all data points may not accurately represent the trend of the data. Instead, drawing a trend line can help identify the overall pattern and direction of the relationship between the variables. The correlation coefficient can also be calculated to determine if linear regression is a good candidate.
Step-by-step explanation:
In this scatter plot, we have two variables - the number of visitors to a beach and the high temperature. The scatter plot shows the relationship between these two variables. To understand the trend of the data, it may not make sense to connect all the data points with a line. Instead, we can draw a trend line that represents the general trend of the data. This trend line helps us see the overall pattern in the data and how the temperature affects the number of visitors.
Connecting all the data points with a line may not accurately represent the general trend because scatter plots are used to show the relationship between two variables, not the exact values for each data point. A trend line, on the other hand, helps us identify the overall pattern and direction of the relationship between the variables. By drawing a trend line, we can see if there is a positive or negative correlation between the number of visitors and the high temperature.
Additionally, we can calculate the correlation coefficient to determine the strength and direction of the relationship. If the correlation coefficient is close to 1 or -1, it indicates a strong linear relationship between the variables and linear regression may be a good candidate. However, if the correlation coefficient is close to 0, it suggests a weak or no linear relationship, and linear regression may not be suitable.