Need assistance with the following questions below.

Suppose you have many observations of the scores students got on an exam, with other characteristics of the students including how many hours they studied for the exam that week and what is their current major GPA. Now, suppose you ran the standard linear regression of the exam grade on the number of hours studied: Grade; = Bo + By Hours Studied; + Uj and got an estimate of By, call it by. Now, suppose a colleague tells you that the students with above average GDAs were the students who studied more for the exam. How does your estimate of 32 in the following regression Grad s; = Bo + B1Hours Studied; + 32Major GPA; + Uj inform you of what your estimate of what (, will be for that same regression? O If 32 >0, then $1 > b1 O If 32 >0, then ] 0 O If 32 >0, then B1>0Suppose you're run a regression relating Iagtourpur) to hymn-kw Hours) in Excel. You are willing to make the necessary assumptions to deduce causality and run hypothesis tests. Your results are as follows: Coeicr'enb' Standard Error tSto't Pvalue Intercept 174583044 28117075534 {1613201297 {154124-0031 Log(Worker Hours) 169948323? {1711729096 1792852225 {1000264681 How should you interpret the coef cie nt on LogtWo rker Hours) of 2.69? O A 1% increase in Worker Hours leads to a 2.69% increase in Output. O A'l unit Increase In Worker Hours leads to a 2.69% increase in Output. O A 1% increase in Worker Hours leads to a .0259% increase in Output. O A 2.69% increase in Worker Hours leads to a 1% increase in Output