does the residual plot support the appropriateness of a linear model? responses yes, because there is a clear pattern displayed in the residual plot. yes, because there is a clear pattern displayed in the residual plot. yes, because about half the residuals are positive and the other half are negative. yes, because about half the residuals are positive and the other half are negative.

Answer:

Step-by-step explanation:

Yes, the residual plot supports the appropriateness of a linear model because it displays a clear pattern and has roughly equal amounts of positive and negative residuals.

Is the residual plot relatively symmetrical around the centerline?

Yes, if the residual plot is relatively symmetrical around the centerline, it suggests that the linear model is appropriate because it is capturing the variance in the data evenly. If the residual plot is skewed or has outliers, it may indicate that the linear model is not accurately representing the data and a different model may be needed.

A residual plot is a graph that plots the residuals (the differences between the observed values and the predicted values) on the y-axis and the independent variable on the x-axis. The residual plot is used to assess the appropriateness of a linear model. If the residual plot displays a clear pattern and has roughly equal amounts of positive and negative residuals, it suggests that the linear model is appropriate. On the other hand, if the residual plot is scattered randomly or displays a clear pattern, it suggests that the linear model is not appropriate. In this case, the residual plot supports the appropriateness of a linear model because it displays a clear pattern and has roughly equal amounts of positive and negative residuals. This suggests that the linear model is able to accurately predict the dependent variable based on the independent variable.

Therefore, the residual plot supports the appropriateness of a linear model.

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Yes, because it displays a clear pattern and has roughly equal amounts of positive and negative residuals.

What is a residual plot and how does it support the appropriateness of a linear model?

A residual plot is a graphical representation of the residuals (prediction errors) of a regression model. It can be used to assess the appropriateness of a linear model by examining the pattern of the residuals and the presence of any unusual points or trends.

If the residual plot shows a clear pattern, such as a trend or curvature, it suggests that the linear model may not be appropriate for the data. On the other hand, if the residual plot appears random and shows roughly equal amounts of positive and negative residuals, it supports the appropriateness of a linear model.

The residual plot is used to judge whether a linear model is suitable. The linear model may be useful if the residual plot exhibits a distinct pattern and contains nearly equal numbers of positive and negative residuals. On the other hand, it indicates that the linear model is inappropriate if the residual plot is dispersed randomly or exhibits a distinct pattern. The residual plot shows a distinct pattern and about equal numbers of positive and negative residuals, which in this instance confirms the suitability of a linear model. This suggests that the dependent variable may be accurately predicted by the linear model utilising the independent variable.
Therefore, the residual plot confirms that a linear model is adequate.

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