08. Predictions

Making Predictions

(Achieve)

Using your (linear) model to make predictions (In context with units & rounded appropriately)

Always predict the dependent variable form a chosen explanatory variable value (Predict a 'y' from a set 'x') DISCUSS in CONTEXT

Interpolation is the prediction of values WITHIN the data range using the model.

Extrapolation is the prediction of data OUTSIDE the data range. Extrapolation should be treated with caution depending on the context and variables, and especially for non-linear models. Extrapolation should also match the nature of the data.

(Merit)

Discuss the accuracy & appropriateness of the predictions

The accuracy of interpolation depends on the degree of accuracy of the models fit to the data, and what the scatter plot is like. Look at the graph and discuss in context.

Extrapolation

Interpolation

(Merit & Excellence)

Discussing the accuracy (precision) of the prediction (look at the level of scatter from the trend line)

Discuss relationship or causality with tespect to predictions.

Discuss appropriateness of the model.

Contextual interpretation of the gradient of the line of best fit.

(Excellence comment)

The difference in precision is probably due to the difference in gender. The shorter athletes are probably all be women so there is a strong association between height and weight. The athletes in the 185cm height range will include men and women, causing the greater range of weights (and a greater range of sports) resting in the lack of precision in the 80kg prediction. Further analysis involving the separation of the gender groups will test this hypothesis...)