**Sse can never be**

A. larger than SST

B. smaller than SST

C. equal to 1

D. equal to zero

Answer» A. larger than SST

**Answer: A. larger than SST**

In statistical analysis, SSE (Sum of Squared Errors) is a measure of the variation or dispersion of data points around a regression line or model.

On the other hand, SST (Total Sum of Squares) represents the total variation or dispersion of data points around the mean of the dependent variable.

SSE is calculated by summing up the squared differences between the actual observed values and the predicted values from the regression model.

SST, on the other hand, is calculated by summing up the squared differences between the actual observed values and the mean of the dependent variable.

When SSE is larger than SST, it implies that the sum of squared errors is greater than the total sum of squares, indicating that the regression model is not a good fit for the data.

In other words, the model is unable to explain a significant portion of the total variation in the dependent variable.

This situation can occur when the model is underfitting the data or when there is a large amount of unexplained random variation.

**On the other hand, if SSE is smaller than SST,**

it suggests that the sum of squared errors is less than the total sum of squares, indicating that the model is able to explain a significant portion of the total variation in the dependent variable.

This is generally desirable, as it indicates a better fit of the model to the data.

**Option C, which states that SSE is equal to 1, is incorrect.**

SSE is a measure of squared errors and represents a sum of positive values. It can take any non-negative value but is unlikely to be exactly equal to 1 unless the dataset is very specific and the model is specifically designed to yield such a result.

**Option D, which states that SSE is equal to zero, is also incorrect.**

If SSE were zero, it would mean that there are no errors or residuals in the model, indicating a perfect fit of the model to the data.

In practice, it is rare to achieve an SSE of exactly zero, as there is usually some degree of variation or noise in real-world data.

**In conclusion**, the correct option is A, which states that SSE can never be larger than SST. SSE represents the unexplained variation or errors in a regression model, while SST represents the total variation in the dependent variable.

If SSE is larger than SST, it indicates a poor fit of the model to the data, suggesting that there is a significant amount of unexplained variation.

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