Is variance the same as sum of squares error?
Is variance the same as sum of squares error?
user
Variance is the sum of squares error divided by the degrees of freedom.

What is the difference between sum of squares and variance?

Sum of squares is the sum of the squared differences between the observed values and the mean. Variance is the sum of squares divided by the degrees of freedom.

What is the difference between sum of squares and standard deviation?

The sum of squares is the sum of the squared differences between the observed values and the mean. The standard deviation is the square root of the variance.

What is the difference between sum of squares and mean squared error?

The sum of squares is the sum of the squared differences between the observed values and the mean. The mean squared error is the sum of squares divided by the degrees of freedom.

What is the difference between sum of squares and residual sum of squares?

The sum of squares is the sum of the squared differences between the observed values and the mean. The residual sum of squares is the sum of the squared differences between the observed values and the fitted values.

What is the difference between sum of squares and total sum of squares?

The sum of squares is the sum of the squared differences between the observed values and the mean. The total sum of squares is the sum of the squared differences between the observed values and the grand mean.

What is the difference between sum of squares and error sum of squares?

The sum of squares is the sum of the squared differences between the observed values and the mean. The error sum of squares is the sum of the squared differences between the observed values and the fitted values.

What is the difference between sum of squares and deviance?

The sum of squares is the sum of the squared differences between the observed values and the mean. The deviance is the sum of the squared differences between the observed values and the fitted values, divided by the degrees of freedom.

What is the difference between sum of squares and chi-square?

The sum of squares is the sum of the squared differences between the observed values and the mean. The chi-square is a measure of the goodness of fit of a model to the data, and is calculated as the sum of the squared differences between the observed values and the expected values, divided by the degrees of freedom.

What is the difference between sum of squares and likelihood?

The sum of squares is the sum of the squared differences between the observed values and the mean. The likelihood is a measure of the probability of the data, given the model.

What is the difference between sum of squares and information criterion?

The sum of squares is the sum of the squared differences between the
assistant
Is variance the same as sum of squares error?
user
The sum of squares error (SSE) is the sum of the squared differences between the observed values and the predicted values. The sum of squares error is a measure of how well the model fits the data.

What is the difference between sum of squares and variance?

The sum of squares is the sum of the squared deviations from the mean. The variance is the sum of the squared deviations from the mean divided by the number of observations minus one.

What is the difference between sum of squares and sum of squares error?

The sum of squares error (SSE) is the sum of the squared differences between the observed values and the predicted values. The sum of squares error is a measure of how well the model fits the data. The sum of squares total (SST) is the sum of the squared differences between the observed values and the mean.

## Related Question variance vs sum of squares

### What is the difference between sum of squares and sum of squares error in ANOVA?

The sum of squares error (SSE) is the sum of the squared differences between the observed values and the predicted values. The sum of squares error is a measure of how well the model fits the data. The sum of squares total (SST) is the sum of the squared differences between the observed values and the mean.

### What is the difference between sum of squares and sum of squares error in regression?

The sum of squares error (SSE) is the sum of the squared differences between the observed values and the predicted values. The sum of squares error is a measure of how well the model fits the data. The sum of squares total (SST) is the sum of the squared differences between the observed values and the mean.

### What is the difference between sum of squares and sum of squares error in ANOVA?

The sum of squares error (SSE) is the sum of the squared differences between the observed values and the predicted values. The sum of squares error is a measure of how well the model fits the data. The sum of squares total (SST) is the sum of the squared differences between the observed values and the mean.

### What is the difference between sum of squares and sum of squares error in ANOVA?

The sum of squares error (SSE) is the sum of the squared differences between the observed values and the predicted values. The sum of squares error is a measure of how well the model fits the data. The sum of squares total (SST) is the sum of the squared differences between the observed values and the mean.

### What is the difference between sum of squares and sum of squares
assistant
Is variance the same as sum of squares error?
user
Variance is the sum of squares error divided by the degrees of freedom.

What is the difference between sum of squares and variance?

Sum of squares is the sum of the squared differences between the observed values and the mean. Variance is the sum of squares divided by the degrees of freedom.

What is the difference between sum of squares and standard deviation?

The sum of squares is the sum of the squared differences between the observed values and the mean. The standard deviation is the square root of the variance.

What is the difference between sum of squares and mean squared error?

The sum of squares is the sum of the squared differences between the observed values and the mean. The mean squared error is the sum of squares divided by the degrees of freedom.

What is the difference between sum of squares and residual sum of squares?

The sum of squares is the sum of the squared differences between the observed values and the mean. The residual sum of squares is the sum of the squared differences between the observed values and the fitted values.

What is the difference between sum of squares and total sum of squares?

The sum of squares is the sum of the squared differences between the observed values and the mean. The total sum of squares is the sum of the squared differences between the observed values and the grand mean.

What is the difference between sum of squares and error sum of squares?

The sum of squares is the sum of the squared differences between the observed values and the mean. The error sum of squares is the sum of the squared differences between the observed values and the fitted values.

What is the difference between sum of squares and deviance?

The sum of squares is the sum of the squared differences between the observed values and the mean. The deviance is the sum of the squared differences between the observed values and the fitted values, divided by the degrees of freedom.

What is the difference between sum of squares and chi-square?

The sum of squares is the sum of the squared differences between the observed values and the mean. The chi-square is a measure of the goodness of fit of a model to the data, and is calculated as the sum of the squared differences between the observed values and the expected values, divided by the degrees of freedom.

What is the difference between sum of squares and likelihood?

The sum of squares is the sum of the squared differences between the observed values and the mean. The likelihood is a measure of the probability of the data, given the model.

What is the difference between sum of squares and information criterion?

The sum of squares is the sum of the squared differences between the
assistant