Question

d. State examples of the Linear Regression technique outcomes

e. State examples of the Linear Regression technique report results

Answer 1

Linear Regression establishes a relationship between dependent variable (Y) and one or more independent variables (X) using a best fit straight line (also known as regression line).

It can be used to quantify the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable).

• If the goal is prediction, forecasting, or error reduction, linear regression can be used to fit a predictive model to an observed data set of values of the response and explanatory variables. After developing such a model, if additional values of the explanatory variables are collected without an accompanying response value, the fitted model can be used to make a prediction of the response.

(e): The most common form of regression analysis is linear regression, in which a researcher finds the line (or a more complex linear combination) that most closely fits the data.

I suggest:

1) a graphical residual analysis scatterplot

2) cross-validation; minimally a few data saved (not used for model selection or estimation of regression coefficients) to check against predictions

3) estimate variance of the prediction error - which varies by prediction under heteroscedasticity, which is natural.