Answer :
Final answer:
The best-fitted lines for data sets 1, 2, and 3 represent a positive linear relationship between X and Y, indicating that as X increases, Y also tends to increase.
Explanation:
In linear regression, the best-fitted line represents the relationship between the independent variable (X) and the dependent variable (Y). It is determined by minimizing the sum of the squared differences between the observed Y values and the predicted Y values on the line.
Let's analyze each data set and draw the best-fitted line:
- Data set 1: The scatter plot of the data points shows a positive linear relationship between X1 and Y1. The best-fitted line passes through the points and represents the trend in the data.
- Data set 2: The scatter plot of the data points shows a positive linear relationship between X2 and Y2. The best-fitted line passes through the points and represents the trend in the data.
- Data set 3: The scatter plot of the data points shows a positive linear relationship between X3 and Y3. The best-fitted line passes through the points and represents the trend in the data.
- Data set 4: The scatter plot of the data points shows no clear linear relationship between X4 and Y4. The points are scattered and do not form a straight line. Therefore, it is not appropriate to draw a best-fitted line for this data set.
From the analysis, we can conclude that the best-fitted lines for data sets 1, 2, and 3 represent a positive linear relationship between X and Y. This means that as X increases, Y also tends to increase. However, for data set 4, there is no clear linear relationship between X and Y. The points are scattered, indicating no significant pattern or trend.
The best-fitted line for data set 1, 2, and 3 can be considered the best because they represent a clear positive linear relationship between X and Y. These lines can be used to make predictions or analyze the relationship between the variables.
Learn more about best fitted line and patterns in linear regression here:
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