Suppose we use a person's dad's height to predict how short or tall the person will be. Suppose we decided to build a regression model to investigate if there is a relationship between these two variables. What should we use as the variables in the analysis

The variables that can be used in the analysis are:

Dependent variable: person's height (Height)

Independent variable: person's dad's height (DadsHt)

Step-by-step explanation:

A linear regression model is used to predict the value of the dependent variable based upon the value of the independent variable.

The general form of a linear regression model is:

Here,

y = dependent variable

x = independent variable

a = intercept

b = slope

Dependent variables are those variables that are under study, i.e. they are being observed for any changes when the other variables in the model are changed.

The dependent variables are also known as response variables.

Independent variables are the variables that are being altered to see a proportionate change in the dependent variable. In a regression model there can be one or more than one independent variables.

The independent variables are also known as the predictor variables.

In this vase we need to form a regression model such that, a person's dad's height can be used to predict how short or tall the person will be.

That is, the dependent variable is the person's height and the independent variable is the person's dad's height.

The variables that can be used in the analysis are:

Dependent variable: person's height (Height)

Independent variable: person's dad's height (DadsHt)