Question

2.) 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

Predictor

Variables Coefficient Std Error    T P

Constant 20.2833 8.70520 2.33 0.0223

DadsHt 0.67499    0.12495 5.40 0.0002

R² 0.2673 Mean Square Error (MSE) 23.9235

Adjusted R² 0.2581    Standard Deviation    4.9000

Which interpretation of the y-intercept estimate would be appropriate?

Group of answer choices

For every additional inch in dad's height, we estimate height to increase by .675 inches.

No practical interpretation is possible since height=0 inches doesn't make sense.

No practical interpretation is possible since dad's height=0 inches doesn't make sense.

For every additional inch in dad's height, we estimate height to increase by 20.28 inches.

Answer 1

The y-intercept is simply equals to average of response variable (Y) when the independent variable (X) is 0. It is the point at Y-axis where the regression line cuts the vertical (Y) axis.

It is given that the researcher wants to predict how short or tall the person based on person's dad's height.

The y-intercept is 20.2833.

Since the dad height cannot be equal to 0, so it can be said that interpretation of y-intercept is meaningless.

Hence the correct answer is,

No practical interpretation is possible since dad's height=0 inches doesn't make sense.