Answer:
Hello some parts of your question is missing below is the missing part
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
Answer : We expect most of the sampled heights to fall within 9.8 inches of their least squares predicted values.
Step-by-step explanation:
standard deviation is the statistical measurement of the level at which a dataset disperses from its mean value
interpreting the standard deviation in this problem ,
given that the standard deviation is 4.9 inches, it simply means that the dataset heights will be either +4.9 inches or -4.9 inches away from the mean value. this means that most of the sampled Dad/'s height will fall within 9.8 inches of their least squares predicted values