Answer:
Explanation:
To determine which of the options are not likely to be a least squares fit, let's consider the concept of least squares fit in linear regression.
In linear regression, the least squares fit is the line that minimizes the sum of the squared vertical distances between the line and the data points. It is the line that best represents the relationship between the variables.
Now, let's analyze each option:
A. The line passes through the largest and smallest data points:
If the line passes through the largest and smallest data points, it means that all the other points are above or below the line. This is likely to be a least squares fit because the line will minimize the squared vertical distances to all the other points.
B. All of the data points are above the line:
If all of the data points are above the line, it means that the line does not capture the relationship between the variables accurately. The line is likely to have a positive slope and not be the least squares fit.
C. Nearly all of the data points are below the line:
Similarly, if nearly all of the data points are below the line, it means that the line does not accurately represent the relationship between the variables. The line is likely to have a negative slope and not be the least squares fit.
Based on this analysis, options B and C are not likely to be a least squares fit because they do not accurately represent the relationship between the variables. Option A, on the other hand, is likely to be a least squares fit as it represents a line that minimizes the squared vertical distances to all the data points.