You are hired by a bank to help determine its lending standards. You decide to start by estimating a simple linear regression model to help predict whether a loan will become past-due (i.e. if any of the payments are tate). You ask the bank to give you data on the following two random variables: PD is an indicator variable equal to 1 if the loan is past-due, and zero if all payments have been made on time. credit_score is the client's credit score at the time the loan was originated. The table below shows sample descriptive statistics for these variables: Variable PD Sample Mean 0.1 Sample Standard Deviation 0.08 credit_score 550 25

The condition of an independent variable being correlated to one or more other independent variables is referred to as     nonlinearity.     statistical significance.     multicollinearity.     linearity.

A rural state wants to encourage high school graduates to continue their education and attend college.  The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed.  They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower.  To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…

Explain about the least square regression line?