We assume the house prices can be modeled with a simple linear regression with

the intercept = +20 000 and

the coefficient beta_m2 = +1000.

What is the unexplained errors' absolute average for the two houses given below with the given predictions ?

house1_m2 = 100 , y_house1 = 100 000 dollars;

house2_m2 = 160 , y_house2 = 190 000 dollars 

Which of the below can be a solution whenever a prediction model cannot be created for any reason? 

i. Using the mean value as the prediction result is the most basic approach. 

ii. If the change in a time series data is slow, the previous value (t-1) can be used. 

iii. If a time series data shows seasonality, the value from the previous season can be used. 

In a multiple linear regression model, the coefficients represent

A linear regression analysis with an independent variable is best described by

Info: The p-value of an already included variable may change when a new variable is added to the model.

In a case, the p-value of an already included variable changes, which of the below is always correct?