Final Answer:
Omitting the outlier would affect the least-squares regression equation by reducing its accuracy.
Step-by-step explanation:
Least-squares regression is a method used to find a line of best fit for a set of data points. It is used to determine the relationship between two or more variables. It is important to note that the line of best fit is determined by the data points included in the set. An outlier is a data point that is far away from the main stream of points. Omitting this outlier would have an effect on the least-squares regression equation because it would reduce the accuracy of the equation.
The accuracy of the least-squares regression equation is determined by how well it fits the data points. If an outlier is omitted, the equation will no longer fit the data points as well as it did before. This is because the outlier is a data point that has a large influence on the equation, so when it is omitted, the equation will no longer be as accurate.
The effect of omitting an outlier on the least-squares regression equation can be seen by looking at the correlation coefficient. The correlation coefficient is a measure of how well the data points fit the equation. If an outlier is omitted, the correlation coefficient will be lower than before. This indicates that the data points do not fit the equation as well as before, and so the equation is less accurate.
In conclusion, omitting an outlier from the least-squares regression equation would reduce the accuracy of the equation. This is because the outlier has a large influence on the equation, and when it is omitted, the equation no longer fits the data points as well as before. The effect of this can be seen by looking at the correlation coefficient, which will be lower than before if an outlier is omitted.