Suppose we use a person's dad's height to predict how short or tall the person will be by building a regression model to investigate if a relationship exists between the two variables. Suppose the regression results are as follows:
Least Squares Linear Regression of Height
What conclusion would you make at alpha = .05 when conducting a test to determine if there is a positive linear relationship between the variables?
a) There is sufficient evidence to indicate that dad's height is a positive linear predictor of height
b) There is insufficient evidence to indicate that dad's height is a positive linear predictor of height
c) There is sufficient evidence to indicate that dad's height is a negative linear predictor of height
d) There is sufficient evidence to indicate that dad's height is not a positive linear predictor of height

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)