Residuals & Correlation

Front

What are residuals in the context of regression analysis?

Back

Residuals are the differences between the observed values and the predicted values from a regression model.

Front

How do you calculate a residual?

Back

Residual = Observed value - Predicted value.

Front

What does a negative residual indicate?

Back

A negative residual indicates that the predicted value is too big.

Front

What does a positive residual indicate?

Back

A positive residual indicates that the predicted value is too small.

Front

What is the purpose of analyzing residuals in regression?

Back

Analyzing residuals helps to assess the fit of the model and identify any patterns that suggest the model is inappropriate.

Front

What does a residual plot show?

Back

A residual plot shows the residuals on the vertical axis and the independent variable on the horizontal axis, helping to visualize the fit of the model.

Front

What indicates a good linear fit in a residual plot?

Back

A good linear fit is indicated by residuals that are randomly scattered around zero without any discernible pattern.

Front

What is correlation?

Back

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

Front

What are the possible values of the correlation coefficient (r)?

Back

The correlation coefficient (r) can range from -1 to 1, where -1 indicates a perfect negative correlation, 1 indicates a perfect positive correlation, and 0 indicates no correlation.

Front

How can you rank scatter plots based on correlation strength?

Back

Scatter plots can be ranked based on how closely the points cluster around a line; the closer they are, the stronger the correlation.