Residuals & Correlation

Front

What is a residual in statistics?

Back

A residual is the difference between the observed value and the predicted value of a data point in a regression analysis.

Front

How do you interpret a positive residual?

Back

A positive residual indicates that the observed value is greater than the predicted value, suggesting that the model underestimates the actual data point.

Front

How do you interpret a negative residual?

Back

A negative residual indicates that the observed value is less than the predicted value, suggesting that the model overestimates the actual data point.

Front

What does a residual plot show?

Back

A residual plot displays the residuals on the vertical axis and the independent variable on the horizontal axis, helping to assess the fit of a regression model.

Front

What does it mean if a residual plot shows no pattern?

Back

If a residual plot shows no pattern, it suggests that the regression model is appropriate for the data.

Front

What does a positive correlation indicate?

Back

A positive correlation indicates that as one variable increases, the other variable also tends to increase.

Front

What does a negative correlation indicate?

Back

A negative correlation indicates that as one variable increases, the other variable tends to decrease.

Front

What does it mean if there is no correlation between two variables?

Back

No correlation means that there is no predictable relationship between the two variables; changes in one do not affect the other.

Front

How can you determine the strength of a correlation?

Back

The strength of a correlation can be determined by the correlation coefficient, which ranges from -1 to 1. Values closer to -1 or 1 indicate stronger correlations.

Front

What is the correlation coefficient?

Back

The correlation coefficient is a statistical measure that describes the direction and strength of a relationship between two variables.