The properties that the residuals should always display are:

1. Mean 0: The residuals are the differences between the observed and predicted values. If the model is well-fitted, the sum of these differences should be zero, which means the mean of the residuals should be zero.

2. Small variance: A well-fitted model should have residuals that are small in magnitude. This means that the variance of the residuals should be small.

3. A symmetric distribution: The residuals should be symmetrically distributed around zero. This is because a well-fitted model should underpredict and overpredict the observed values equally.

The residuals do not necessarily need to display a bimodal distribution or all positive values. In fact, if all residuals were positive, it would indicate that the model is consistently underpredicting the observed values, which is not a characteristic of a well-fitted model. Similarly, a bimodal distribution of residuals could indicate that the model is not fitting the data well in certain areas.

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The properties that the residuals should always display are:

1. Mean 0: The residuals are the differences between the observed and predicted values. If the model is well-fitted, the sum of these differences should be zero, which means the mean of the residuals should be zero.

2. Small variance: A well-fitted model should have residuals that are small in magnitude. This means that the variance of the residuals should be small.

3. A symmetric distribution: The residuals should be symmetrically distributed around zero. This is because a well-fitted model should underpredict and overpredict the observed values equally.

The residuals do not necessarily need to display a bimodal distribution or all positive values. In fact, if all residuals were positive, it would indicate that the model is consistently underpredicting the observed values, which is not a characteristic of a well-fitted model. Similarly, a bimodal distribution of residuals could indicate that the model is not fitting the data well in certain areas.

Knowee AI · Accepted Answer

The properties that the residuals should always display are:

1. Mean 0: The residuals are the differences between the observed and predicted values. If the model is well-fitted, the sum of these differences should be zero, which means the mean of the residuals should be zero.

2. Small variance: A well-fitted model should have residuals that are small in magnitude. This means that the variance of the residuals should be small.

3. A symmetric distribution: The residuals should be symmetrically distributed around zero. This is because a well-fitted model should underpredict and overpredict the observed values equally.

The residuals do not necessarily need to display a bimodal distribution or all positive values. In fact, if all residuals were positive, it would indicate that the model is consistently underpredicting the observed values, which is not a characteristic of a well-fitted model. Similarly, a bimodal distribution of residuals could indicate that the model is not fitting the data well in certain areas.

A model is fitted and a dotplot of the residuals constructed. Which of the following properties should the residuals always display? Tick all that applyGroup of answer choicesA symmetric distributionA bimodal distributionAll positiveMean 0Small variance

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